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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

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Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Laboratory Experiments

Last updated 22 Mar 2021

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Experiments look for the effect that manipulated variables (independent variables, or IVs) have on measured variables (dependent variables, or DVs), i.e. causal effects.

Laboratory experiments pay particular attention to eliminating the effects of other, extraneous variables, by controlling them (i.e. removing or keeping them constant) in an artificial environment. This makes it more likely for researchers to find a causal effect, having confidence that no variables other than changes in an IV can affect a resulting DV. Laboratory experiments are the most heavily controlled form of experimental research.

Participants can also be randomly allocated to experimental conditions, to avoid experimenter bias (i.e. the experimenter cannot be accused of choosing who will be in each experimental condition, which could affect the results).

Evaluation of laboratory experiments:

- High control over extraneous variables means that they cannot confound the results, so a ‘cause and effect’ relationship between the IV and DV is often assumed.

- Results of laboratory experiments tend to be reliable, as the conditions created (and thus results produced) can be replicated.

- Variables can be measured accurately with the tools made available in a laboratory setting, which may otherwise be impossible for experiments conducted ‘in the field’ (field experiments).

- Data collected may lack ecological validity, as the artificial nature of laboratory experiments can cast doubt over whether the results reflect the nature of real life scenarios.

- There is a high risk of demand characteristics, i.e. participants may alter their behaviour based on their interpretation of the purpose of the experiment.

- There is also a risk of experimenter bias, e.g. researchers’ expectations may affect how they interact with participants (affecting participants’ behaviour), or alter their interpretation of the results.

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• Lab Experiment

What do you think of when you hear the word "laboratory"? Do you picture people in white coats and goggles and gloves standing over a table with beakers and tubes? Well, that picture is pretty close to reality in some cases. In others, laboratory experiments, especially in psychology, focus more on observing behaviours in highly controlled settings to establish causal conclusions. Let's explore lab experiments further. 

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The aim of lab experiments is to identify if observed changes in the      are caused by the        .

Is it difficult to generalise results from lab experiments to real-life settings? 

Demand characteristics lower the           of the research.

True or false: there is more likelihood of demand characteristics influencing lab experiments than field experiments.

A researcher wanted to explore how driving conditions affected speeding. Which type of experimental method is the researcher more likely to use? 

A researcher wanted to explore if sleep deprivation affected cognitive abilities. Which type of experimental method is the researcher more likely to use? 

Are lab experiments easy to replicate? 

True or false: Participants are aware that they are taking part in the lab experiment and sometimes may not know the aim of the investigation.

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Jump to a key chapter

• We are going to delve into the topic of lab experiments in the context of psychology.
• We will start by looking at the lab experiment definition and how lab experiments are used in psychology.
• Moving on from this, we will look at how lab experiment examples in psychology and cognitive lab experiments may be conducted.
• And to finish off, we will also explore the strengths and weaknesses of lab experiments.

Lab Experiment Psychology Definition

You can probably guess from the name that lab experiments occur in lab settings. Although this is not always the case, they can sometimes occur in other controlled environments. The purpose of lab experiments is to identify the cause and effect of a phenomenon through experimentation.

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to accurately measure how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

In lab experiments, the IV is what the researcher predicts as the cause of a phenomenon, and the dependent variable is what the researcher predicts as the effect of a phenomenon.

Lab Experiment: P sychology

Lab experiments in psychology are used when trying to establish causal relationships between variables . For example, a researcher would use a lab experiment if they were investigating how sleep affects memory recall.

The majority of psychologists think of psychology as a form of science. Therefore, they argue that the protocol used in psychological research should resemble those used in the natural sciences. For research to be established as scientific , three essential features should be considered:

• Empiricism - the findings should be observable via the five senses.
• Reliability - if the study was replicated, similar results should be found.
• Validity - the investigation should accurately measure what it intends to.

But do lab experiments fulfil these requirements of natural sciences research? If done correctly, then yes. Lab experiments are empirical as they involve the researcher observing changes occurring in the DV. Reliability is established by using a standardised procedure in lab experiments .

A standardised procedure is a protocol that states how the experiment will be carried out. This allows the researcher to ensure the same protocol is used for each participant, increasing the study's internal reliability.

Standardised procedures are also used to help other researchers replicate the study to identify if they measure similar results.

Dissimilar results reflect low reliability.

Validity is another feature of a lab experiment considered. Lab experiments are conducted in a carefully controlled setting where the researcher has the most control compared to other experiments to prevent extraneous variables from affecting the DV .

Extraneous variables are factors other than the IV that affect the DV; as these are variables that the researcher is not interested in investigating, these reduce the validity of the research.

There are issues of validity in lab experiments, which we'll get into a bit later!

Lab Experiment Examples: Asch's Conformity Study

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question. Participants were given two pieces of paper, one depicting a 'target line' and another three, one of which resembled the 'target line' and the others of different lengths.

The participants were put in groups of eight. Unknown to the participants, the other seven were confederates (participants who were secretly part of the research team) who were instructed to give the wrong answer. If the actual participant changed their answer in response, this would be an example of conformity .

Asch controlled the location where the investigation took place, constructed a contrived scenario and even controlled the confederates who would affect the behaviour of the actual participants to measure the DV.

Some other famous examples of research that are lab experiment examples include research conducted by Milgram (the obedience study) and Loftus and Palmer's eyewitness testimony accuracy study . These researchers likely used this method because of some of their strengths , e.g., their high level of control .

Lab Experiment Examples: Cognitive Lab Experiments

Let's look at what a cognitive lab experiment may entail. Suppose a researcher is interested in investigating how sleep affects memory scores using the MMSE test. In the theoretical study , an equal number of participants were randomly allocated into two groups; sleep-deprived versus well-rested. Both groups completed the memory test after a whole night of sleep or staying awake all night.

In this research scenario , the DV can be identified as memory test scores and the IV as whether participants were sleep-deprived or well-rested.

Some examples of extraneous variables the study controlled include researchers ensuring participants did not fall asleep, the participants took the test at the same time, and participants in the well-rested group slept for the same time.

Lab Experiment Advantages and Disadvantages

It's important to consider the advantages and disadvantages of laboratory experiments . Advantages include the highly controlled setting of lab experiments, the standardised procedures and causal conclusions that can be drawn. Disadvantages include the low ecological validity of lab experiments and demand characteristics participants may present.

Strengths of Lab Experiments: Highly Controlled

Laboratory experiments are conducted in a well-controlled setting. All the variables, including extraneous and confounding variables , are rigidly controlled in the investigation. Therefore, the risk of experimental findings being affected by extraneous or confounding variables is reduced . As a result, the well-controlled design of laboratory experiments implies the research has high internal validity .

Internal validity means the study uses measures and protocols that measure exactly what it intends to, i.e. how only the changes in the IV affect the DV.

Strengths of Lab Experiments: Standardised Procedures

Laboratory experiments have standardised procedures, which means the experiments are replicable , and all participants are tested under the same conditions. T herefore, standardised procedures allow others to replicate the study to identify whether the research is reliable and that the findings are not a one-off result. As a result, the replicability of laboratory experiments allows researchers to verify the study's reliability .

Strengths of Lab Experiments: Causal Conclusions

A well-designed laboratory experiment can draw causal conclusions. Ideally, a laboratory experiment can rigidly control all the variables , including extraneous and confounding variables. Therefore, laboratory experiments provide great confidence to researchers that the IV causes any observed changes in DV.

Weaknesses of Lab Experiments

In the following, we will present the disadvantages of laboratory experiments. This discusses ecological validity and demand characteristics.

Weaknesses of Lab Experiments: Low Ecological Validity

Laboratory experiments have low ecological validity because they are conducted in an artificial study that does not reflect a real-life setting . As a result, findings generated in laboratory experiments can be difficult to generalise to real life due to the low mundane realism. Mundane realism reflects the extent to which lab experiment materials are similar to real-life events.

Weaknesses of Lab Experiments: Demand Characteristics

A disadvantage of laboratory experiments is that the research setting may lead to demand characteristics .

Demand characteristics are the cues that make participants aware of what the experimenter expects to find or how participants are expected to behave.

The participants are aware they are involved in an experiment. So, participants may have some ideas of what is expected of them in the investigation, which may influence their behaviours. As a result, the demand characteristics presented in laboratory experiments can arguably change the research outcome , reducing the findings' validity .

Lab Experiment - Key takeaways

The lab experiment definition is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affect the dependent variable (DV; variable measured).

Psychologists aim to ensure that lab experiments are scientific and must be empirical, reliable and valid.

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question.

The advantages of lab experiments are high internal validity, standardised procedures and the ability to draw causal conclusions.

The disadvantages of lab experiments are low ecological validity and demand characteristics.

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Frequently Asked Questions about Lab Experiment

What is a lab experiment?

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

What is the purpose of lab experiments?

Lab experiments investigate cause-and-effect. They aim to determine the effect of changes in the independent variable on the dependent variable. 

What is a lab experiment and field experiment?

A field experiment is an experiment conducted in a natural, everyday setting. The experimenter still controls the IV; however, extraneous and confounding variables may be difficult to control due to the natural setting.

Similar, to filed experiments researchers, can control the IV and extraneous variables. However, this takes place in an artificial setting such as a lab. 

Why would a psychologist use a laboratory experiment? 

A psychologist may use a lab experiment when trying to establish the causal relationships between variables to explain a phenomenon. 

Why is lab experience important?

Lab experience allows researchers to scientifically determine whether a hypothesis/ theory should be accepted or rejected. 

What is a lab experiment example? 

The research conducted by Loftus and Palmer (accuracy of eyewitness testimony) and Milgram (obedience) used a lab experiment design. These experimental designs give the researcher high control, allowing them to control extraneous and independent variables.

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The aim of lab experiments is to identify if observed changes in the      are caused by the      .

Demand characteristics lower the         of the research.

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How to Conduct a Psychology Experiment

Conducting your first psychology experiment can be a long, complicated, and sometimes intimidating process. It can be especially confusing if you are not quite sure where to begin or which steps to take.

Like other sciences, psychology utilizes the  scientific method  and bases conclusions upon empirical evidence. When conducting an experiment, it is important to follow the seven basic steps of the scientific method:

• Ask a testable question
• Define your variables
• Conduct background research
• Design your experiment
• Perform the experiment
• Collect and analyze the data
• Draw conclusions
• Share the results with the scientific community

At a Glance

It's important to know the steps of the scientific method if you are conducting an experiment in psychology or other fields. The processes encompasses finding a problem you want to explore, learning what has already been discovered about the topic, determining your variables, and finally designing and performing your experiment. But the process doesn't end there! Once you've collected your data, it's time to analyze the numbers, determine what they mean, and share what you've found.

Find a Research Problem or Question

Picking a research problem can be one of the most challenging steps when you are conducting an experiment. After all, there are so many different topics you might choose to investigate.

Are you stuck for an idea? Consider some of the following:

Investigate a Commonly Held Belief

Folk knowledge is a good source of questions that can serve as the basis for psychological research. For example, many people believe that staying up all night to cram for a big exam can actually hurt test performance.

You could conduct a study to compare the test scores of students who stayed up all night with the scores of students who got a full night's sleep before the exam.

Review Psychology Literature

Published studies are a great source of unanswered research questions. In many cases, the authors will even note the need for further research. Find a published study that you find intriguing, and then come up with some questions that require further exploration.

Think About Everyday Problems

There are many practical applications for psychology research. Explore various problems that you or others face each day, and then consider how you could research potential solutions. For example, you might investigate different memorization strategies to determine which methods are most effective.

Define Your Variables

Variables are anything that might impact the outcome of your study. An operational definition describes exactly what the variables are and how they are measured within the context of your study.

For example, if you were doing a study on the impact of sleep deprivation on driving performance, you would need to operationally define sleep deprivation and driving performance .

An operational definition refers to a precise way that an abstract concept will be measured. For example, you cannot directly observe and measure something like test anxiety . You can, however, use an anxiety scale and assign values based on how many anxiety symptoms a person is experiencing. 

In this example, you might define sleep deprivation as getting less than seven hours of sleep at night. You might define driving performance as how well a participant does on a driving test.

What is the purpose of operationally defining variables? The main purpose is control. By understanding what you are measuring, you can control for it by holding the variable constant between all groups or manipulating it as an independent variable .

Develop a Hypothesis

The next step is to develop a testable hypothesis that predicts how the operationally defined variables are related. In the recent example, the hypothesis might be: "Students who are sleep-deprived will perform worse than students who are not sleep-deprived on a test of driving performance."

Null Hypothesis

In order to determine if the results of the study are significant, it is essential to also have a null hypothesis. The null hypothesis is the prediction that one variable will have no association to the other variable.

In other words, the null hypothesis assumes that there will be no difference in the effects of the two treatments in our experimental and control groups .

The null hypothesis is assumed to be valid unless contradicted by the results. The experimenters can either reject the null hypothesis in favor of the alternative hypothesis or not reject the null hypothesis.

It is important to remember that not rejecting the null hypothesis does not mean that you are accepting the null hypothesis. To say that you are accepting the null hypothesis is to suggest that something is true simply because you did not find any evidence against it. This represents a logical fallacy that should be avoided in scientific research.  

Conduct Background Research

Once you have developed a testable hypothesis, it is important to spend some time doing some background research. What do researchers already know about your topic? What questions remain unanswered?

You can learn about previous research on your topic by exploring books, journal articles, online databases, newspapers, and websites devoted to your subject.

Reading previous research helps you gain a better understanding of what you will encounter when conducting an experiment. Understanding the background of your topic provides a better basis for your own hypothesis.

After conducting a thorough review of the literature, you might choose to alter your own hypothesis. Background research also allows you to explain why you chose to investigate your particular hypothesis and articulate why the topic merits further exploration.

As you research the history of your topic, take careful notes and create a working bibliography of your sources. This information will be valuable when you begin to write up your experiment results.

Select an Experimental Design

After conducting background research and finalizing your hypothesis, your next step is to develop an experimental design. There are three basic types of designs that you might utilize. Each has its own strengths and weaknesses:

Pre-Experimental Design

A single group of participants is studied, and there is no comparison between a treatment group and a control group. Examples of pre-experimental designs include case studies (one group is given a treatment and the results are measured) and pre-test/post-test studies (one group is tested, given a treatment, and then retested).

Quasi-Experimental Design

This type of experimental design does include a control group but does not include randomization. This type of design is often used if it is not feasible or ethical to perform a randomized controlled trial.

True Experimental Design

A true experimental design, also known as a randomized controlled trial, includes both of the elements that pre-experimental designs and quasi-experimental designs lack—control groups and random assignment to groups.

Standardize Your Procedures

In order to arrive at legitimate conclusions, it is essential to compare apples to apples.

Each participant in each group must receive the same treatment under the same conditions.

For example, in our hypothetical study on the effects of sleep deprivation on driving performance, the driving test must be administered to each participant in the same way. The driving course must be the same, the obstacles faced must be the same, and the time given must be the same.

Choose Your Participants

In addition to making sure that the testing conditions are standardized, it is also essential to ensure that your pool of participants is the same.

If the individuals in your control group (those who are not sleep deprived) all happen to be amateur race car drivers while your experimental group (those that are sleep deprived) are all people who just recently earned their driver's licenses, your experiment will lack standardization.

When choosing subjects, there are some different techniques you can use.

Simple Random Sample

In a simple random sample, the participants are randomly selected from a group. A simple random sample can be used to represent the entire population from which the representative sample is drawn.

Drawing a simple random sample can be helpful when you don't know a lot about the characteristics of the population.

Stratified Random Sample

Participants must be randomly selected from different subsets of the population. These subsets might include characteristics such as geographic location, age, sex, race, or socioeconomic status.

Stratified random samples are more complex to carry out. However, you might opt for this method if there are key characteristics about the population that you want to explore in your research.

Conduct Tests and Collect Data

After you have selected participants, the next steps are to conduct your tests and collect the data. Before doing any testing, however, there are a few important concerns that need to be addressed.

Address Ethical Concerns

First, you need to be sure that your testing procedures are ethical . Generally, you will need to gain permission to conduct any type of testing with human participants by submitting the details of your experiment to your school's Institutional Review Board (IRB), sometimes referred to as the Human Subjects Committee.

Obtain Informed Consent

After you have gained approval from your institution's IRB, you will need to present informed consent forms to each participant. This form offers information on the study, the data that will be gathered, and how the results will be used. The form also gives participants the option to withdraw from the study at any point in time.

Once this step has been completed, you can begin administering your testing procedures and collecting the data.

Analyze the Results

After collecting your data, it is time to analyze the results of your experiment. Researchers use statistics to determine if the results of the study support the original hypothesis and if the results are statistically significant.

Statistical significance means that the study's results are unlikely to have occurred simply by chance.

The types of statistical methods you use to analyze your data depend largely on the type of data that you collected. If you are using a random sample of a larger population, you will need to utilize inferential statistics.

These statistical methods make inferences about how the results relate to the population at large.

Because you are making inferences based on a sample, it has to be assumed that there will be a certain margin of error. This refers to the amount of error in your results. A large margin of error means that there will be less confidence in your results, while a small margin of error means that you are more confident that your results are an accurate reflection of what exists in that population.

Share Your Results After Conducting an Experiment

Your final task in conducting an experiment is to communicate your results. By sharing your experiment with the scientific community, you are contributing to the knowledge base on that particular topic.

One of the most common ways to share research results is to publish the study in a peer-reviewed professional journal. Other methods include sharing results at conferences, in book chapters, or academic presentations.

In your case, it is likely that your class instructor will expect a formal write-up of your experiment in the same format required in a professional journal article or lab report :

• Introduction
• Tables and figures

What This Means For You

Designing and conducting a psychology experiment can be quite intimidating, but breaking the process down step-by-step can help. No matter what type of experiment you decide to perform, always check with your instructor and your school's institutional review board for permission before you begin.

NOAA SciJinks. What is the scientific method? .

Nestor, PG, Schutt, RK. Research Methods in Psychology . SAGE; 2015.

Andrade C. A student's guide to the classification and operationalization of variables in the conceptualization and eesign of a clinical study: Part 2 .  Indian J Psychol Med . 2021;43(3):265-268. doi:10.1177/0253717621996151

Purna Singh A, Vadakedath S, Kandi V. Clinical research: A review of study designs, hypotheses, errors, sampling types, ethics, and informed consent .  Cureus . 2023;15(1):e33374. doi:10.7759/cureus.33374

Colby College. The Experimental Method .

Leite DFB, Padilha MAS, Cecatti JG. Approaching literature review for academic purposes: The Literature Review Checklist .  Clinics (Sao Paulo) . 2019;74:e1403. doi:10.6061/clinics/2019/e1403

Salkind NJ. Encyclopedia of Research Design . SAGE Publications, Inc.; 2010. doi:10.4135/9781412961288

Miller CJ, Smith SN, Pugatch M. Experimental and quasi-experimental designs in implementation research .  Psychiatry Res . 2020;283:112452. doi:10.1016/j.psychres.2019.06.027

Nijhawan LP, Manthan D, Muddukrishna BS, et. al. Informed consent: Issues and challenges . J Adv Pharm Technol Rese . 2013;4(3):134-140. doi:10.4103/2231-4040.116779

Serdar CC, Cihan M, Yücel D, Serdar MA. Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies .  Biochem Med (Zagreb) . 2021;31(1):010502. doi:10.11613/BM.2021.010502

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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laboratory , Place where scientific research and development is conducted and analyses performed, in contrast with the field or factory. Most laboratories are characterized by controlled uniformity of conditions (constant temperature , humidity, cleanliness). Modern laboratories use a vast number of instruments and procedures to study, systematize, or quantify the objects of their attention. Procedures often include sampling, pretreatment and treatment, measurement , calculation, and presentation of results; each may be carried out by techniques ranging from having an unaided person use crude tools to running an automated analysis system with computer controls, data storage, and elaborate readouts.

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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America's Lab Report: Investigations in High School Science (2006)

Chapter: 3 laboratory experiences and student learning, 3 laboratory experiences and student learning.

In this chapter, the committee first identifies and clarifies the learning goals of laboratory experiences and then discusses research evidence on attainment of those goals. The review of research evidence draws on three major strands of research: (1) cognitive research illuminating how students learn; (2) studies that examine laboratory experiences that stand alone, separate from the flow of classroom science instruction; and (3) research projects that sequence laboratory experiences with other forms of science instruction. 1 We propose the phrase “integrated instructional units” to describe these research and design projects that integrate laboratory experiences within a sequence of science instruction. In the following section of this chapter, we present design principles for laboratory experiences derived from our analysis of these multiple strands of research and suggest that laboratory experiences designed according to these principles are most likely to accomplish their learning goals. Next we consider the role of technology in supporting student learning from laboratory experiences. The chapter concludes with a summary.

GOALS FOR LABORATORY EXPERIENCES

Laboratories have been purported to promote a number of goals for students, most of which are also the goals of science education in general (Lunetta, 1998; Hofstein and Lunetta, 1982). The committee commissioned a paper to examine the definition and goals of laboratory experiences (Millar, 2004) and also considered research reviews on laboratory education that have identified and discussed learning goals (Anderson, 1976; Hofstein and Lunetta, 1982; Lazarowitz and Tamir, 1994; Shulman and Tamir, 1973). While these inventories of goals vary somewhat, a core set remains fairly consistent. Building on these commonly stated goals, the committee developed a comprehensive list of goals for or desired outcomes of laboratory experiences:

Enhancing mastery of subject matter . Laboratory experiences may enhance student understanding of specific scientific facts and concepts and of the way in which these facts and concepts are organized in the scientific disciplines.

Developing scientific reasoning . Laboratory experiences may promote a student’s ability to identify questions and concepts that guide scientific

investigations; to design and conduct scientific investigations; to develop and revise scientific explanations and models; to recognize and analyze alternative explanations and models; and to make and defend a scientific argument. Making a scientific argument includes such abilities as writing, reviewing information, using scientific language appropriately, constructing a reasoned argument, and responding to critical comments.

Understanding the complexity and ambiguity of empirical work . Interacting with the unconstrained environment of the material world in laboratory experiences may help students concretely understand the inherent complexity and ambiguity of natural phenomena. Laboratory experiences may help students learn to address the challenges inherent in directly observing and manipulating the material world, including troubleshooting equipment used to make observations, understanding measurement error, and interpreting and aggregating the resulting data.

Developing practical skills . In laboratory experiences, students may learn to use the tools and conventions of science. For example, they may develop skills in using scientific equipment correctly and safely, making observations, taking measurements, and carrying out well-defined scientific procedures.

Understanding of the nature of science . Laboratory experiences may help students to understand the values and assumptions inherent in the development and interpretation of scientific knowledge, such as the idea that science is a human endeavor that seeks to understand the material world and that scientific theories, models, and explanations change over time on the basis of new evidence.

Cultivating interest in science and interest in learning science . As a result of laboratory experiences that make science “come alive,” students may become interested in learning more about science and see it as relevant to everyday life.

Developing teamwork abilities . Laboratory experiences may also promote a student’s ability to collaborate effectively with others in carrying out complex tasks, to share the work of the task, to assume different roles at different times, and to contribute and respond to ideas.

Although most of these goals were derived from previous research on laboratory experiences and student learning, the committee identified the new goal of “understanding the complexity and ambiguity of empirical work” to reflect the unique nature of laboratory experiences. Students’ direct encounters with natural phenomena in laboratory science courses are inherently more ambiguous and messy than the representations of these phenomena in science lectures, textbooks, and mathematical formulas (Millar, 2004). The committee thinks that developing students’ ability to recognize this complexity and develop strategies for sorting through it is an essential

goal of laboratory experiences. Unlike the other goals, which coincide with the goals of science education more broadly and may be advanced through lectures, reading, or other forms of science instruction, laboratory experiences may be the only way to advance the goal of helping students understand the complexity and ambiguity of empirical work.

RECENT DEVELOPMENTS IN RESEARCH AND DESIGN OF LABORATORY EXPERIENCES

In reviewing evidence on the extent to which students may attain the goals of laboratory experiences listed above, the committee identified a recent shift in the research. Historically, laboratory experiences have been separate from the flow of classroom science instruction and often lacked clear learning goals. Because this approach remains common today, we refer to these isolated interactions with natural phenomena as “typical” laboratory experiences. 2 Reflecting this separation, researchers often engaged students in one or two experiments or other science activities and then conducted assessments to determine whether their understanding of the science concept underlying the activity had increased. Some studies directly compared measures of student learning following laboratory experiences with measures of student learning following lectures, discussions, videotapes, or other methods of science instruction in an effort to determine which modes of instruction were most effective.

Over the past 10 years, some researchers have shifted their focus. Assuming that the study of the natural world requires opportunities to directly encounter that world, investigators are integrating laboratory experiences and other forms of instruction into instructional sequences in order to help students progress toward science learning goals. These studies draw on principles of learning derived from the rapid growth in knowledge from cognitive research to address the question of how to design science instruction, including laboratory experiences, in order to support student learning.

Given the complexity of these teaching and learning sequences, the committee struggled with how best to describe them. Initially, the committee used the term “science curriculum units.” However, that term failed to convey the importance of integration in this approach to sequencing laboratory experiences with other forms of teaching and learning. The research reviewed by the committee indicated that these curricula not only integrate laboratory experiences in the flow of science instruction, but also integrate

student learning about both the concepts and processes of science. To reflect these aspects of the new approach, the committee settled on the term “integrated instructional units” in this report.

The following sections briefly describe principles of learning derived from recent research in the cognitive sciences and their application in design of integrated instructional units.

Principles of Learning Informing Integrated Instructional Units

Recent research and development of integrated instructional units that incorporate laboratory experiences are based on a large and growing body of cognitive research. This research has led to development of a coherent and multifaceted theory of learning that recognizes that prior knowledge, context, language, and social processes play critical roles in cognitive development and learning (National Research Council, 1999). Taking each of these factors into account, the National Research Council (NRC) report How People Learn identifies four critical principles that support effective learning environments (Glaser, 1994; National Research Council, 1999), and a more recent NRC report, How Students Learn , considers these principles as they relate specifically to science (National Research Council, 2005). These four principles are summarized below.

Learner-Centered Environments

The emerging integrated instructional units are designed to be learner-centered. This principle is based on research showing that effective instruction begins with what learners bring to the setting, including cultural practices and beliefs, as well as knowledge of academic content. Taking students’ preconceptions into account is particularly critical in science instruction. Students come to the classroom with conceptions of natural phenomena that are based on their everyday experiences in the world. Although these conceptions are often reasonable and can provide satisfactory everyday explanations to students, they do not always match scientific explanations and break down in ways that students often fail to notice. Teachers face the challenge of engaging with these intuitive ideas, some of which are more firmly rooted than others, in order to help students move toward a more scientific understanding. In this way, understanding scientific knowledge often requires a change in—not just an addition to—what students notice and understand about the world (National Research Council, 2005).

Knowledge-Centered Environments

The developing integrated instructional units are based on the principle that learning is enhanced when the environment is knowledge-centered. That is, the laboratory experiences and other instruction included in integrated instructional units are designed to help students learn with understanding, rather than simply acquiring sets of disconnected facts and skills (National Research Council, 1999).

In science, the body of knowledge with which students must engage includes accepted scientific ideas about natural phenomena as well as an understanding of what it means to “do science.” These two aspects of science are reflected in the goals of laboratory experiences, which include mastery of subject matter (accepted scientific ideas about phenomena) and several goals related to the processes of science (understanding the complexity of empirical work, development of scientific reasoning). Research on student thinking about science shows a progression of ideas about scientific knowledge and how it is justified. At the first stage, students perceive scientific knowledge as right or wrong. Later, students characterize discrepant ideas and evidence as “mere opinion.” Eventually, students recognize scientific knowledge as being justified by evidence derived through rigorous research. Several studies have shown that a large proportion of high school students are at the first stage in their views of scientific knowledge (National Research Council, 2005).

Knowledge-centered environments encourage students to reflect on their own learning progress (metacognition). Learning is facilitated when individuals identify, monitor, and regulate their own thinking and learning. To be effective problem solvers and learners, students need to determine what they already know and what else they need to know in any given situation, including when things are not going as expected. For example, students with better developed metacognitive strategies will abandon an unproductive problem-solving strategy very quickly and substitute a more productive one, whereas students with less effective metacognitive skills will continue to use the same strategy long after it has failed to produce results (Gobert and Clement, 1999). The basic metacognitive strategies include: (1) connecting new information to former knowledge, (2) selecting thinking strategies deliberately, and (3) monitoring one’s progress during problem solving.

A final aspect of knowledge-centered learning, which may be particularly relevant to integrated instructional units, is that the practices and activities in which people engage while learning shape what they learn. Transfer (the ability to apply learning in varying situations) is made possible to the extent that knowledge and learning are grounded in multiple contexts. Transfer is more difficult when a concept is taught in a limited set of contexts or through a limited set of activities. By encountering the same concept at work in multiple contexts (such as in laboratory experiences and in discussion),

students can develop a deeper understanding of the concept and how it can be used as well as the ability to transfer what has been learned in one context to others (Bransford and Schwartz, 2001).

Assessment to Support Learning

Another important principle of learning that has informed development of integrated instructional units is that assessment can be used to support learning. Cognitive research has shown that feedback is fundamental to learning, but feedback opportunities are scarce in most classrooms. This research indicates that formative assessments provide students with opportunities to revise and improve the quality of their thinking while also making their thinking apparent to teachers, who can then plan instruction accordingly. Assessments must reflect the learning goals of the learning environment. If the goal is to enhance understanding and the applicability of knowledge, it is not sufficient to provide assessments that focus primarily on memory for facts and formulas. The Thinkertools science instructional unit discussed in the following section incorporates this principle, including formative self-assessment tools that help students advance toward several of the goals of laboratory experiences.

Community-Centered Environments

Research has shown that learning is enhanced in a community setting, when students and teachers share norms that value knowledge and participation (see Cobb et al., 2001). Such norms increase people’s opportunities and motivation to interact, receive feedback, and learn. Learning is enhanced when students have multiple opportunities to articulate their ideas to peers and to hear and discuss others’ ideas. A community-centered classroom environment may not be organized in traditional ways. For example, in science classrooms, the teacher is often the sole authority and arbiter of scientific knowledge, placing students in a relatively passive role (Lemke, 1990). Such an organization may promote students’ view that scientific knowledge is a collection of facts about the world, authorized by expert scientists and irrelevant to students’ own experience. The instructional units discussed below have attempted to restructure the social organization of the classroom and encourage students and the teacher to interact and learn from each other.

Design of Integrated Instructional Units

The learning principles outlined above have begun to inform design of integrated instructional units that include laboratory experiences with other types of science learning activities. These integrated instructional units were

developed through research programs that tightly couple research, design, and implementation in an iterative process. The research programs are beginning to document the details of student learning, development, and interaction when students are given systematic support—or scaffolding—in carefully structured social and cognitive activities. Scaffolding helps to guide students’ thinking, so that they can gradually take on more autonomy in carrying out various parts of the activities. Emerging research on these integrated instructional units provides guidance about how to design effective learning environments for real-world educational settings (see Linn, Davis, and Bell, 2004a; Cobb et al., 2003; Design-Based Research Collective, 2003).

Integrated instructional units interweave laboratory experiences with other types of science learning activities, including lectures, reading, and discussion. Students are engaged in framing research questions, designing and executing experiments, gathering and analyzing data, and constructing arguments and conclusions as they carry out investigations. Diagnostic, formative assessments are embedded into the instructional sequences and can be used to gauge student’s developing understanding and to promote their self-reflection on their thinking.

With respect to laboratory experiences, these instructional units share two key features. The first is that specific laboratory experiences are carefully selected on the basis of research-based ideas of what students are likely to learn from them. For example, any particular laboratory activity is likely to contribute to learning only if it engages students’ current thinking about the target phenomena and is likely to make them critically evaluate their ideas in relation to what they see during the activity. The second is that laboratory experiences are explicitly linked to and integrated with other learning activities in the unit. The assumption behind this second feature is that just because students do a laboratory activity, they may not necessarily understand what they have done. Nascent research on integrated instructional units suggests that both framing a particular laboratory experience ahead of time and following it with activities that help students make sense of the experience are crucial in using a laboratory experience to support science learning. This “integration” approach draws on earlier research showing that intervention and negotiation with an authority, usually a teacher, was essential to help students make meaning out of their laboratory activities (Driver, 1995).

Examples of Integrated Instructional Units

Scaling up chemistry that applies.

Chemistry That Applies (CTA) is a 6-8 week integrated instructional unit designed to help students in grades 8-10 understand the law of conservation

of matter. Created by researchers at the Michigan Department of Education (Blakeslee et al., 1993), this instructional unit was one of only a few curricula that were highly rated by American Assocation for the Advancement of Science Project 2061 in its study of middle school science curricula (Kesidou and Roseman, 2002). Student groups explore four chemical reactions—burning, rusting, the decomposition of water, and the volcanic reaction of baking soda and vinegar. They cause these reactions to happen, obtain and record data in individual notebooks, analyze the data, and use evidence-based arguments to explain the data.

The instructional unit engages the students in a carefully structured sequence of hands-on laboratory investigations interwoven with other forms of instruction (Lynch, 2004). Student understanding is “pressed” through many experiences with the reactions and by group and individual pressures to make meaning of these reactions. For example, video transcripts indicate that students engaged in “science talk” during teacher demonstrations and during student experiments.

Researchers at George Washington University, in a partnership with Montgomery County public schools in Maryland, are currently conducting a five-year study of the feasibility of scaling up effective integrated instructional units, including CTA (Lynch, Kuipers, Pyke, and Szesze, in press). In 2001-2002, CTA was implemented in five highly diverse middle schools that were matched with five comparison schools using traditional curriculum materials in a quasi-experimental research design. All 8th graders in the five CTA schools, a total of about 1,500 students, participated in the CTA curriculum, while all 8th graders in the matched schools used the science curriculum materials normally available. Students were given pre- and posttests.

In 2002-2003, the study was replicated in the same five pairs of schools. In both years, students who participated in the CTA curriculum scored significantly higher than comparison students on a posttest. Average scores of students who participated in the CTA curriculum showed higher levels of fluency with the concept of conservation of matter (Lynch, 2004). However, because the concept is so difficult, most students in both the treatment and control group still have misconceptions, and few have a flexible, fully scientific understanding of the conservation of matter. All subgroups of students who were engaged in the CTA curriculum—including low-income students (eligible for free and reduced-price meals), black and Hispanic students, English language learners, and students eligible for special educational services—scored significantly higher than students in the control group on the posttest (Lynch and O’Donnell, 2005). The effect sizes were largest among three subgroups considered at risk for low science achievement, including Hispanic students, low-income students, and English language learners.

Based on these encouraging results, CTA was scaled up to include about 6,000 8th graders in 20 schools in 2003-2004 and 12,000 8th graders in 37 schools in 2004-2005 (Lynch and O’Donnell, 2005).

ThinkerTools

The ThinkerTools instructional unit is a sequence of laboratory experiences and other learning activities that, in its initial version, yielded substantial gains in students’ understanding of Newton’s laws of motion (White, 1993). Building on these positive results, ThinkerTools was expanded to focus not only on mastery of these laws of motion but also on scientific reasoning and understanding of the nature of science (White and Frederiksen, 1998). In the 10-week unit, students were guided to reflect on their own thinking and learning while they carry out a series of investigations. The integrated instructional unit was designed to help them learn about science processes as well as about the subject of force and motion. The instructional unit supports students as they formulate hypotheses, conduct empirical investigations, work with conceptually analogous computer simulations, and refine a conceptual model for the phenomena. Across the series of investigations, the integrated instructional unit introduces increasingly complex concepts. Formative assessments are integrated throughout the instructional sequence in ways that allow students to self-assess and reflect on core aspects of inquiry and epistemological dimensions of learning.

Researchers investigated the impact of Thinker Tools in 12 7th, 8th, and 9th grade classrooms with 3 teachers and 343 students. The researchers evaluated students’ developing understanding of scientific investigations using a pre-post inquiry test. In this assessment, students were engaged in a thought experiment that asked them to conceptualize, design, and think through a hypothetical research study. Gains in scores for students in the reflective self-assessment classes and control classrooms were compared. Results were also broken out by students categorized as high and low achieving, based on performance on a standardized test conducted before the intervention. Students in the reflective self-assessment classes exhibited greater gains on a test of investigative skills. This was especially true for low-achieving students. The researchers further analyzed specific components of the associated scientific processes—formulation of hypotheses, designing an experiment, predicting results, drawing conclusions from made-up results, and relating those conclusions back to the original hypotheses. Students in the reflective-self-assessment classes did better on all of these components than those in control classrooms, especially on the more difficult components (drawing conclusions and relating them to the original hypotheses).

Computer as Learning Partner

Beginning in 1980, a large group of technologists, classroom teachers, and education researchers developed the Computer as Learning Partner (CLP)

integrated instructional unit. Over 10 years, the team developed and tested eight versions of a 12-week unit on thermodynamics. Each year, a cohort of about 300 8th grade students participated in a sequence of teaching and learning activities focused primarily on a specific learning goal—enhancing students’ understanding of the difference between heat and temperature (Linn, 1997). The project engaged students in a sequence of laboratory experiences supported by computers, discussions, and other forms of science instruction. For example, computer images and words prompted students to make predictions about heat and conductivity and perform experiments using temperature-sensitive probes to confirm or refute their predictions. Students were given tasks related to scientific phenomena affecting their daily lives—such as how to keep a drink cold for lunch or selecting appropriate clothing for hiking in the mountains—as a way to motivate their interest and curiosity. Teachers play an important role in carrying out the curriculum, asking students to critique their own and each others’ investigations and encouraging them to reflect on their own thinking.

Over 10 years of study and revision, the integrated instructional unit proved increasingly effective in achieving its stated learning goals. Before the sequenced instruction was introduced, only 3 percent of middle school students could adequately explain the difference between heat and temperature. Eight versions later, about half of the students participating in CLP could explain this difference, representing a 400 percent increase in achievement. In addition, nearly 100 percent of students who participated in the final version of the instructional unit demonstrated understanding of conductors (Linn and Songer, 1991). By comparison, only 25 percent of a group of undergraduate chemistry students at the University of California at Berkeley could adequately explain the difference between heat and temperature. A longitudinal study comparing high school seniors who participated in the thermodynamics unit in middle school with seniors who had received more traditional middle school science instruction found a 50 percent improvement in CLP students’ performance in distinguishing between heat and temperature (Linn and Hsi, 2000)

Participating in the CLP instructional unit also increased students’ interest in science. Longitudinal studies of CLP participants revealed that, among those who went on to take high school physics, over 90 percent thought science was relevant to their lives. And 60 percent could provide examples of scientific phenomena in their daily lives. By comparison, only 60 percent of high school physics students who had not participated in the unit during middle school thought science was relevant to their lives, and only 30 percent could give examples in their daily lives (Linn and Hsi, 2000).

EFFECTIVENESS OF LABORATORY EXPERIENCES

Description of the literature review.

The committee’s review of the literature on the effectiveness of laboratory experiences considered studies of typical laboratory experiences and emerging research focusing on integrated instructional units. In reviewing both bodies of research, we aim to specify how laboratory experiences can further each of the science learning goals outlined at the beginning of this chapter.

Limitations of the Research

Our review was complicated by weaknesses in the earlier research on typical laboratory experiences, isolated from the stream of instruction (Hofstein and Lunetta, 1982). First, the investigators do not agree on a precise definition of the “laboratory” experiences under study. Second, many studies were weak in the selection and control of variables. Investigators failed to examine or report important variables relating to student abilities and attitudes. For example, they failed to note students’ prior laboratory experiences. They also did not give enough attention to extraneous factors that might affect student outcomes, such as instruction outside the laboratory. Third, the studies of typical laboratory experiences usually involved a small group of students with little diversity, making it difficult to generalize the results to the large, diverse population of U.S. high schools today. Fourth, investigators did not give enough attention to the adequacy of the instruments used to measure student outcomes. As an example, paper and pencil tests that focus on testing mastery of subject matter, the most frequently used assessment, do not capture student attainment of all of the goals we have identified. Such tests are not able to measure student progress toward goals that may be unique to laboratory experiences, such as developing scientific reasoning, understanding the complexity and ambiguity of empirical work, and development of practical skills.

Finally, most of the available research on typical laboratory experiences does not fully describe these activities. Few studies have examined teacher behavior, the classroom learning environment, or variables identifying teacher-student interaction. In addition, few recent studies have focused on laboratory manuals—both what is in them and how they are used. Research on the intended design of laboratory experiences, their implementation, and whether the implementation resembles the initial design would provide the understanding needed to guide improvements in laboratory instruction. However, only a few studies of typical laboratory experiences have measured the effectiveness of particular laboratory experiences in terms of both the extent

to which their activities match those that the teacher intended and the extent to which the students’ learning matches the learning objectives of the activity (Tiberghien, Veillard, Le Marchal, Buty, and Millar, 2000).

We also found weaknesses in the evolving research on integrated instructional units. First, these new units tend to be hothouse projects; researchers work intensively with teachers to construct atypical learning environments. While some have been developed and studied over a number of years and iterations, they usually involve relatively small samples of students. Only now are some of these efforts expanding to a scale that will allow robust generalizations about their value and how best to implement them. Second, these integrated instructional units have not been designed specifically to contrast some version of laboratory or practical experience with a lack of such experience. Rather, they assume that educational interventions are complex, systemic “packages” (Salomon, 1996) involving many interactions that may influence specific outcomes, and that science learning requires some opportunities for direct engagement with natural phenomena. Researchers commonly aim to document the complex interactions between and among students, teachers, laboratory materials, and equipment in an effort to develop profiles of successful interventions (Cobb et al., 2003; Collins, Joseph, and Bielaczyc, 2004; Design-Based Research Collective, 2003). These newer studies focus on how to sequence laboratory experiences and other forms of science instruction to support students’ science learning.

Scope of the Literature Search

A final note on the review of research: the scope of our study did not allow for an in-depth review of all of the individual studies of laboratory education conducted over the past 30 years. Fortunately, three major reviews of the literature from the 1970s, 1980s, and 1990s are available (Lazarowitz and Tamir, 1994; Lunetta, 1998; Hofstein and Lunetta, 2004). The committee relied on these reviews in our analysis of studies published before 1994. To identify studies published between 1994 and 2004, the committee searched electronic databases.

To supplement the database search, the committee commissioned three experts to review the nascent body of research on integrated instructional units (Bell, 2005; Duschl, 2004; Millar, 2004). We also invited researchers who are currently developing, revising, and studying the effectiveness of integrated instructional units to present their findings at committee meetings (Linn, 2004; Lynch, 2004).

All of these activities yielded few studies that focused on the high school level and were conducted in the United States. For this reason, the committee expanded the range of the literature considered to include some studies targeted at middle school and some international studies. We included stud-

ies at the elementary through postsecondary levels as well as studies of teachers’ learning in our analysis. In drawing conclusions from studies that were not conducted at the high school level, the committee took into consideration the extent to which laboratory experiences in high school differ from those in elementary and postsecondary education. Developmental differences among students, the organizational structure of schools, and the preparation of teachers are a few of the many factors that vary by school level and that the committee considered in making inferences from the available research. Similarly, when deliberating on studies conducted outside the United States, we considered differences in the science curriculum, the organization of schools, and other factors that might influence the outcomes of laboratory education.

Mastery of Subject Matter

Evidence from research on typical laboratory experiences.

Claims that typical laboratory experiences help students master science content rest largely on the argument that opportunities to directly interact with, observe, and manipulate materials will help students to better grasp difficult scientific concepts. It is believed that these experiences will force students to confront their misunderstandings about phenomena and shift toward more scientific understanding.

Despite these claims, there is almost no direct evidence that typical laboratory experiences that are isolated from the flow of science instruction are particularly valuable for learning specific scientific content (Hofstein and Lunetta, 1982, 2004; Lazarowitz and Tamir, 1994). White (1996) points out that many major reviews of science education from the 1960s and 1970s indicate that laboratory work does little to improve understanding of science content as measured by paper and pencil tests, and later studies from the 1980s and early 1990s do not challenge this view. Other studies indicate that typical laboratory experiences are no more effective in helping students master science subject matter than demonstrations in high school biology (Coulter, 1966), demonstration and discussion (Yager, Engen, and Snider, 1969), and viewing filmed experiments in chemistry (Ben-Zvi, Hofstein, Kempa, and Samuel, 1976). In contrast to most of the research, a single comparative study (Freedman, 2002) found that students who received regular laboratory instruction over the course of a school year performed better on a test of physical science knowledge than a control group of students who took a similar physical science course without laboratory activities.

Clearly, most of the evidence does not support the argument that typical laboratory experiences lead to improved learning of science content. More specifically, concrete experiences with phenomena alone do not appear to

force students to confront their misunderstandings and reevaluate their own assumptions. For example, VandenBerg, Katu, and Lunetta (1994) reported, on the basis of clinical studies with individual students, that hands-on activities with introductory electricity materials facilitated students’ understanding of the relationships among circuit elements and variables. The carefully selected practical activities created conceptual conflict in students’ minds—a first step toward changing their naïve ideas about electricity. However, the students remained unable to develop a fully scientific mental model of a circuit system. The authors suggested that greater engagement with conceptual organizers, such as analogies and concept maps, could have helped students develop more scientific understandings of basic electricity. Several researchers, including Dupin and Joshua (1987), have reported similar findings. Studies indicate that students often hold beliefs so intensely that even their observations in the laboratory are strongly influenced by those beliefs (Champagne, Gunstone, and Klopfer, 1985, cited in Lunetta, 1998; Linn, 1997). Students tend to adjust their observations to fit their current beliefs rather than change their beliefs in the face of conflicting observations.

Evidence from Research on Integrated Instructional Units

Current integrated instructional units build on earlier studies that found integration of laboratory experiences with other instructional activities enhanced mastery of subject matter (Dupin and Joshua, 1987; White and Gunstone, 1992, cited in Lunetta, 1998). A recent review of these and other studies concluded (Hofstein and Lunetta, 2004, p. 33):

When laboratory experiences are integrated with other metacognitive learning experiences such as “predict-observe-explain” demonstrations (White and Gunstone, 1992) and when they incorporate the manipulation of ideas instead of simply materials and procedures, they can promote the learning of science.

Integrated instructional units often focus on complex science topics that are difficult for students to understand. Their design is based on research on students’ intuitive conceptions of a science topic and how those conceptions differ from scientific conceptions. Students’ ideas often do not match the scientific understanding of a phenomenon and, as noted previously, these intuitive notions are resistant to change. For this reason, the sequenced units incorporate instructional activities specifically designed to confront intuitive conceptions and provide an environment in which students can construct normative conceptions. The role of laboratory experiences is to emphasize the discrepancies between students’ intuitive ideas about the topic and scientific ideas, as well as to support their construction of normative understanding. In order to help students link formal, scientific concepts to real

phenomena, these units include a sequence of experiences that will push them to question their intuitive and often inaccurate ideas.

Emerging studies indicate that exposure to these integrated instructional units leads to demonstrable gains in student mastery of a number of science topics in comparison to more traditional approaches. In physics, these subjects include Newtonian mechanics (Wells, Hestenes, and Swackhamer, 1995; White, 1993); thermodynamics (Songer and Linn, 1991); electricity (Shaffer and McDermott, 1992); optics (Bell and Linn, 2000; Reiner, Pea, and Shulman, 1995); and matter (Lehrer, Schauble, Strom, and Pligge, 2001; Smith, Maclin, Grosslight, and Davis, 1997; Snir, Smith, and Raz, 2003). Integrated instructional units in biology have enhanced student mastery of genetics (Hickey, Kindfield, Horwitz, and Christie, 2003) and natural selection (Reiser et al., 2001). A chemistry unit has led to gains in student understanding of stoichiometry (Lynch, 2004). Many, but not all, of these instructional units combine computer-based simulations of the phenomena under study with direct interactions with these phenomena. The role of technology in providing laboratory experiences is described later in this chapter.

Developing Scientific Reasoning

While philosophers of science now agree that there is no single scientific method, they do agree that a number of reasoning skills are critical to research across the natural sciences. These reasoning skills include identifying questions and concepts that guide scientific investigations, designing and conducting scientific investigations, developing and revising scientific explanations and models, recognizing and analyzing alternative explanations and models, and making and defending a scientific argument. It is not necessarily the case that these skills are sequenced in a particular way or used in every scientific investigation. Instead, they are representative of the abilities that both scientists and students need to investigate the material world and make meaning out of those investigations. Research on children’s and adults’ scientific reasoning (see the review by Zimmerman, 2000) suggests that effective experimentation is difficult for most people and not learned without instructional support.

Early research on the development of investigative skills suggested that students could learn aspects of scientific reasoning through typical laboratory instruction in college-level physics (Reif and St. John, 1979, cited in Hofstein and Lunetta, 1982) and in high school and college biology (Raghubir, 1979; Wheatley, 1975, cited in Hofstein and Lunetta, 1982).

More recent research, however, suggests that high school and college science teachers often emphasize laboratory procedures, leaving little time for discussion of how to plan an investigation or interpret its results (Tobin, 1987; see Chapter 4 ). Taken as a whole, the evidence indicates that typical laboratory work promotes only a few aspects of the full process of scientific reasoning—making observations and organizing, communicating, and interpreting data gathered from these observations. Typical laboratory experiences appear to have little effect on more complex aspects of scientific reasoning, such as the capacity to formulate research questions, design experiments, draw conclusions from observational data, and make inferences (Klopfer, 1990, cited in White, 1996).

Research developing from studies of integrated instructional units indicates that laboratory experiences can play an important role in developing all aspects of scientific reasoning, including the more complex aspects, if the laboratory experiences are integrated with small group discussion, lectures, and other forms of science instruction. With carefully designed instruction that incorporates opportunities to conduct investigations and reflect on the results, students as young as 4th and 5th grade can develop sophisticated scientific thinking (Lehrer and Schauble, 2004; Metz, 2004). Kuhn and colleagues have shown that 5th graders can learn to experiment effectively, albeit in carefully controlled domains and with extended supervised practice (Kuhn, Schauble, and Garcia-Mila, 1992). Explicit instruction on the purposes of experiments appears necessary to help 6th grade students design them well (Schauble, Giaser, Duschl, Schulze, and John, 1995).These studies suggest that laboratory experiences must be carefully designed to support the development of scientific reasoning.

Given the difficulty most students have with reasoning scientifically, a number of instructional units have focused on this goal. Evidence from several studies indicates that, with the appropriate scaffolding provided in these units, students can successfully reason scientifically. They can learn to design experiments (Schauble et al., 1995; White and Frederiksen, 1998), make predictions (Friedler, Nachmias, and Linn, 1990), and interpret and explain data (Bell and Linn, 2000; Coleman, 1998; Hatano and Inagaki, 1991; Meyer and Woodruff, 1997; Millar, 1998; Rosebery, Warren, and Conant, 1992; Sandoval and Millwood, 2005). Engagement with these instructional units has been shown to improve students’ abilities to recognize discrepancies between predicted and observed outcomes (Friedler et al., 1990) and to design good experiments (Dunbar, 1993; Kuhn et al., 1992; Schauble et al., 1995; Schauble, Klopfer, and Raghavan, 1991).

Integrated instructional units seem especially beneficial in developing scientific reasoning skills among lower ability students (White and Frederiksen, 1998).

Recently, research has focused on an important element of scientific reasoning—the ability to construct scientific arguments. Developing, revising, and communicating scientific arguments is now recognized as a core scientific practice (Driver, Newton, and Osborne, 2000; Duschl and Osborne, 2002). Laboratory experiences play a key role in instructional units designed to enhance students’ argumentation abilities, because they provide both the impetus and the data for constructing scientific arguments. Such efforts have taken many forms. For example, researchers working with young Haitian-speaking students in Boston used the students’ own interests to develop scientific investigations. Students designed an investigation to determine which school drinking fountain had the best-tasting water. The students designed data collection protocols, collected and analyzed their data, and then argued about their findings (Rosebery et al., 1992). The Knowledge Integration Environment project asked middle school students to examine a common set of evidence to debate competing hypotheses about light propagation. Overall, most students learned the scientific concept (that light goes on forever), although those who made better arguments learned more than their peers (Bell and Linn, 2000). These and other examples (e.g., Sandoval and Millwood, 2005) show that students in middle and high school can learn to argue scientifically, by learning to coordinate theoretical claims with evidence taken from their laboratory investigations.

Developing Practical Skills

Science educators and researchers have long claimed that learning practical laboratory skills is one of the important goals for laboratory experiences and that such skills may be attainable only through such experiences (White, 1996; Woolnough, 1983). However, development of practical skills has been measured in research less frequently than mastery of subject matter or scientific reasoning. Such practical outcomes deserve more attention, especially for laboratory experiences that are a critical part of vocational or technical training in some high school programs. When a primary goal of a program or course is to train students for jobs in laboratory settings, they must have the opportunity to learn to use and read sophisticated instruments and carry out standardized experimental procedures. The critical questions about acquiring these skills through laboratory experiences may not be whether laboratory experiences help students learn them, but how the experiences can be constructed so as to be most effective in teaching such skills.

Some research indicates that typical laboratory experiences specifically focused on learning practical skills can help students progress toward other goals. For example, one study found that students were often deficient in the simple skills needed to successfully carry out typical laboratory activities, such as using instruments to make measurements and collect accurate data (Bryce and Robertson, 1985). Other studies indicate that helping students to develop relevant instrumentation skills in controlled “prelab” activities can reduce the probability that important measurements in a laboratory experience will be compromised due to students’ lack of expertise with the apparatus (Beasley, 1985; Singer, 1977). This research suggests that development of practical skills may increase the probability that students will achieve the intended results in laboratory experiences. Achieving the intended results of a laboratory activity is a necessary, though not sufficient, step toward effectiveness in helping students attain laboratory learning goals.

Some research on typical laboratory experiences indicates that girls handle laboratory equipment less frequently than boys, and that this tendency is associated with less interest in science and less self-confidence in science ability among girls (Jovanovic and King, 1998). It is possible that helping girls to develop instrumentation skills may help them to participate more actively and enhance their interest in learning science.

Studies of integrated instructional units have not examined the extent to which engagement with these units may enhance practical skills in using laboratory materials and equipment. This reflects an instructional emphasis on helping students to learn scientific ideas with real understanding and on developing their skills at investigating scientific phenomena, rather than on particular laboratory techniques, such as taking accurate measurements or manipulating equipment. There is no evidence to suggest that students do not learn practical skills through integrated instructional units, but to date researchers have not assessed such practical skills.

Understanding the Nature of Science

Throughout the past 50 years, studies of students’ epistemological beliefs about science consistently show that most of them have naïve views about the nature of scientific knowledge and how such knowledge is constructed and evaluated by scientists over time (Driver, Leach, Millar, and Scott, 1996; Lederman, 1992). The general public understanding of science is similarly inaccurate. Firsthand experience with science is often seen as a key way to advance students’ understanding of and appreciation for the conventions of science. Laboratory experiences are considered the primary mecha-

nism for providing firsthand experience and are therefore assumed to improve students’ understanding of the nature of science.

Research on student understanding of the nature of science provides little evidence of improvement with science instruction (Lederman, 1992; Driver et al., 1996). Although much of this research historically did not examine details of students’ laboratory experiences, it often included very large samples of science students and thus arguably captured typical laboratory experiences (research from the late 1950s through the 1980s is reviewed by Lederman, 1992). There appear to be developmental trends in students’ understanding of the relations between experimentation and theory-building. Younger students tend to believe that experiments yield direct answers to questions; during middle and high school, students shift to a vague notion of experiments being tests of ideas. Only a small number of students appear to leave high school with a notion of science as model-building and experimentation, in an ongoing process of testing and revision (Driver et al., 1996; Carey and Smith, 1993; Smith et al., 2000). The conclusion that most experts draw from these results is that the isolated nature and rote procedural focus of typical laboratory experiences inhibits students from developing robust conceptions of the nature of science. Consequently, some have argued that the nature of science must be an explicit target of instruction (Khishfe and Abd-El-Khalick, 2002; Lederman, Abd-El-Khalick, Bell, and Schwartz, 2002).

As discussed above, there is reasonable evidence that integrated instructional units help students to learn processes of scientific inquiry. However, such instructional units do not appear, on their own, to help students develop robust conceptions of the nature of science. One large-scale study of a widely available inquiry-oriented curriculum, in which integrated instructional units were an explicit feature, showed no significant change in students’ ideas about the nature of science after a year’s instruction (Meichtry, 1993). Students engaged in the BGuILE science instructional unit showed no gains in understanding the nature of science from their participation, and they seemed not even to see their experience in the unit as necessarily related to professional science (Sandoval and Morrison, 2003). These findings and others have led to the suggestion that the nature of science must be an explicit target of instruction (Lederman et al., 2002).

There is evidence from the ThinkerTools science instructional unit that by engaging in reflective self-assessment on their own scientific investiga-

tions, students gained a more sophisticated understanding of the nature of science than matched control classes who used the curriculum without the ongoing monitoring and evaluation of their own and others’ research (White and Frederiksen, 1998). Students who engaged in the reflective assessment process “acquire knowledge of the forms that scientific laws, models, and theories can take, and of how the development of scientific theories is related to empirical evidence” (White and Frederiksen, 1998, p. 92). Students who participated in the laboratory experiences and other learning activities in this unit using the reflective assessment process were less likely to “view scientific theories as immutable and never subject to revision” (White and Frederiksen, 1998, p. 72). Instead, they saw science as meaningful and explicable. The ThinkerTools findings support the idea that attention to nature of science issues should be an explicit part of integrated instructional units, although even with such attention it remains difficult to change students’ ideas (Khishfe and Abd-el-Khalick, 2002).

A survey of several integrated instructional units found that they seem to bridge the “language gap” between science in school and scientific practice (Duschl, 2004). The units give students “extended opportunities to explore the relationship between evidence and explanation,” helping them not only to develop new knowledge (mastery of subject matter), but also to evaluate claims of scientific knowledge, reflecting a deeper understanding of the nature of science (Duschl, 2004). The available research leaves open the question of whether or not these experiences help students to develop an explicit, reflective conceptual framework about the nature of science.

Cultivating Interest in Science and Interest in Learning Science

Studies of the effect of typical laboratory experiences on student interest are much rarer than those focusing on student achievement or other cognitive outcomes (Hofstein and Lunetta, 2004; White, 1996). The number of studies that address interest, attitudes, and other affective outcomes has decreased over the past decade, as researchers have focused almost exclusively on cognitive outcomes (Hofstein and Lunetta, 2004). Among the few studies available, the evidence is mixed. Some studies indicate that laboratory experiences lead to more positive attitudes (Renner, Abraham, and Birnie, 1985; Denny and Chennell, 1986). Other studies show no relation between laboratory experiences and affect (Ato and Wilkinson, 1986; Freedman, 2002), and still others report laboratory experiences turned students away from science (Holden, 1990; Shepardson and Pizzini, 1993).

There are, however, two apparent weaknesses in studies of interest and attitude (Hofstein and Lunetta, 1982). One is that researchers often do not carefully define interest and how it should be measured. Consequently, it is unclear if students simply reported liking laboratory activities more than other classroom activities, or if laboratory activities engendered more interest in science as a field, or in taking science courses, or something else. Similarly, studies may report increased positive attitudes toward science from students’ participation in laboratory experiences, without clear description of what attitudes were measured, how large the changes were, or whether changes persisted over time.

Student Perceptions of Typical Laboratory Experiences

Students’ perceptions of laboratory experiences may affect their interest and engagement in science, and some studies have examined those perceptions. Researchers have found that students often do not have clear ideas about the general or specific purposes of their work in typical science laboratory activities (Chang and Lederman, 1994) and that their understanding of the goals of lessons frequently do not match their teachers’ goals for the same lessons (Hodson, 1993; Osborne and Freyberg, 1985; Wilkenson and Ward, 1997). When students do not understand the goals of experiments or laboratory investigations, negative consequences for learning occur (Schauble et al., 1995). In fact, students often do not make important connections between the purpose of a typical laboratory investigation and the design of the experiments. They do not connect the experiment with what they have done earlier, and they do not note the discrepancies among their own concepts, the concepts of their peers, and those of the science community (Champagne et al., 1985; Eylon and Linn, 1988; Tasker, 1981). As White (1998) notes, “to many students, a ‘lab’ means manipulating equipment but not manipulating ideas.” Thus, in considering how laboratory experiences may contribute to students’ interest in science and to other learning goals, their perceptions of those experiences must be considered.

A series of studies using the Science Laboratory Environment Inventory (SLEI) has demonstrated links between students’ perceptions of laboratory experiences and student outcomes (Fraser, McRobbie, and Giddings, 1993; Fraser, Giddings, and McRobbie, 1995; Henderson, Fisher, and Fraser, 2000; Wong and Fraser, 1995). The SLEI, which has been validated cross-nationally, measures five dimensions of the laboratory environment: student cohesiveness, open-endedness, integration, rule clarity, and material environment (see Table 3-1 for a description of each scale). Using the SLEI, researchers have studied students’ perceptions of chemistry and biology laboratories in several countries, including the United States. All five dimensions appear to be positively related with student attitudes, although the

TABLE 3-1 Descriptive Information for the Science Laboratory Environment Inventory

relation of open-endedness with attitudes seems to vary with student population. In some populations, there is a negative relation to attitudes (Fraser et al., 1995) and to some cognitive outcomes (Henderson et al., 2000).

Research using the SLEI indicates that positive student attitudes are particularly strongly associated with cohesiveness (the extent to which students know, help, and are supportive of one another) and integration (the extent to which laboratory activities are integrated with nonlaboratory and theory classes) (Fraser et al.,1995; Wong and Fraser, 1995). Integration also shows a positive relation to students’ cognitive outcomes (Henderson et al., 2000; McRobbie and Fraser, 1993).

Students’ interest and attitudes have been measured less often than other goals of laboratory experiences in studies of integrated instructional units. When evidence is available, it suggests that students who participate in these units show greater interest in and more positive attitudes toward science. For example, in a study of ThinkerTools, completion of projects was used as a measure of student interest. The rate of submitting completed projects was higher for students in the ThinkerTools curriculum than for those in traditional instruction. This was true for all grades and ability levels (White and

Frederiksen, 1998). This study also found that students’ ongoing evaluation of their own and other students’ thinking increased motivation and self-confidence in their individual ability: students who participated in this ongoing evaluation not only turned in their final project reports more frequently, but they were also less likely to turn in reports that were identical to their research partner’s.

Participation in the ThinkerTools instructional unit appears to change students’ attitudes toward learning science. After completing the integrated instructional unit, fewer students indicated that “being good at science” was a result of inherited traits, and fewer agreed with the statement, “In general, boys tend to be naturally better at science than girls.” In addition, more students indicated that they preferred taking an active role in learning science, rather than simply being told the correct answer by the teacher (White and Frederiksen, 1998).

Researchers measured students’ engagement and motivation to master the complex topic of conservation of matter as part of the study of CTA. Students who participated in the CTA curriculum had higher levels of basic engagement (active participation in activities) and were more likely to focus on learning from the activities than students in the control group (Lynch et al., in press). This positive effect on engagement was especially strong among low-income students. The researchers speculate, “perhaps as a result of these changes in engagement and motivation, they learned more than if they had received the standard curriculum” (Lynch et al., in press).

Students who participated in CLP during middle school, when surveyed years later as high school seniors, were more likely to report that science is relevant to their lives than students who did not participate (Linn and Hsi, 2000). Further research is needed to illuminate which aspects of this instructional unit contribute to increased interest.

Developing Teamwork Abilities

Teamwork and collaboration appear in research on typical laboratory experiences in two ways. First, working in groups is seen as a way to enhance student learning, usually with reference to literature on cooperative learning or to the importance of providing opportunities for students to discuss their ideas. Second and more recently, attention has focused on the ability to work in groups as an outcome itself, with laboratory experiences seen as an ideal opportunity to develop these skills. The focus on teamwork as an outcome is usually linked to arguments that this is an essential skill for workers in the 21st century (Partnership for 21st Century Skills, 2003).

There is considerable evidence that collaborative work can help students learn, especially if students with high ability work with students with low ability (Webb and Palincsar, 1996). Collaboration seems especially helpful to lower ability students, but only when they work with more knowledgeable peers (Webb, Nemer, Chizhik, and Sugrue, 1998). Building on this research, integrated instructional units engage students in small-group collaboration as a way to encourage them to connect what they know (either from their own experiences or from prior instruction) to their laboratory experiences. Often, individual students disagree about prospective answers to the questions under investigation or the best way to approach them, and collaboration encourages students to articulate and explain their reasoning. A number of studies suggest that such collaborative investigation is effective in helping students to learn targeted scientific concepts (Coleman, 1998; Roschelle, 1992).

Extant research lacks specific assessment of the kinds of collaborative skills that might be learned by individual students through laboratory work. The assumption appears to be that if students collaborate and such collaborations are effective in supporting their conceptual learning, then they are probably learning collaborative skills, too.

Overall Effectiveness of Laboratory Experiences

The two bodies of research—the earlier research on typical laboratory experiences and the emerging research on integrated instructional units—yield different findings about the effectiveness of laboratory experiences in advancing the goals identified by the committee. In general, the nascent body of research on integrated instructional units offers the promise that laboratory experiences embedded in a larger stream of science instruction can be more effective in advancing these goals than are typical laboratory experiences (see Table 3-2 ).

Research on the effectiveness of typical laboratory experiences is methodologically weak and fragmented. The limited evidence available suggests that typical laboratory experiences, by themselves, are neither better nor worse than other methods of science instruction for helping students master science subject matter. However, more recent research indicates that integrated instructional units enhance students’ mastery of subject matter. Studies have demonstrated increases in student mastery of complex topics in physics, chemistry, and biology.

Typical laboratory experiences appear, based on the limited research available, to support some aspects of scientific reasoning; however, typical laboratory experiences alone are not sufficient for promoting more sophisticated scientific reasoning abilities, such as asking appropriate questions,

TABLE 3-2 Attainment of Educational Goals in Typical Laboratory Experiences and Integrated Instructional Units

designing experiments, and drawing inferences. Research on integrated instructional units provides evidence that the laboratory experiences and other forms of instruction they include promote development of several aspects of scientific reasoning, including the ability to ask appropriate questions, design experiments, and draw inferences.

The evidence indicates that typical laboratory experiences do little to increase students’ understanding of the nature of science. In contrast, some studies find that participating in integrated instructional units that are designed specifically with this goal in mind enhances understanding of the nature of science.

The available research suggests that typical laboratory experiences can play a role in enhancing students’ interest in science and in learning science. There is evidence that engagement with the laboratory experiences and other learning activities included in integrated instructional units enhances students’ interest in science and motivation to learn science.

In sum, the evolving research on integrated instructional units provides evidence of increases in students’ understanding of subject matter, development of scientific reasoning, and interest in science, compared with students who received more traditional forms of science instruction. Studies conducted to date also suggest that the units are effective in helping diverse groups of students attain these three learning goals. In contrast, the earlier research on typical laboratory experiences indicates that such typical laboratory experiences are neither better nor worse than other forms of science instruction in supporting student mastery of subject matter. Typical laboratory experiences appear to aid in development of only some aspects of scientific reasoning, and they appear to play a role in enhancing students’ interest in science and in learning science.

Due to a lack of available studies, the committee was unable to draw conclusions about the extent to which either typical laboratory experiences or laboratory experiences incorporated into integrated instructional units might advance the other goals identified at the beginning of this chapter—enhancing understanding of the complexity and ambiguity of empirical work, acquiring practical skills, and developing teamwork skills.

PRINCIPLES FOR DESIGN OF EFFECTIVE LABORATORY EXPERIENCES

The three bodies of research we have discussed—research on how people learn, research on typical laboratory experiences, and developing research on how students learn in integrated instructional units—yield information that promises to inform the design of more effective laboratory experiences.

The committee considers the emerging evidence sufficient to suggest four general principles that can help laboratory experiences achieve the goals outlined above. It must be stressed, however, that research to date has not described in much detail how these principles can be implemented nor how each principle might relate to each of the educational goals of laboratory experiences.

Clearly Communicated Purposes

Effective laboratory experiences have clear learning goals that guide the design of the experience. Ideally these goals are clearly communicated to students. Without a clear understanding of the purposes of a laboratory activity, students seem not to get much from it. Conversely, when the purposes of a laboratory activity are clearly communicated by teachers to students, then students seem capable of understanding them and carrying them out. There seems to be no compelling evidence that particular purposes are more understandable to students than others.

Sequenced into the Flow of Instruction

Effective laboratory experiences are thoughtfully sequenced into the flow of classroom science instruction. That is, they are explicitly linked to what has come before and what will come after. A common theme in reviews of laboratory practice in the United States is that laboratory experiences are presented to students as isolated events, unconnected with other aspects of classroom work. In contrast, integrated instructional units embed laboratory experiences with other activities that build on the laboratory experiences and push students to reflect on and better understand these experiences. The way a particular laboratory experience is integrated into a flow of activities should be guided by the goals of the overall sequence of instruction and of the particular laboratory experience.

Integrated Learning of Science Concepts and Processes

Research in the learning sciences (National Research Council, 1999, 2001) strongly implies that conceptual understanding, scientific reasoning, and practical skills are three capabilities that are not mutually exclusive. An educational program that partitions the teaching and learning of content from the teaching and learning of process is likely to be ineffective in helping students develop scientific reasoning skills and an understanding of science as a way of knowing. The research on integrated instructional units, all of which intertwine exploration of content with process through laboratory experiences, suggests that integration of content and process promotes attainment of several goals identified by the committee.

Ongoing Discussion and Reflection

Laboratory experiences are more likely to be effective when they focus students more on discussing the activities they have done during their laboratory experiences and reflecting on the meaning they can make from them, than on the laboratory activities themselves. Crucially, the focus of laboratory experiences and the surrounding instructional activities should not simply be on confirming presented ideas, but on developing explanations to make sense of patterns of data. Teaching strategies that encourage students to articulate their hypotheses about phenomena prior to experimentation and to then reflect on their ideas after experimentation are demonstrably more successful at supporting student attainment of the goals of mastery of subject matter, developing scientific reasoning, and increasing interest in science and science learning. At the same time, opportunities for ongoing discussion and reflection could potentially support students in developing teamwork skills.

COMPUTER TECHNOLOGIES AND LABORATORY EXPERIENCES

From scales to microscopes, technology in many forms plays an integral role in most high school laboratory experiences. Over the past two decades, personal computers have enabled the development of software specifically designed to help students learn science, and the Internet is an increasingly used tool for science learning and for science itself. This section examines the role that computer technologies now and may someday play in science learning in relation to laboratory experiences. Certain uses of computer technology can be seen as laboratory experiences themselves, according to the committee’s definition, to the extent that they allow students to interact with data drawn directly from the world. Other uses, less clearly laboratory experiences in themselves, provide certain features that aid science learning.

Computer Technologies Designed to Support Learning

Researchers and science educators have developed a number of software programs to support science learning in various ways. In this section, we summarize what we see as the main ways in which computer software can support science learning through providing or augmenting laboratory experiences.

Scaffolded Representations of Natural Phenomena

Perhaps the most common form of science education software are programs that enable students to interact with carefully crafted models of natural phenomena that are difficult to see and understand in the real world and have proven historically difficult for students to understand. Such programs are able to show conceptual interrelationships and connections between theoretical constructs and natural phenomena through the use of multiple, linked representations. For example, velocity can be linked to acceleration and position in ways that make the interrelationships understandable to students (Roschelle, Kaput, and Stroup, 2000). Chromosome genetics can be linked to changes in pedigrees and populations (Horowitz, 1996). Molecular chemical representations can be linked to chemical equations (Kozma, 2003).

In the ThinkerTools integrated instructional unit, abstracted representations of force and motion are provided for students to help them “see” such ideas as force, acceleration, and velocity in two dimensions (White, 1993; White and Frederiksen, 1998). Objects in the ThinkerTools microworld are represented as simple, uniformly sized “dots” to avoid students becoming confused about the idea of center of mass. Students use the microworld to solve various problems of motion in one or two dimensions, using the com-

puter keyboard to apply forces to dots to move them along specified paths. Part of the key to the software’s guidance is that it provides representations of forces and accelerations in which students can see change in response to their actions. A “dot trace,” for example, shows students how applying more force affects an object’s acceleration in a predictable way. A “vector cross” represents the individual components of forces applied in two dimensions in a way that helps students to link those forces to an object’s motion.

ThinkerTools is but one example of this type of interactive, representational software. Others have been developed to help students reason about motion (Roschelle, 1992), electricity (Gutwill, Fredericksen, and White, 1999), heat and temperature (Linn, Bell, and Hsi, 1998), genetics (Horwitz and Christie, 2000), and chemical reactions (Kozma, 2003), among others. These programs differ substantially from one another in how they represent their target phenomena, as there are substantial differences in the topics themselves and in the problems that students are known to have in understanding them. They share, however, a common approach to solving a similar set of problems—how to represent natural phenomena that are otherwise invisible in ways that help students make their own thinking explicit and guide them to normative scientific understanding.

When used as a supplement to hands-on laboratory experiences within integrated instructional units, these representations can support students’ conceptual change (e.g., Linn et al., 1998; White and Frederiksen, 1998). For example, students working through the ThinkerTools curriculum always experiment with objects in the real world before they work with the computer tools. The goals of the laboratory experiences are to provide some experience with the phenomena under study and some initial ideas that can then be explored on the computer.

Structured Simulations of Inaccessible Phenomena

Various types of simulations of phenomena represent another form of technology for science learning. These simulations allow students to explore and observe phenomena that are too expensive, infeasible, or even dangerous to interact with directly. Strictly speaking, a computer simulation is a program that simulates a particular phenomenon by running a computational model whose behavior can sometimes be changed by modifying input parameters to the model. For example, the GenScope program provides a set of linked representations of genetics and genetics phenomena that would otherwise be unavailable for study to most students (Horowitz and Christie, 2000). The software represents alleles, chromosomes, family pedigrees, and the like and links representations across levels in ways that enable students to trace inherited traits to specific genetic differences. The software uses an underlying Mendelian model of genetic inheritance to gov-

ern its behavior. As with the representations described above, embedding the use of the software in a carefully thought out curriculum sequence is crucial to supporting student learning (Hickey et al., 2000).

Another example in biology is the BGuILE project (Reiser et al., 2001). The investigators created a series of structured simulations allowing students to investigate problems of evolution by natural selection. In the Galapagos finch environment, for example, students can examine a carefully selected set of data from the island of Daphne Major to explain a historical case of natural selection. The BGuILE software does not, strictly speaking, consist of simulations because it does not “run” a model; from a student’s perspective, it simulates either Daphne Major or laboratory experiments on tuberculosis bacteria. Studies show that students can learn from the BGuILE environments when these environments are embedded in a well-organized curriculum (Sandoval and Reiser, 2004). They also show that successful implementation of such technology-supported curricula relies heavily on teachers (Tabak, 2004).

Structured Interactions with Complex Phenomena and Ideas

The examples discussed here share a crucial feature. The representations built into the software and the interface tools provided for learners are intended to help them learn in very specific ways. There are a great number of such tools that have been developed over the last quarter of a century. Many of them have been shown to produce impressive learning gains for students at the secondary level. Besides the ones mentioned, other tools are designed to structure specific scientific reasoning skills, such as prediction (Friedler et al., 1990) and the coordination of claims with evidence (Bell and Linn, 2000; Sandoval, 2003). Most of these efforts integrate students’ work on the computer with more direct laboratory experiences. Rather than thinking of these representations and simulations as a way to replace laboratory experiences, the most successful instructional sequences integrate them with a series of empirical laboratory investigations. These sequences of science instruction focus students’ attention on developing a shared interpretation of both the representations and the real laboratory experiences in small groups (Bell, 2005).

Computer Technologies Designed to Support Science

Advances in computer technologies have had a tremendous impact on how science is done and on what scientists can study. These changes are vast, and summarizing them is well beyond the scope of the committee’s charge. We found, however, that some innovations in scientific practice, especially uses of the Internet, are beginning to be applied to secondary

science education. With respect to future laboratory experiences, perhaps the most significant advance in many scientific fields is the aggregation of large, varied data sets into Internet-accessible databases. These databases are most commonly built for specific scientific communities, but some researchers are creating and studying new, learner-centered interfaces to allow access by teachers and schools. These research projects build on instructional design principles illuminated by the integrated instructional units discussed above.

One example is the Center for Embedded Networked Sensing (CENS), a National Science Foundation Science and Technology Center investigating the development and deployment of large-scale sensor networks embedded in physical environments. CENS is currently working on ecosystem monitoring, seismology, contaminant flow transport, and marine microbiology. As sensor networks come on line, making data available, science educators at the center are developing middle school curricula that include web-based tools to enable students to explore the same data sets that the professional scientists are exploring (Pea, Mills, and Takeuchi, 2004).

The interfaces professional scientists use to access such databases tend to be too inflexible and technical for students to use successfully (Bell, 2005). Bounding the space of possible data under consideration, supporting appropriate considerations of theory, and promoting understanding of the norms used in the visualization can help support students in developing a shared understanding of the data. With such support, students can develop both conceptual understanding and understanding of the data analysis process. Focusing students on causal explanation and argumentation based on the data analysis process can help them move from a descriptive, phenomenological view of science to one that considers theoretical issues of cause (Bell, 2005).

Further research and evaluation of the educational benefit of student interaction with large scientific databases are absolutely necessary. Still, the development of such efforts will certainly expand over time, and, as they change notions of what it means to conduct scientific experiments, they are also likely to change what it means to conduct a school laboratory.

The committee identified a number of science learning goals that have been attributed to laboratory experiences. Our review of the evidence on attainment of these goals revealed a recent shift in research, reflecting some movement in laboratory instruction. Historically, laboratory experiences have been disconnected from the flow of classroom science lessons. We refer to these separate laboratory experiences as typical laboratory experiences. Reflecting this separation, researchers often engaged students in one or two

experiments or other science activities and then conducted assessments to determine whether their understanding of the science concept underlying the activity had increased. Some studies compared the outcomes of these separate laboratory experiences with the outcomes of other forms of science instruction, such as lectures or discussions.

Over the past 10 years, researchers studying laboratory education have shifted their focus. Drawing on principles of learning derived from the cognitive sciences, they have asked how to sequence science instruction, including laboratory experiences, in order to support students’ science learning. We refer to these instructional sequences as “integrated instructional units.” Integrated instructional units connect laboratory experiences with other types of science learning activities, including lectures, reading, and discussion. Students are engaged in framing research questions, making observations, designing and executing experiments, gathering and analyzing data, and constructing scientific arguments and explanations.

The two bodies of research on typical laboratory experiences and integrated instructional units, including laboratory experiences, yield different findings about the effectiveness of laboratory experiences in advancing the science learning goals identified by the committee. The earlier research on typical laboratory experiences is weak and fragmented, making it difficult to draw precise conclusions. The weight of the evidence from research focused on the goals of developing scientific reasoning and enhancing student interest in science showed slight improvements in both after students participated in typical laboratory experiences. Research focused on the goal of student mastery of subject matter indicates that typical laboratory experiences are no more or less effective than other forms of science instruction (such as reading, lectures, or discussion).

Studies conducted to date on integrated instructional units indicate that the laboratory experiences, together with the other forms of instruction included in these units, show greater effectiveness for these same three goals (compared with students who received more traditional forms of science instruction): improving students’ mastery of subject matter, increasing development of scientific reasoning, and enhancing interest in science. Integrated instructional units also appear to be effective in helping diverse groups of students progress toward these three learning goals . A major limitation of the research on integrated instructional units, however, is that most of the units have been used in small numbers of science classrooms. Only a few studies have addressed the challenge of implementing—and studying the effectiveness of—integrated instructional units on a wide scale.

Due to a lack of available studies, the committee was unable to draw conclusions about the extent to which either typical laboratory experiences or integrated instructional units might advance the other goals identified at the beginning of this chapter—enhancing understanding of the complexity

and ambiguity of empirical work, acquiring practical skills, and developing teamwork skills. Further research is needed to clarify how laboratory experiences might be designed to promote attainment of these goals.

The committee considers the evidence sufficient to identify four general principles that can help laboratory experiences achieve the learning goals we have outlined. Laboratory experiences are more likely to achieve their intended learning goals if (1) they are designed with clear learning outcomes in mind, (2) they are thoughtfully sequenced into the flow of classroom science instruction, (3) they are designed to integrate learning of science content with learning about the processes of science, and (4) they incorporate ongoing student reflection and discussion.

Computer software and the Internet have enabled development of several tools that can support students’ science learning, including representations of complex phenomena, simulations, and student interaction with large scientific databases. Representations and simulations are most successful in supporting student learning when they are integrated in an instructional sequence that also includes laboratory experiences. Researchers are currently developing tools to support student interaction with—and learning from—large scientific databases.

Anderson, R.O. (1976). The experience of science: A new perspective for laboratory teaching . New York: Columbia University, Teachers College Press.

Ato, T., and Wilkinson, W. (1986). Relationships between the availability and use of science equipment and attitudes to both science and sources of scientific information in Benue State, Nigeria. Research in Science and Technological Education , 4 , 19-28.

Beasley, W.F. (1985). Improving student laboratory performance: How much practice makes perfect? Science Education , 69 , 567-576.

Bell, P. (2005). The school science laboratory: Considerations of learning, technology, and scientific practice . Paper prepared for the Committee on High School Science Laboratories: Role and Vision. Available at: http://www7.nationalacademies.org/bose/July_12-13_2004_High_School_Labs_Meeting_Agenda.html [accessed June 2005].

Bell, P., and Linn, M.C. (2000). Scientific arguments as learning artifacts: Designing for learning from the web with KIE. International Journal of Science Education , 22 (8), 797-817.

Ben-Zvi, R., Hofstein, A., Kampa, R.F, and Samuel, D. (1976). The effectiveness of filmed experiments in high school chemical education. Journal of Chemical Education , 53 , 518-520.

Blakeslee, T., Bronstein, L., Chapin, M., Hesbitt, D., Peek, Y., Thiele, E., and Vellanti, J. (1993). Chemistry that applies . Lansing: Michigan Department of Education. Available at: http://www.ed-web2.educ.msu.edu/CCMS/secmod/Cluster3.pdf [accessed Feb. 2005].

Bransford, J.D., and Schwartz, D.L. (2001). Rethinking transfer: A simple proposal with multiple implications. In A. Iran-Nejad, and P.D. Pearson (Eds.), Review of research in education (pp. 61-100). Washington, DC: American Educational Research Association.

Bryce, T.G.K., and Robertson, I.J. (1985). What can they do: A review of practical assessment in science. Studies in Science Education , 12 , 1-24.

Carey, S., and Smith, C. (1993). On understanding the nature of scientific knowledge. Educational Psychologist , 28 , 235-251.

Champagne, A.B., Gunstone, R.F., and Klopfer, L.E. (1985). Instructional consequences of students’ knowledge about physical phenomena. In L.H.T. West and A.L. Pines (Eds.), Cognitive structure and conceptual change (pp. 61-68). New York: Academic Press.

Chang, H.P., and Lederman, N.G. (1994). The effect of levels of co-operation within physical science laboratory groups on physical science achievement. Journal of Research in Science Teaching , 31 , 167-181.

Cobb, P., Confrey, J., diSessa, A., Lehrer, R., and Schauble, L. (2003). Design experiments in educational research. Educational Researcher , 32 (1), 9-13.

Cobb, P., Stephan, M., McClain, K., and Gavemeijer, K. (2001). Participating in classroom mathematical practices. Journal of the Learning Sciences , 10 , 113-164.

Coleman, E.B. (1998). Using explanatory knowledge during collaborative problem solving in science. Journal of the Learning Sciences , 7 (3, 4), 387-427.

Collins, A., Joseph, D., and Bielaczyc, K. (2004). Design research: Theoretical and methodological issues. Journal of the Learning Sciences , 13 (1), 15-42.

Coulter, J.C. (1966). The effectiveness of inductive laboratory demonstration and deductive laboratory in biology. Journal of Research in Science Teaching , 4 , 185-186.

Denny, M., and Chennell, F. (1986). Exploring pupils’ views and feelings about their school science practicals: Use of letter-writing and drawing exercises. Educational Studies , 12 , 73-86.

Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher , 32 (1), 5-8.

Driver, R. (1995). Constructivist approaches to science teaching. In L.P. Steffe and J. Gale (Eds.), Constructivism in education (pp. 385-400). Hillsdale, NJ: Lawrence Erlbaum.

Driver, R., Leach, J., Millar, R., and Scott, P. (1996). Young people’s images of science . Buckingham, UK: Open University Press.

Driver, R., Newton, P., and Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education , 84 , 287-312.

Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science , 17 , 397-434.

Dupin, J.J., and Joshua, S. (1987). Analogies and “modeling analogies” in teaching: Some examples in basic electricity. Science Education , 73 , 791-806.

Duschl, R.A. (2004). The HS lab experience: Reconsidering the role of evidence, explanation and the language of science . Paper prepared for the Committee on High School Science Laboratories: Role and Vision, July 12-13, National Research Council, Washington, DC. Available at: http://www7.nationalacademies.org/bose/July_12-13_2004_High_School_Labs_Meeting_Agenda.html [accessed July 2005].

Duschl, R.A., and Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education , 38 , 39-72.

Eylon, B., and Linn, M.C. (1988). Learning and instruction: An examination of four research perspectives in science education. Review of Educational Research , 58 (3), 251-301.

Fraser, B.J., Giddings, G.J., and McRobbie, C.J. (1995). Evolution and validation of a personal form of an instrument for assessing science laboratory classroom environments. Journal of Research in Science Teaching , 32 , 399-422.

Fraser, B.J., McRobbie, C.J., and Giddings, G.J. (1993). Development and cross-national validation of a laboratory classroom environment instrument for senior high school science. Science Education , 77 , 1-24.

Freedman, M.P. (2002). The influence of laboratory instruction on science achievement and attitude toward science across gender differences. Journal of Women and Minorities in Science and Engineering , 8 , 191-200.

Friedler, Y., Nachmias, R., and Linn, M.C. (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching , 27 (2), 173-192.

Glaser, R. (1994). Learning theory and instruction. In G. d’Ydewalle, P. Eelen, and P. Bertelson (Eds.), International perspectives on science, volume 2: The state of the art (pp. 341-357). Hove, England: Erlbaum.

Gobert, J., and Clement, J. (1999). The effects of student-generated diagrams versus student-generated summaries on conceptual understanding of spatial, causal, and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching , 36 (1), 39-53.

Gutwill, J.P., Fredericksen, J.R., and White, B.Y. (1999). Making their own connections: Students’ understanding of multiple models in basic electricity. Cognition and Instruction , 17 (3), 249-282.

Hatano, G., and Inagaki, K. (1991). Sharing cognition through collective comprehension activity. In L.B. Resnick, J.M. Levine, and S.D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 331-348). Washington, DC: American Psychological Association.

Henderson, D., Fisher, D., and Fraser, B. (2000). Interpersonal behavior, laboratory learning environments, and student outcomes in senior biology classes. Journal of Research in Science Teaching , 37 , 26-43.

Hickey, D.T., Kindfield, A.C.H., Horwitz, P., and Christie, M.A. (2000). Integrating instruction, assessment, and evaluation in a technology-based genetics environment: The GenScope follow-up study. In B.J. Fishman and S.F. O’Connor-Divelbiss (Eds.), Proceedings of the International Conference of the Learning Sciences (pp. 6-13). Mahwah, NJ: Lawrence Erlbaum.

Hickey, D.T., Kindfield, A.C., Horwitz, P., and Christie, M.A. (2003). Integrating curriculum, instruction, assessment, and evaluation in a technology-supported genetics environment. American Educational Research Journal , 40 (2), 495-538.

Hodson, D. (1993). Philosophic stance of secondary school science teachers, curriculum experiences, and children’s understanding of science: Some preliminary findings. Interchange , 24 , 41-52.

Hofstein, A., and Lunetta, V.N. (1982). The role of the laboratory in science teaching: Neglected aspects of research. Review of Educational Research , 52 (2), 201-217.

Hofstein, A., and Lunetta, V.N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education , 88 , 28-54.

Holden, C. (1990). Animal rights activism threatens dissection. Science , 25 , 751.

Horowitz, P. (1996). Linking models to data: Hypermodels for science education. High School Journal , 79 (2), 148-156.

Horowitz, P., and Christie, M.A. (2000). Computer-based manipulatives for teaching scientific reasoning: An example. In M.J. Jacobson and R.B. Kozma (Eds.), Innovations in science and mathematics education: Advanced designs for technologies of learning (pp. 163-191). Mahwah, NJ: Lawrence Erlbaum.

Jovanovic, J., and King, S.S. (1998). Boys and girls in the performance-based science classroom: Who’s doing the performing? American Educational Research Journal , 35 (3), 477-496.

Kesidou, S., and Roseman, J. (2002). How well do middle school science programs measure up? Findings from Project 2061’s curriculum review. Journal of Research in Science Teaching , 39 (6), 522-549.

Khishfe, R., and Abd-El-Khalick, F. (2002). Influence of explicit and reflective versus implicit inquiry-oriented instruction on sixth graders’ views of nature of science. Journal of Research in Science Teaching , 39 (7), 551-578.

Klopfer, L.E. (1990). Learning scientific enquiry in the student laboratory. In E. Hegarty-Hazel (Ed.), The student laboratory and the science curriculum (pp. 95-118). London, England: Routledge.

Kozma, R.B. (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction , 13 , 205-226.

Kuhn, D., Schauble, L., and Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction , 9 (4), 285-327.

Lazarowitz, R., and Tamir, P. (1994). Research on using laboratory instruction in science. In D.L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 94-130). New York: Macmillan.

Lederman, N.G. (1992). Students’ and teachers’ conceptions of the nature of science: A review of the research. Journal of Research in Science Teaching , 29 (4), 331-359.

Lederman, N.G., Abd-El-Khalick, F., Bell, R.L., and Schwartz, R.S. (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of Research in Science Teaching , 39 (6), 497-521.

Lehrer, R., and Schauble, L. (2004). Scientific thinking and science literacy: Supporting development in learning contexts. In W. Damon, R. Lerner, K. Anne Renninger, and E. Sigel (Eds.), Handbook of child psychology, sixth edition, volume four: Child psychology in practice . Hoboken, NJ: John Wiley & Sons.

Lehrer, R., Schauble, L., Strom, D., and Pligge, M. (2001). Similarity of form and substance: Modeling material kind. In S.M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress . Mahwah, NJ: Lawrence Erlbaum.

Lemke, J. (1990). Talking science: Language, learning, and values . Norwood, NJ: Ablex.

Linn, M.C. (1997). The role of the laboratory in science learning. Elementary School Journal , 97 , 401-417.

Linn, M.C. (2004). High school science laboratories: How can technology contribute? Presentation to the Committee on High School Science Laboratories: Role and Vision. June. Available at: http://www7.nationalacademies.org/bose/June_3-4_2004_High_School_Labs_Meeting_Agenda.html [accessed April 2005].

Linn, M.C., Bell, P., and Hsi, S. (1998). Using the Internet to enhance student understanding of science: The knowledge integration environment. Interactive Learning Environments , 6 (1-2), 4-38.

Linn, M.C., Davis, E., and Bell, P. (2004a). Inquiry and technology. In M.C. Linn, E. Davis, and P. Bell, (Eds.), Internet environments for science education . Mahwah, NJ: Lawrence Erlbaum.

Linn, M.C., Davis, E., and Bell, P. (Eds.). (2004b). Internet environments for science education . Mahwah, NJ: Lawrence Erlbaum.

Linn, M.C., and Hsi, S. (2000). Computers, teachers, peers . Mahwah, NJ: Lawrence Erlbaum.

Linn, M.C., and Songer, B. (1991). Teaching thermodynamics to middle school children: What are appropriate cognitive demands? Journal of Research in Science Teaching , 28 (10), 885-918.

Lunetta, V.N. (1998). The school science laboratory. In B.J. Fraser and K.G. Tobin (Eds.), International handbook of science education (pp. 249-262). London, England: Kluwer Academic.

Lynch, S. (2004). What are the effects of highly rated, lab-based curriculum materials on diverse learners? Presentation to the Committee on High School Science Laboratories: Role and Vision. July 12. Available at: http://www7.nationalacademies.org/bose/July_12-13_2004_High_School_Labs_Meeting_Agenda.html [accessed Oct. 2004].

Lynch, S., Kuipers, J., Pyke, C., and Szesze, M. (In press). Examining the effects of a highly rated science curriculum unitinstructional unit on diverse populations: Results from a planning grant. Journal of Research in Science Teaching .

Lynch, S., and O’Donnell, C. (2005). The evolving definition, measurement, and conceptualization of fidelity of implementation in scale-up of highly rated science curriculum unitsintegrated instructional units in diverse middle schools . Paper presented at the annual meeting of the American Educational Research Association, April 7, Montreal, Canada.

McRobbie, C.J., and Fraser, B.J. (1993). Associations between student outcomes and psychosocial science environment. Journal of Educational Research , 87 , 78-85.

Meichtry, Y.J. (1993). The impact of science curricula on student views about the nature of science. Journal of Research in Science Teaching , 30 (5), 429-443.

Metz, K.E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction , 22 (2), 219-290.

Meyer, K., and Woodruff, E. (1997). Consensually driven explanation in science teaching. Science Education , 80 , 173-192.

Millar, R. (1998). Rhetoric and reality: What practical work in science education is really for. In J. Wellington (Ed.), Practical work in school science: Which way now? (pp. 16-31). London, England: Routledge.

Millar, R. (2004). The role of practical work in the teaching and learning of science . Paper prepared for the Committee on High School Science Laboratories: Role and Vision. Available at: http://www7.nationalacademies.org/bose/June3-4_2004_High_School_Labs_Meeting_Agenda.html [accessed April 2005].

National Research Council. (1999). How people learn: Brain, mind, experience, and school . Committee on Developments in the Science of Learning, J.D. Bransford, A.L. Brown, and R.R. Cocking (Eds.). Washington, DC: National Academy Press.

National Research Council. (2001). Eager to learn: Educating our preschoolers . Committee on Early Childhood Pedagogy. B.T. Bowman, M.S. Donovan, and M.S. Burns (Eds.). Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council. (2005). Systems for state science assessment . Committee on Test Design for K-12 Science Achievement, M.R. Wilson and M.W. Bertenthal (Eds.). Board on Testing and Assessment, Center for Education. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Osborne, R., and Freyberg, P. (1985). Learning in science: The implications of children’s science . London, England: Heinemann.

Partnership for 21st Century Skills. (2003). Learning for the 21st century . Washington, DC: Author. Available at: http://www.21stcenturyskills.org/reports/learning.asp [accessed April 2005].

Pea, R., Mills, M., and Takeuchi, L. (Eds). (2004). Making SENS: Science education networks of sensors . Report from an OMRON-sponsored workshop of the Media-X Program at Stanford University, October 3. Stanford, CA: Stanford Center for Innovations in Learning. Available at:: http://www.makingsens.stanford.edu/index.html [accessed May 2005].

Raghubir, K.P. (1979). The laboratory investigative approach to science instruction. Journal of Research in Science Teaching , 16 , 13-18.

Reif, F., and St. John, M. (1979) Teaching physicists thinking skills in the laboratory. American Journal of Physics , 47 (11), 950-957.

Reiner, M., Pea, R.D., and Shulman, D.J. (1995). Impact of simulator-based instruction on diagramming in geometrical optics by introductory physics students. Journal of Science Education and Technology , 4 (3), 199-225.

Reiser, B.J., Tabak, I., Sandoval, W.A., Smith, B.K., Steinmuller, F., and Leone, A.J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S.M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263-305). Mahwah, NJ: Lawrence Erlbaum.

Renner, J.W., Abraham, M.R., and Birnie, H.H. (1985). Secondary school students’ beliefs about the physics laboratory, Science Education , 69 , 649-63.

Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. Journal of the Learning Sciences , 2 (3), 235-276.

Roschelle, J., Kaput, J., and Stroup, W. (2000). SimCalc: Accelerating students’ engagement with the mathematics of change. In M.J. Jacobsen and R.B. Kozma (Eds). Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 47-75). Hillsdale, NJ: Lawrence Erlbaum.

Rosebery, A.S., Warren, B., and Conant, F.R. (1992). Appropriating scientific discourse: Findings from language minority classrooms. Journal of the Learning Sciences , 2 (1), 61-94.

Salomon, G. (1996). Studying novel learning environments as patterns of change. In S. Vosniadou, E. De Corte, R. Glaser, and H. Mandl (Eds.), International perspectives on the design of technology-supported learning environments (pp. 363-377). Mahwah, NJ: Lawrence Erlbaum.

Sandoval, W.A. (2003). Conceptual and epistemic aspects of students’ scientific explanations. Journal of the Learning Sciences , 12 (1), 5-51.

Sandoval, W.A., and Millwood, K.A. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction , 23 (1), 23-55.

Sandoval, W.A., and Morrison, K. (2003). High school students’ ideas about theories and theory change after a biological inquiry unit. Journal of Research in Science Teaching , 40 (4), 369-392.

Sandoval, W.A., and Reiser, B.J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic supports for science inquiry. Science Education , 88 , 345-372.

Schauble, L., Glaser, R., Duschl, R.A., Schulze, S., and John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. Journal of the Learning Sciences , 4 (2), 131-166.

Schauble, L., Klopfer, L.E., and Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching , 28 (9), 859-882.

Shaffer, P.S., and McDermott, L.C. (1992). Research as a guide for curriculum development: An example from introductory electricity. Part II: Design of instructional strategies. American Journal of Physics , 60 (11), 1003-1013.

Shepardson, D.P., and Pizzini, E.L. (1993). A comparison of student perceptions of science activities within three instructional approaches. School Science and Mathematics , 93 , 127-131.

Shulman, L.S., and Tamir, P. (1973). Research on teaching in the natural sciences. In R.M.W. Travers (Ed.), Second handbook of research on teaching . Chicago: Rand-McNally.

Singer, R.N. (1977). To err or not to err: A question for the instruction of psychomotor skills. Review of Educational Research , 47 , 479-489.

Smith, C.L., Maclin, D., Grosslight, L., and Davis, H. (1997). Teaching for understanding: A study of students’ pre-instruction theories of matter and a comparison of the effectiveness of two approaches to teaching about matter and density. Cognition and Instruction , 15 , 317-394.

Smith, C.L., Maclin, D., Houghton, C., and Hennessey, M. (2000). Sixth-grade students’ epitemologies of science: The impact of school science experiences on epitemological development. Cognition and Instruction , 18 , 349-422.

Snir, J., Smith, C.L., and Raz, G. (2003). Linking phenomena with competing underlying models: A software tool for introducing students to the particulate model of matter. Science Education , 87 (6), 794-830.

Songer, N.B., and Linn, M.C. (1991). How do students’ views of science influence knowledge integration? Journal of Research in Science Teaching , 28 (9), 761-784.

Tabak, I. (2004). Synergy: a complement to emerging patterns of distributed scaffolding. Journal of the Learning Sciences , 13 (3), 305-335.

Tasker, R. (1981). Children’s views and classroom experiences. Australian Science Teachers’ Journal , 27 , 33-37.

Tiberghien, A., Veillard, L., Le Marechal, J.-F., Buty, C., and Millar, R. (2000). An analysis of labwork tasks used in science teaching at upper secondary school and university levels in several European countries. Science Education , 85 , 483-508.

Tobin, K. (1987). Forces which shape the implemented curriculum in high school science and mathematics. Teaching and Teacher Education , 3 (4), 287-298.

VandenBerg, E., Katu, N., and Lunetta, V.N. (1994). The role of “experiments” in conceptual change . Paper presented at the annual meeting of the National Association for Research in Science Teaching, Anaheim, CA.

Webb, N.M., Nemer, K.M., Chizhik, A.W., and Sugrue, B. (1998). Equity issues in collaborative group assessment: Group composition and performance. American Educational Research Journal , 35 (4), 607-652.

Webb, N.M., and Palincsar, A.S. (1996). Group processes in the classroom. In D.C. Berliner and R.C. Calfee (Eds.), Handbook of educational psychology (pp. 841-873). New York: Macmillan.

Wells, M., Hestenes, D., and Swackhamer, G. (1995). A modeling method for high school physics instruction. American Journal of Physics , 63 (7), 606-619.

Wheatley, J.H. (1975).Evaluating cognitive learning in the college science laboratory. Journal of Research in Science Teaching , 12 , 101-109.

White, B.Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction , 10 (1), 1-100.

White, B.Y., and Frederiksen, J.R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction , 16 (1), 3-118.

White, R.T. (1996). The link between the laboratory and learning. International Journal of Science Education , 18 , 761-774.

White, R.T., and Gunstone, R.F. (1992). Probing understanding . London, England: Falmer.

Wilkenson, J.W., and Ward, M. (1997). The purpose and perceived effectiveness of laboratory work in secondary schools. Australian Science Teachers’ Journal , 43-55.

Wong, A.F.L., and Fraser, B.J. (1995). Cross-validation in Singapore of the science laboratory environment inventory. Psychological Reports , 76 , 907-911.

Woolnough, B.E. (1983). Exercises, investigations and experiences. Physics Education , 18 , 60-63.

Yager, R.E., Engen, J.B., and Snider, C.F. (1969). Effects of the laboratory and demonstration method upon the outcomes of instruction in secondary biology. Journal of Research in Science Teaching , 5 , 76-86.

Zimmerman, C. (2000). The development of scientific reasoning skills. Developmental Review , 20 , 99-149.

Laboratory experiences as a part of most U.S. high school science curricula have been taken for granted for decades, but they have rarely been carefully examined. What do they contribute to science learning? What can they contribute to science learning? What is the current status of labs in our nation's high schools as a context for learning science? This book looks at a range of questions about how laboratory experiences fit into U.S. high schools:

• What is effective laboratory teaching?
• What does research tell us about learning in high school science labs?
• How should student learning in laboratory experiences be assessed?
• Do all student have access to laboratory experiences?
• What changes need to be made to improve laboratory experiences for high school students?
• How can school organization contribute to effective laboratory teaching?

With increased attention to the U.S. education system and student outcomes, no part of the high school curriculum should escape scrutiny. This timely book investigates factors that influence a high school laboratory experience, looking closely at what currently takes place and what the goals of those experiences are and should be. Science educators, school administrators, policy makers, and parents will all benefit from a better understanding of the need for laboratory experiences to be an integral part of the science curriculum—and how that can be accomplished.

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Laboratory Experiment

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Laboratory experiment refers to the psychological experiment conducted in a laboratory setting. In the laboratory experiment, the causal relationship between variables is explored through strict control of experimental conditions and study variables. It serves as one of the important methods of psychological research. Before the middle of the nineteenth century, the methods of observing nature and summarizing one’s own experience were mainly used in psychological research. Ernst Heinrich Weber determined the two-point threshold and the weight difference threshold in 1834, Gustav Theodor Fechner founded the psychophysical methods in 1860, and William James established the psychology laboratory for demonstration in the United States in 1875, both of which were actually early laboratory experiments in psychological research. Since Wilhelm Wundt has established the laboratory especially for psychological research in 1879 at Leipzig University in Germany, and Wundt himself has been...

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Kantowitz BH, Roediger HL, Elmes DG (2015) Experimental psychology, 10th edn. Cengage Learning, Boston

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Zhang X-M, Hua S (2014) Experimental psychology. Beijing Normal University Publishing Group, Beijing

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130 Laboratory Apparatus And Their Uses (With Pictures)

A laboratory is a special room or place that is equipped to facilitate scientific experiments, observations and for teaching science. Laboratory apparatus refers to the various tools, equipment, and instruments used in scientific research, experimentation, and analysis within a laboratory setting. These tools are essential for conducting experiments, measuring and analyzing data, and ensuring the accuracy and reliability of scientific results.

Some of the laboratory apparatus are used as a source of heat, for safety, for making observations and for measurement of variables such as voltage, temperature, volume, time and mass.

There are apparatus that are used in general laboratory experiments while others serve specific in experiments. They are also made from materials that are resistant to chemical reactions and corrosion. Common materials include glass, stainless steel, and various types of plastics.

It is important to note that most of the apparatus that are used as containers or reaction vessels are made of transparent glass or plastic and may come in different sizes. Let us talk about Laboratory apparatus in three categories: Basic Apparatus, Safety Apparatus , General Apparatus and Specialized Apparatus

Here is a list of 130 laboratory apparatus / Equipment

General equipment/apparatus that are found in almost all laboratories:

• Alcohol burner
• Bunsen burner
• Burette clamp
• Buchner funnel
• Balance scale
• Conical or titration flask
• Crucible tong
• china dish (Evaporating Dish)
• Crucible with cover
• Clay Triangles
• Dry-cell battery
• Dissecting set
• Erlenmeyer flask
• Flat bottomed flask
• Filter paper
• Friability tester
• Glass funnel
• Glass tubing
• Litmus paper
• Measuring cylinders

Mortar and pestle

• Measuring flasks
• No of weights
• Petri dishes
• Rubber stopper
• Reagent bottle
• Rubber tubing
• Stirring rod
• Separatory funnel
• Stethoscope
• Speedometer
• Test tube rack
• Tripod stand
• Test tube holder
• Test tube stand
• Test tube brush
• Tuning fork

Thermometer

• Wash bottle
• Watch glass

Others Laboratory Apparatus or Equipment

• Analytical balance
• Atomic absorption spectrometer
• BOD incubator
• Chromatography column
• Cryogenic freezer
• Colorimeter
• Conductivity meter
• Dewar flask
• Distillation apparatus
• Electrophoresis chamber
• Flame photometer
• Gas chromatograph
• Geiger-Muller counter
• Inoculating loop
• Inverted microscope
• Kjeldahl apparatus
• Laboratory oven
• Laboratory refrigerator
• Laser spectrometer
• Magnetic stirrer
• Mass spectrometer

Microcentrifuge

• NMR spectrometer
• Orbital shaker
• Oscilloscope
• Particle counter
• PCR machine (Polymerase Chain Reaction)
• Peristaltic pump
• pH electrode
• Pipette filler
• Polarimeter
• Refractometer
• Rotary evaporator

Spectrophotometer

• Syringe filter

Ultracentrifuge

• UV-Vis spectrophotometer
• Vortex mixer
• X-ray diffraction machine
• YSI meter (for measuring dissolved oxygen)
• Gas syringe
• Melting point apparatus
• Infrared spectrometer
• Particle size analyzer
• Bacterial incubator
• Thermal cycler (PCR machine)
• Gas manifold
• Conductivity cell
• Reflux condenser
• Freeze dryer
• Inert gas chamber
• Ultrasonic cleaner
• Atomic force microscope (AFM)
• Gas generator
• Digital pH meter
• Atomic emission spectrometer
• Magnetic balance
• Tensiometer
• Ultraviolet lamp
• Inoculation needle
• Rotary shaker
• Autotitrator
• Freeze-thaw chamber
• Gel documentation system
• Pipette tips
• Rotary vane pump
• Vacuum desiccator
• Gas chromatography-mass spectrometry (GC-MS)
• High-performance liquid chromatography (HPLC) system
• Inverted fluorescence microscope

Basic Laboratory Apparatus

Bunsen Burner

This is a piece of apparatus that is used as a safe source of heat in laboratories using a single gas flame. A Bunsen has an inlet that is usually connected to an external source of laboratory gas by rubber tubing. Its flame is used not only for heating, but for combustion and sterilizing objects too.

This is an apparatus that is used to give finer details of small objects that would otherwise not be seen by the naked eye or a hand lens. It does so by magnifying objects up to thousands times their original size. There exist two main variants of a microscope namely; a light microscope and an electron microscope

Weighing Balances

These are used to weigh the mass of substances in a laboratory. There are different types of weigh balances such as beam balance, spring balance, top pan balances and electronic balances.

Watches and clocks

These are apparatus for measuring time. Stop watches and stop clocks are the most commonly used for accurately measuring time during experiments.

When it comes to measuring the voltage between any two points, nothing does the job better than a voltmeter. It is normally connected in parallel with a device so as to measure its voltage.

Beakers serve a wide range of purposes. Calibrated beakers are used to measure approximate volumes of liquids, holding both liquids and solids and heating them when necessary. In addition to that, beakers may be used for stirring and mixing different substances in a laboratory.

Volumetric Flask

Volumetric flasks come in handy when fairly accurate and precise volumes of liquids are required. They can as well be used for dilution when preparing standard solutions.

This is an apparatus that is used for adding fairly accurate volumes of liquids up to nearly 0.01ml especially during titrations. It is fitted with an adjustable stopcock that regulates the amount of liquid that is released at a time.

A pipette (sometimes spelled pipet ) is a laboratory tool commonly used in chemistry, biology and medicine to transport a measured volume of liquid, often as a media dispenser. Pipettes come in several designs for various purposes with differing levels of accuracy and precision, from single piece glass pipettes to more complex adjustable or electronic pipettes.

A thermometer is used to measure the degree of hotness or coldness of a substance. They come in different types such as maximum and minimum thermometer, clinical thermometer and general purpose thermometers.

Flat-bottomed Flask

It is used for general laboratory experiments. A flat-bottomed flask can be used to collect, measure and hold liquids. They may as well be used for heating substances and mixing solutions in a laboratory.

Filter Funnel

Filter funnels are used for delivering different amounts of liquids carefully into holding apparatus. It can also be used together with a filter paper to separate finer solid substances from liquids. They vary in sizes and material from which they are built from depending on the purpose for which they are needed.

A desiccator is a sealable storage unit used for drying or keeping moisture sensitive substances free from moisture. There are two main types of desiccators that are made from polycarbonate or polypropylene material. These are; vacuum desiccators and non-vacuum desiccators.

Reagent Bottle

Reagent bottle or media bottle refers to containers used for storing and sampling both liquid and solid bench reagents in a variety of laboratory experiments. Most reagent bottles are made of glass or plastic.

A spatula is a broad, flat, hand-held blade apparatus that is used for spreading, mixing and scooping solid substances. The do come in various shapes and sizes.

Dropping funnel

This is an apparatus that is used to add controlled amounts of liquids into reaction vessels more so when the reaction is expected to be too vigorous if large amounts of the reagent are used at a go.

These apparatus are used to prepare solid reagents into a paste or powder by grinding, crushing or pounding them. They are mostly made of metal, wood, nonporous marble and granite material.

Test-tube is a tubular apparatus that is used for general laboratory experiments. They may be used to hold and compare chemical substances. In addition to that, test-tubes can be used to mix liquid substances and heating small chemical samples.

This is a heat resistant apparatus used when heating solid substances under high temperatures. It is commonly made of porcelain as it is resistant to heat when strongly heating solid substances.

Safety Apparatus

It is essential for any laboratory to have a wide range of safety equipment at its disposal. They are intended to keep laboratory users and their working environment safe from injuries, corrosive chemicals, poisonous fumes or accidental fires while carrying out experiments. The list of protective gear ranges from:

Safety Goggles

• Purpose : Safety goggles provide eye protection by shielding the eyes from chemical splashes, flying debris, and hazardous fumes or liquids.
• Usage : They are essential in laboratories, workshops, and industrial environments where eye hazards are present. Goggles should fit snugly to prevent entry of harmful substances.

Disposable Coveralls and Aprons

• Purpose : Disposable coveralls and aprons are protective garments that shield the body and clothing from chemical spills, contaminants, or biohazards.
• Usage : Workers wear these items to prevent exposure to hazardous substances, ensuring both personal safety and contamination control.

Disposable Latex Gloves

• Purpose : Disposable latex gloves are worn to protect the hands from contact with chemicals, biological materials, and contaminants.
• Usage : These gloves are common in laboratories, healthcare settings, and industries where hand protection is essential. They reduce the risk of skin contact and contamination.

Plastic Bags

• Purpose : Plastic bags are used for containing and disposing of hazardous waste materials, contaminated items, or biohazards.
• Usage : In laboratories and medical facilities, plastic bags are crucial for safe disposal of waste materials and maintaining cleanliness.
• Purpose : Gas masks protect the respiratory system by filtering out harmful gases, fumes, and particulates from the air.
• Usage : Gas masks are used in environments where there is a risk of exposure to toxic or hazardous airborne substances, such as during chemical spills or in industrial settings.

Fire Blanket or Extinguisher

• Purpose : Fire blankets and extinguishers are used to suppress fires in emergency situations.
• Usage : In the event of a small fire, fire blankets can be used to smother flames. Fire extinguishers are designed to spray fire-suppressing agents to extinguish fires safely.

First Aid Kits

• Purpose : First aid kits contain essential medical supplies and equipment to provide immediate medical assistance in case of injuries or accidents.
• Usage : First aid kits are located in workplaces, laboratories, and public areas to address injuries, burns, cuts, and other medical emergencies.

Plumbed Eyewash Units

• Purpose : Plumbed eyewash units provide a continuous flow of water to rinse and flush the eyes in case of chemical exposure.
• Usage : Eyewash stations are installed in laboratories and workplaces where hazardous chemicals are handled, ensuring prompt eye irrigation in case of accidents.

Flammable Safe

• Purpose : A flammable safe is designed to store flammable liquids and materials safely, preventing ignition or explosions.
• Usage : These safes are essential for fire safety in laboratories, where flammable substances are often used or stored.

Chemical Spill Kits

• Purpose : Chemical spill kits contain materials and equipment for responding to chemical spills, containing and neutralizing the spill, and protecting personnel.
• Usage : In laboratory environments, chemical spill kits are crucial to mitigate the effects of accidental chemical spills, preventing harm and environmental damage.

Plastic Dust Pan and Scoop

• Purpose : Plastic dust pans and scoops are used to collect and safely dispose of solid chemical spills, dust, or debris.
• Usage : They are essential tools for cleaning up laboratory or industrial workspaces, ensuring the safe removal of potentially hazardous materials.

General Laboratory Apparatus

• Purpose : Microscopes are used to magnify and visualize objects or specimens that are too small to be seen with the naked eye. They are essential tools in fields such as biology, microbiology, and materials science.
• Components : A typical microscope consists of an eyepiece, objective lenses with varying magnification powers, a stage for holding the sample, and a light source for illumination.
• Usage : Researchers place a sample on the stage, adjust the focus using the fine and coarse adjustment knobs, and select the appropriate objective lens for the desired magnification.
• Purpose : Bunsen burners are used for heating, sterilizing, and flame-related experiments in the laboratory. They provide a consistent open flame.
• Components : A Bunsen burner has a gas inlet, an adjustable air vent, and a flame nozzle.
• Usage : The flame intensity and type (oxidizing or reducing) can be adjusted by controlling the air mixture. Bunsen burners are commonly used in chemistry for tasks like heating solutions and sterilizing equipment.
• Purpose : Beakers are used for holding, mixing, and heating liquids. They come in various sizes and are a staple in laboratories for general-purpose tasks.
• Features : Beakers typically have volume markings, a spout for pouring, and a flat bottom.
• Usage : Beakers are versatile containers, but they are not designed for precise measurements. They are often used for mixing solutions, conducting simple reactions, or as a vessel for holding liquids during experiments.

Erlenmeyer Flask

• Purpose : Erlenmeyer flasks are conical-shaped containers with narrow necks. They are used for mixing, heating, and storing liquids, particularly when you need to prevent splashes and evaporation.
• Features : Erlenmeyer flasks have volume markings and can be fitted with stoppers or caps.
• Usage : They are commonly used for titration, as reaction vessels for chemical reactions, or as containers for cultures in microbiology.
• Purpose : Test tubes are small, cylindrical containers used for holding, heating, or mixing small quantities of liquids or solids.
• Features : They come in various sizes, and some have screw caps or stoppers.
• Usage : Test tubes are versatile and widely used in chemical and biological experiments, such as holding reagents, conducting small-scale reactions, or culturing microorganisms.

Graduated Cylinder

• Purpose : Graduated cylinders are used to accurately measure the volume of liquids. They have volume markings for precise measurements.
• Features : They have a narrow, graduated scale and a spout for pouring.
• Usage : Graduated cylinders are essential for preparing solutions with precise volumes and measuring liquids accurately.
• Purpose : Pipettes are used for precise measurement and transfer of small volumes of liquid. They come in various types, including micropipettes for ultra-precise measurements.
• Features : Pipettes have a calibrated scale for volume selection, and some are disposable while others are reusable and require calibration.
• Usage : Pipettes are commonly used in biology, chemistry, and analytical chemistry for tasks like transferring samples, making dilutions, and preparing standards.
• Purpose : Burets are used for precise titrations in analytical chemistry. They allow for controlled dispensing of a titrant into a solution.
• Features : Burets are long, graduated tubes with a stopcock at the bottom for controlling the flow of liquid.
• Usage : Burets are essential in titration experiments where the volume of titrant needed to reach a specific endpoint is critical.

Florence Flask

• Purpose : Florence flasks are used for boiling and heating liquids. They have a round bottom that allows for even heating.
• Features : They typically have a long neck and are often used with a rubber stopper or glass tubing for attaching other equipment.
• Usage : Florence flasks are commonly used in distillation setups and refluxing reactions.
• Purpose : Volumetric flasks are used for preparing solutions with precise volumes. They come in various sizes and are designed to hold a specific volume when filled to the calibration mark.
• Features : Volumetric flasks have a long neck with a single calibration mark on the neck.
• Usage : They are crucial for preparing accurate and known concentrations of solutions, such as standards used in chemical analysis.
• Purpose : Funnels are used for transferring liquids or fine-grained substances from one container to another. They help avoid spills and maintain accuracy.
• Features : Funnels have a wide, tapered opening at the top and a narrow spout at the bottom.
• Usage : Funnels are essential for tasks like filtering solutions, adding reagents to containers, and filling smaller vessels without spillage.
• Purpose : Crucibles are heat-resistant containers used for heating substances to high temperatures. They are typically made of porcelain or ceramic materials.
• Features : They have a small, cylindrical shape and come with lids.
• Usage : Crucibles are commonly used for processes such as heating samples to dryness, ashing organic materials, and performing high-temperature reactions.
• Purpose : Tongs are used for safely handling hot glassware and objects in the laboratory.
• Features : They have long, pincer-like arms with insulated handles.
• Usage : Tongs are essential for gripping and moving hot crucibles, beakers, flasks, and other equipment without direct contact.

Evaporating Dish

• Purpose : Evaporating dishes are shallow, flat-bottomed containers used for evaporating solvents from solutions.
• Features : They are typically made of porcelain or borosilicate glass and are resistant to high temperatures.
• Usage : Evaporating dishes are used to concentrate solutions by gently heating them to drive off the solvent, leaving behind the solute.
• Purpose : Desiccators are sealed containers used to store substances in a dry environment, protecting them from moisture.
• Features : They have an airtight seal and often contain a drying agent like silica gel or calcium chloride.
• Usage : Desiccators are used for storing moisture-sensitive materials, such as hygroscopic chemicals or humidity-sensitive samples.
• Purpose : Centrifuges are used for separating components of a liquid or mixture based on density by spinning them at high speeds.
• Features : They have a rotor that holds sample tubes and can generate centrifugal forces.
• Usage : Centrifuges are used in various fields, including biology, chemistry, and clinical laboratories, for tasks like separating cells, proteins, and particles from liquids.
• Purpose : A hot plate is an electric heating device used to heat glassware or other containers, usually with a flat, heated surface.
• Usage : Hot plates are commonly used for tasks such as boiling water, heating solutions, or conducting reactions that require controlled and consistent temperature.

Magnetic Stirrer

• Purpose : Magnetic stirrers use a rotating magnetic field to create a vortex in a liquid, which stirs or mixes the contents of a container without the need for a physical stirring rod.
• Usage : They are used for even and continuous mixing of solutions, particularly in chemistry and biology experiments.
• Purpose : A pH meter measures the acidity or alkalinity (pH) of a solution. It provides a numerical pH value based on the concentration of hydrogen ions in the solution.
• Usage : pH meters are vital in various fields, including chemistry, biology, and environmental science, for accurately determining pH levels in solutions.
• Purpose : A spectrophotometer measures the absorption or transmission of light by a substance across a range of wavelengths. It is used for quantitative analysis of substances in a solution.
• Usage : Spectrophotometers are essential for applications like quantifying the concentration of a solute, identifying compounds, and studying chemical reactions.
• Purpose : Autoclaves are pressurized and high-temperature chambers used to sterilize equipment, media, and samples in a laboratory.
• Usage : Autoclaves are crucial for maintaining sterile conditions in microbiology, biotechnology, and medical laboratories.
• Purpose : Incubators provide a controlled environment with regulated temperature and humidity for the growth of microorganisms or the incubation of biological samples.
• Usage : They are essential for cell culture, microbial culturing, and other biological research applications.

Refrigerator/Freezer

• Purpose : Laboratory refrigerators and freezers are used to store temperature-sensitive reagents, samples, and biological materials at controlled temperatures.
• Usage : They are crucial for preserving the integrity and stability of materials, such as enzymes, vaccines, and DNA.
• Purpose : A microcentrifuge is a high-speed centrifuge designed to spin small volumes of liquid at very high speeds, separating components based on density.
• Usage : They are used for tasks such as pelleting cells or particles, separating DNA, and isolating proteins.

Gel Electrophoresis Apparatus

• Purpose : Gel electrophoresis apparatus is used to separate and analyze DNA, RNA, or proteins based on their size and charge.
• Usage : It is a fundamental tool in molecular biology for tasks like DNA fingerprinting, DNA fragment separation, and protein analysis.

PCR Machine (Polymerase Chain Reaction)

• Purpose : A PCR machine amplifies specific DNA sequences through repeated cycles of heating and cooling.
• Usage : PCR machines are vital in molecular biology for DNA amplification, genetic testing, and DNA sequencing.

Spectrofluorometer

• Purpose : A spectrofluorometer measures the fluorescence emission spectra of substances when excited by light of a specific wavelength.
• Components : It typically includes a light source, monochromator, sample holder, and photodetector.
• Usage : Spectrofluorometers are used to study the fluorescence properties of compounds, such as fluorescent dyes, proteins, and biomolecules, in chemical and biological research. They are crucial for characterizing fluorescent materials and quantifying their concentrations.

Distillation Apparatus :

• Purpose : Distillation apparatus is used to separate components of a liquid mixture based on their different boiling points.
• Components : It comprises a boiling flask, distillation head, condenser, receiver flask, and a heat source.
• Usage : Distillation is a common technique for purifying or separating liquids in chemistry, including the production of distilled water or the isolation of pure chemicals.

Condenser :

• Purpose : A condenser cools and condenses vaporized substances back into a liquid state, typically in distillation setups.
• Components : It includes a coiled or straight glass tube through which cooling water circulates.
• Usage : Condensers are essential components in distillation and reflux processes, allowing the collection of purified liquids.
• Purpose : A spatula is a small, flat utensil used for transferring solid chemicals or powders.
• Materials : Spatulas are typically made of stainless steel, plastic, or glass.
• Usage : Spatulas are commonly used to weigh or transfer small quantities of solids in chemistry and analytical work. They come in various shapes and sizes to suit different applications.

Pipette Bulb :

• Purpose : A pipette bulb is a rubber bulb that attaches to a pipette for creating suction and facilitating liquid transfer.
• Usage : Pipette bulbs are used to draw liquids into pipettes accurately. They provide a manual means of controlling the volume of liquid aspirated and dispensed.

Buchner Funnel :

• Purpose : A Buchner funnel is used in vacuum filtration to separate solids from liquids. It contains a perforated plate and a vacuum source to pull liquid through.
• Components : It includes a funnel with a flat, porous base and a conical flask or vacuum flask below it.
• Usage : Buchner funnels are commonly used for isolating precipitates or collecting solid residues from liquid suspensions. Vacuum filtration speeds up the process.

Mortar and Pestle :

• Purpose : A mortar and pestle are tools used for grinding, crushing, and mixing solid materials into fine powders or pastes.
• Materials : Mortars are typically made of ceramic, glass, or stone, while the pestle is a heavy rod.
• Usage : They are widely used in chemistry and biology for tasks such as sample preparation, grinding chemicals, or creating homogenous mixtures.

Stirring Rod :

• Purpose : A stirring rod is a long, thin glass or plastic rod used for manually stirring liquids or suspensions.
• Usage : Stirring rods are commonly used for mixing solutions, ensuring homogeneity in reactions, and transferring small quantities of liquid.

Thermometer :

• Purpose : A thermometer measures temperature. Laboratory thermometers are designed for accuracy and precision.
• Types : There are various types of thermometers, including mercury-in-glass, digital, and infrared.
• Usage : Thermometers are used in various applications, from monitoring reaction temperatures to maintaining controlled conditions in incubators and ovens.

Melting Point Apparatus

• Purpose : A melting point apparatus is used to determine the melting point of a solid substance, which is a characteristic property.
• Components : It includes a heating block, sample holder, and a magnifying lens.
• Usage : It is employed in chemistry for identifying and verifying the purity of organic compounds by comparing their melting points to known standards.
• Purpose : A Petri dish is a shallow, flat, cylindrical container with a lid, used for culturing and observing microorganisms and small specimens.
• Materials : Petri dishes are typically made of glass or clear plastic.
• Usage : Petri dishes are widely used in microbiology for bacterial and fungal cultures and in various biological experiments, including bacterial plate counts and tissue culture.

Separatory Funnel

• Purpose : A separatory funnel is used to separate immiscible liquids or liquids with different densities.
• Components : It has a conical shape with a stopcock at the bottom for controlled liquid drainage.
• Usage : Separatory funnels are commonly used in chemistry for processes like liquid-liquid extraction, purification, and phase separations.

Gas Burette

• Purpose : A gas burette is a graduated glass tube used to measure the volume of gases in chemical experiments.
• Usage : It is employed in experiments where precise gas volume measurements are necessary, such as in gas collection or stoichiometry experiments.

Hemocytometer

• Purpose : A hemocytometer is a special counting chamber used for manually counting blood cells and other small particles under a microscope.
• Components : It consists of a thick glass slide with a grid etched on it and a coverslip.
• Usage : Hemocytometers are essential in clinical laboratories and research for accurate cell counting in applications like blood cell analysis and cell culture.

Vortex Mixer :

• Purpose : A vortex mixer is a high-speed mixer that creates a vortex in a liquid sample to mix its contents.
• Components : It has a motorized base with a rubber cup or platform for holding sample tubes.
• Usage : Vortex mixers are used to quickly and thoroughly mix liquids, suspensions, and small samples in test tubes or microcentrifuge tubes.

Ultrasonic Cleaner

• Purpose : An ultrasonic cleaner uses high-frequency sound waves to remove contaminants from objects immersed in a liquid.
• Components : It consists of a tank filled with cleaning solution, ultrasonic transducers, and a timer.
• Usage : Ultrasonic cleaners are commonly used to clean laboratory glassware, small parts, and delicate instruments, ensuring thorough cleaning without manual scrubbing.

TLC Plate (Thin-Layer Chromatography Plate)

• Purpose : TLC is a chromatography technique used to separate and analyze mixtures. A TLC plate is a flat, thin sheet coated with a stationary phase for this purpose.
• Components : The plate is typically made of glass or plastic with a thin layer of absorbent material (such as silica gel) as the stationary phase.
• Usage : Researchers spot or apply a sample mixture at the base of the plate, which is then placed in a solvent chamber. As the solvent rises through capillary action, it carries the components of the mixture, allowing for separation based on their interactions with the stationary phase.

Rotary Evaporator

• Purpose : A rotary evaporator is used for the gentle and efficient removal of solvents from liquid mixtures, typically in chemical synthesis or sample preparation.
• Components : It consists of a rotating flask, a water bath or heating bath, a vacuum system, and a condenser.
• Usage : The sample is placed in the rotating flask and heated under vacuum. The reduced pressure lowers the boiling point of the solvent, facilitating its removal. The condenser then collects the vapor, which condenses back into a liquid.
• Purpose : A viscometer measures the viscosity of a fluid, which is a measure of its resistance to flow.
• Types : There are various types of viscometers, including capillary viscometers, rotational viscometers, and falling ball viscometers.
• Usage : Viscometers are used in industries like pharmaceuticals, food, and oil to determine fluid properties and quality control. They are also employed in research to study the flow behavior of fluids.
• Purpose : A hydrometer is an instrument used to measure the specific gravity (density) of a liquid.
• Components : It typically consists of a graduated glass tube with a weighted bulb at the bottom.
• Usage : Hydrometers are commonly used in various applications, such as in breweries to measure the alcohol content of beer, in laboratories for density measurements, and in the petroleum industry for testing fuel quality.

Microtome :

• Purpose : A microtome is a precision instrument used to cut thin slices (sections) of biological or material samples for microscopy or analysis.
• Types : There are different types of microtomes, including rotary microtomes, cryostats, and ultramicrotomes.
• Usage : Microtomes are vital in histology, biology, and material science for preparing samples for examination under microscopes or other analytical instruments.

Autotitrator (Automatic Titrator)

• Purpose : An autotitrator is an automated titration instrument used for precise and efficient chemical analysis, especially in determining the concentration of analytes in a solution.
• Components : It consists of a burette, a titration vessel, a pH electrode, and automated control systems.
• Usage : Autotitrators perform titrations accurately and with reduced human error. They are widely used in analytical chemistry, quality control, and environmental monitoring.

Gas Syringe

• Purpose : A gas syringe is a device used to measure and transfer known volumes of gases in laboratory experiments.
• Components : It typically consists of a cylindrical glass tube with a plunger or piston.
• Usage : Gas syringes are used in experiments where precise gas volumes are required, such as in gas collection, gas stoichiometry, and determining gas properties like molar mass or density.

Specialized Laboratory Apparatus/Equipment

Nuclear Magnetic Resonance (NMR) Spectrometer :

• Purpose : An NMR spectrometer is used for the analysis of organic compounds’ structure and properties. It measures the nuclear magnetic resonance of atomic nuclei.
• Components : It consists of a powerful magnet, radiofrequency (RF) transmitter and receiver, and a sample holder.
• Usage : Researchers place a sample in the magnet, which aligns the nuclei with the magnetic field. RF pulses are applied, and the resulting signals provide information about the chemical environment and connectivity of atoms in the sample.

Scanning Electron Microscope (SEM)

• Purpose : SEM produces high-resolution images of the surface of specimens using focused electron beams.
• Components : It includes an electron source, electromagnetic lenses, a sample chamber, and detectors for secondary electrons and backscattered electrons.
• Usage : The electron beam scans the sample’s surface, and signals from interactions with the beam create detailed images, revealing surface topography and composition.

Gas Chromatography-Mass Spectrometry (GC-MS)

• Purpose : GC-MS combines gas chromatography with mass spectrometry to identify and quantify chemical compounds in a mixture.
• Components : It has a gas chromatograph to separate compounds and a mass spectrometer to analyze their masses.
• Usage : The mixture is vaporized and separated in the chromatograph. The separated compounds are then ionized in the mass spectrometer and identified by their mass-to-charge ratios.

High-Performance Liquid Chromatograph (HPLC)

• Purpose : HPLC separates and quantifies compounds in a liquid mixture based on their interactions with a stationary phase.
• Components : It includes a pump, injector, column, detector, and data system.
• Usage : Liquid samples are pumped through a column filled with stationary phase. Different compounds interact differently, leading to separation. The detector records signals that are used for quantification.

UV-Visible Spectrophotometer

• Purpose : This instrument measures the absorption of ultraviolet and visible light by a sample, often for quantitative analysis.
• Components : It has a light source, monochromator, sample holder, and detector.
• Usage : A beam of light passes through the sample, and the detector measures how much light is absorbed. This data can be used to determine the concentration of an absorbing substance.

Flame Photometer

• Purpose : Flame photometers are used to measure the concentration of specific elements in a sample by analyzing the color of the flame produced when the elements are atomized.
• Components : It consists of a flame, nebulizer, burner, and a system for detecting emitted light.
• Usage : A sample is introduced into the flame, and the characteristic colors produced are compared to known standards to determine the element’s concentration.

Mass Spectrometer

• Purpose : Mass spectrometers determine the molecular composition of a sample by measuring the mass-to-charge ratio of ions.
• Components : They include an ionization source, mass analyzer, and detector.
• Usage : Samples are ionized, and the resulting ions are separated based on their mass-to-charge ratio. The detector records these ions, providing information about the sample’s composition.

Atomic Force Microscope (AFM)

• Purpose : AFMs allow for imaging and manipulating materials at the nanoscale by scanning a sharp tip across the surface.
• Components : AFMs have a cantilever with a sharp tip and a detector for measuring tip-sample interactions.
• Usage : The tip is brought close to the sample’s surface, and interactions between the tip and sample are measured, producing high-resolution topographical images.

Differential Scanning Calorimeter (DSC)

• Purpose : DSC measures changes in heat flow associated with phase transitions and chemical reactions in materials.
• Components : It consists of a sample holder, reference cell, and heating element.
• Usage : The sample and a reference are heated or cooled simultaneously, and the heat flow difference between them is recorded. This provides information about thermal properties and transitions.

Gas Density Meter

• Purpose : Gas density meters determine the density of gases under varying conditions of temperature and pressure.
• Components : They typically involve a sensor that measures the speed of sound in the gas.
• Usage : By measuring the speed of sound, these meters can calculate the density of gases, which is important in various industrial and research applications.

Circular Dichroism Spectrometer (CD)

• Purpose : CD spectrometers analyze the optical activity of chiral molecules to determine their secondary structure.
• Components : They include a light source, sample holder, and detectors for measuring differences in left and right circularly polarized light.
• Usage : CD spectroscopy is widely used in chemistry and biochemistry to study the conformation of biomolecules like proteins and nucleic acids.
• Purpose : Ultracentrifuges separate particles in suspensions based on size and density using high centrifugal forces.
• Components : They have a rotor, sample tubes, and a powerful motor for high-speed spinning.
• Usage : Ultracentrifugation is essential for tasks like separating macromolecules, organelles, or colloidal particles in biological and biochemical research.

Sonication Bath

• Purpose : Sonication baths use high-frequency sound waves to disrupt and disperse particles in liquids for sample preparation.
• Components : They consist of a bath filled with liquid and a sonication probe or transducer.
• Usage : Sonication is employed for tasks like cell disruption, homogenization, and degassing of solutions.

Raman Spectrometer

• Purpose : Raman spectrometers measure the scattering of monochromatic light by molecules to identify and characterize chemical compounds.
• Components : They include a laser source, spectrometer, and a detector for Raman scattering.
• Usage : Raman spectroscopy is used for chemical analysis, materials characterization, and identifying molecular structures.

Atomic Emission Spectrometer

• Purpose : Atomic emission spectrometers analyze the emission of light by excited atoms to determine elemental composition in samples.
• Components : They include a sample introduction system, excitation source (flame or plasma), and a detector.
• Usage : This instrument is widely used in elemental analysis, such as in environmental monitoring and metal analysis.

Microplate Reader

• Purpose : Microplate readers read absorbance, fluorescence, or luminescence in microplate wells for high-throughput screening and assays.
• Components : They have multiple detectors and can accommodate microplates with multiple sample wells.
• Usage : Microplate readers are essential in molecular biology, biochemistry, and drug discovery for rapid analysis of numerous samples.

Chromatography Data System (CDS)

• Purpose : A Chromatography Data System is software used to control and analyze data from chromatography instruments.
• Components : It includes data acquisition, processing, and reporting capabilities.
• Usage : CDS is crucial for managing and interpreting data generated from chromatography experiments, ensuring accurate and reliable results.

Cryo-Electron Microscope

• Purpose : Cryo-EM uses extremely low temperatures to study the structure of biological macromolecules and large assemblies.
• Components : It includes a specialized electron microscope and a cryogenic sample stage.
• Usage : Cryo-EM is revolutionizing structural biology by enabling the visualization of complex structures at near-atomic resolution.

Potentiostat-Galvanostat

• Purpose : A potentiostat-galvanostat is used to control and measure electrochemical reactions, often in corrosion studies and battery research.
• Components : It has three electrodes (working, reference, and counter electrodes) and a control unit.
• Usage : It’s employed in a wide range of electrochemical experiments, including corrosion rate determination and battery testing.

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometer (LA-ICP-MS) :

• Purpose : LA-ICP-MS analyzes solid samples by vaporizing them with a laser and measuring the elemental composition with ICP-MS.
• Components : It involves a laser ablation system coupled to an ICP-MS instrument.
• Usage : LA-ICP-MS is used for spatially-resolved elemental analysis in various fields, including geology, environmental science, and materials research.

Further References

• Laboratory Apparatus : https://owlcation.com/stem/A-Chemistry-Guide-List-of-Common-Laboratory-Equipment-Names-and-Uses
• Lab Equipments : https://www.google.com/amp/s/www.cnlabglassware.com/more-than-20-common-laboratory-apparatus-their-uses.html%3famp=1?espv=1

Related posts:

• Polyphosphoric Acid (H3PO4): Properties And Uses
• Tungsten(W): Properties, Use, Health & Environmental Effects
• Bunsen Burner Parts: Operation, Uses And Flames
• Cyclopentanol or Cyclopentyl alcohol: Preparation and Properties
• Different Types Of Electrodes
• Bismuth (Bi): Element Information, Properties And Uses

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The 10 Most Important Lab Safety Rules

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The science lab is an inherently dangerous place, with fire hazards, dangerous chemicals, and risky procedures. No one wants to have an accident in the lab, so it's imperative to follow lab safety rules. 

Key rules include following all instructions carefully, knowing the location and proper use of safety equipment, and dressing appropriately for lab work. These precautions help ensure a safer environment and minimize the risk of accidents. Here are the most important lab safety rules and why you must follow them.

The Most Important Lab Safety Rule

Follow the instructions. Whether it's listening to your instructor or lab supervisor or following a procedure in a book, it's critical to listen, pay attention, and be familiar with all the steps, from start to finish, before you begin. If you are unclear about any point or have questions, get them answered before starting, even if it's a question about a step later on in the protocol. Know how to use all of the lab equipment before you begin.

Why is this the most important rule? If you don't follow it:

• You endanger yourself and others in the lab.
• You could easily ruin your experiment.
• You put the lab at risk of an accident, which could damage equipment as well as harm people.
• You could get suspended (if you're a student) or fired (if you're a researcher).

Know the Location of Safety Equipment

In the event something goes wrong, it's important to know the location of the safety equipment and how to use it. It's a good idea to periodically check equipment to make sure it is in working order. For example, does water actually come out of the safety shower? Does the water in the eye wash look clean?

Not sure where safety equipment is located? Review lab safety signs and look for them before starting an experiment.

Dress for the Lab

Dress for the lab. This is a safety rule because your clothing is one of your best forms of protection against an accident. For any science lab, wear covered shoes and long pants, and keep your hair up so it can't fall into your experiment or a flame.

Make sure you wear protective gear , as needed. Basics include a lab coat and safety goggles. You may also need gloves, hearing protection, and other items, depending on the nature of the experiment.

Don't Eat or Drink in the Laboratory

Save your snacking for the office, not the lab. Don't eat or drink in the science laboratory. Don't store your food or beverages in the same refrigerator that contains experiments, chemicals, or cultures.

• There is too much risk of contaminating your food. You could touch it with a hand that is coated with chemicals or pathogens or set it down on a lab bench that has residue from past experiments.
• Having drinks in the lab risks your experiment, too. You could spill a drink on your research or lab notebook.
• Eating and drinking in the lab is a form of distraction. If you are eating, you aren't concentrating on your work.
• If you're used to drinking liquids in the lab, you might accidentally reach for and drink the wrong liquid. This is especially true if you did not label your glassware or used lab glassware as dishes.

Don't Taste or Sniff Chemicals

Not only should you not bring in food or drinks, but you shouldn't taste or smell chemicals or biological cultures already in the lab. Tasting or smelling some chemicals can be dangerous or even deadly. The best way to know what's in a container is to label it, so get in the habit of making a label for glassware before adding the chemical.

Don't Play Mad Scientist in the Laboratory

Another important safety rule is to act responsibly in the lab; don't play Mad Scientist, randomly mixing chemicals to see what happens. The result could be an explosion, fire, or release of toxic gases .

Similarly, the laboratory is not the place for horseplay. You could break glassware, annoy others, and potentially cause an accident.

Dispose of Lab Waste Properly

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Another important laboratory safety rule is to know what to do with your experiment when it's over. Before you start an experiment, you should know what to do at the end. Don't leave your mess for the next person to clean up.

Here are some questions to consider:

• Are the chemicals safe to dump down the drain? If not, what do you do with them?
• If you have biological cultures, is it safe to clean up with soap and water or do you need an autoclave to kill dangerous organisms?
• Do you have broken glass or needles? Know the protocol for disposing of chemical sharps.

Know What to Do With Lab Accidents

 Getty Images/Oliver Sun Kim

Accidents happen, but you can do your best to prevent them and have a plan to follow when they occur. Most laboratories have a plan to follow in the event of an accident.

One particularly important safety rule is to tell a supervisor if and when an accident occurs . Don't lie about it or try to cover it up. If you get cut, exposed to a chemical, or bitten by a lab animal, or if you spill something, there could be consequences, and the danger isn't necessarily only to you. If you don't get the proper care, sometimes you could expose others to a toxin or pathogen. Also, if you don't admit to an accident, you could get your lab in a lot of trouble.

Leave Experiments at the Lab

Getty Images/G Robert Bishop

It's important, for your safety and the safety of others, to leave your experiment at the lab. Don't take it home with you. You could cause a spill, lose a specimen, or have an accident. This is how science fiction movies start. In real life, you can hurt someone, cause a fire, or lose your lab privileges.

While you should leave lab experiments at the lab, if you want to do science at home, there are many safe science experiments you can try.

Don't Experiment on Yourself

The premise of many a science fiction movie starts with a scientist conducting an experiment on him or herself. However, you won't gain superpowers or discover the secret to eternal youth. More than likely, whatever you accomplish will be at great personal risk.

Science means using the scientific method . You need data on multiple subjects to draw conclusions, but using yourself as a subject and self-experimenting is dangerous, not to mention bad science.

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Spiteful stacks and questionable queues.

Assignment Description

In this lab, you will learn to think recursively and apply it to the stack and queue data structures. You might also practice templates.

Lab Insight

Stacks and queues are incredible data structures used in a wide range of applications throughout the world. You have already seen a great example of a queue in CS 225. We use the queue data structure to help us manage our office hours. Queues can do so many incredible things like helping schedule tasks for a computer or managing user priority based on a FIFO (first in, first out) principle. You will find both useful in traversing a graph later in the semester. These data structures are very versatile and useful throughout the software world. A visualization of them can be found here .

What is recursion? Recursion is a way of thinking about problems that allows the computer to do more of the heavy lifting for us. It is analogous to the mathematical definition of recursive functions, where you can define a function call in terms of calls to itself and basic arithmetic operations, but not in terms of loops.

Why recursion? While being able to think recursively is one of the harder parts of computer science, it is also one of the most powerful. In fact, there are whole languages that entirely use recursion instead of loops, which, even though it may seem inefficient, leads to some very useful optimizations a compiler can make when dealing with such code. There are probably more problems in computer science that are simpler recursively than they are iteratively (using loops). Also, once you have a recursive algorithm, it is always possible to transform it into an iterative algorithm using a stack and a while loop. In this way, computer scientists can think about problems recursively, then use that recursive solution to make a fast iterative algorithm (and in the grand scheme of big-O notation, using recursion has little overhead compared to the rest of the running time). Here we’ll only ask you to do the first part.

How do I write recursively? Recursion just means calling a function within itself. This may sound crazy, but in fact it is not. Let’s take an iterative function to calculate the factorial of a number \(n\), \(n!\):

Okay, so four lines of code. Pretty short and understandable. Now let’s look at a recursive version:

Only two lines of code! (Depending on whether you like putting your return statement on the same line.) Even on such a small problem, recursion helps us express ourselves more concisely. This definition also fits better with the mathematical definition:

A typical recursive function call consists of three parts. Let’s examine the function more closely to see them. Here’s the same code again, with more discussion.

Checking Out the Code

All assignments will be distributed via our release repo on github this semester. You will need to have set up your git directory to have our release as a remote repo as described in our git set up

You can merge the assignments as they are released into your personal repo with

The first git command will fetch and merge changes from the main branch on your remote repository named release into your personal. The --no-edit flag automatically generates a commit message for you, and the --no-rebase flag will merge the upstream branch into the current branch. Generally, these two flags shouldn’t be used, but are included for ease of merging assignments into your repo.

The second command will push to origin (your personal), which will allow it to track the new changes from release .

You will need to run these commands for every assignment that is released.

All the files for this lab are in the lab_quacks directory.

Preparing Your Code

This semester for MPs we are using CMake rather than just make. This allows for us to use libraries such as Catch2 that can be installed in your system rather than providing them with each assignment. This change does mean that for each assignment you need to use CMake to build your own custom makefiles. To do this you need to run the following in the base directory of the assignment. Which in this assignment is the lab_quacks directory.

This first makes a new directory in your assignment directory called build . This is where you will actually build the assignment and then moves to that directory. This is not included in the provided code since we are following industry standard practices and you would normally exclude the build directory from any source control system.

Now you need to actually run CMake as follows.

This runs CMake to initialize the current directory which is the build directory you just made as the location to build the assignment. The one argument to CMake here is .. which referes to the parent of the current directory which in this case is top of the assignment. This directory has the files CMake needs to setup your assignment to be build.

At this point you can in the build directory run make as described to build the various programs for the MP.

STL Stack and Queue These activities use the standard template library’s stack and queue structures. The interfaces of these abstract data types are slightly different than in lecture, so it will be helpful for you to look up “STL Stack” and “STL Queue” on Google (C++ reference has good information). In particular, note that the pop() operations do not return the element removed, and that you must look that up before calling pop() .

As usual, to see all the required functions, check out the Doxygen .

Recursive Exercises

You may not use any loops for this section! Try to think about the problem recursively: in terms of a base case, a smaller problem, and an incremental step to transform the smaller problem to the current problem.

Sum of Digits

Given a non-negative int n , return the sum of its digits recursively (no loops). Note that modulo ( % ) by 10 yields the rightmost digit ( 126 % 10 == 6 ), while divide ( / ) by 10 removes the rightmost digit ( 126 / 10 == 12 ).

We have triangle made of blocks. The topmost row has 1 block, the next row down has 2 blocks, the next row has 3 blocks, and so on:

Compute recursively (no loops or multiplication) the total number of blocks in such a triangle with the given number of rows.

Note These examples were stolen from http://codingbat.com/java/Recursion-1 . All credit goes to CodingBat. If you are having a hard time with sum (below), we encourage you to go to CodingBat and try more recursive exercises. These are in Java, but there are links at the bottom of the page describing the differences of strings and arrays in Java from C++, which are minor.

The sum Function

Write a function called sum that takes one stack by reference, and returns the sum of all the elements in the stack, leaving the original stack in the same state (unchanged). You may modify the stack, as long as you restore it to its original values. You may use only two local variables of type T in your function. Note that this function is templatized on the stack’s type, so stacks of objects overloading the addition operator ( operator+ ) can be summed. Hint: think recursively!

STL Stack We are using the Standard Template Library (STL) stack in this problem. Its pop function works a bit differently from the stack we built. Try searching for “STL stack” to learn how to use it.

Non Recursive Exercises

Balancing brackets: the isbalanced function.

For this exercise, you must write a function called isBalanced that takes one argument, an std::queue , and returns whether the string represented by the queue has balanced brackets, parentheses, and braces . The queue may contain any characters, although you should only consider bracket characters ( ‘[’ and ‘]’ ), parentheses ( ‘(‘ and ‘)’ ), and braces (‘{‘ and ‘}’ ) when considering whether a string is balanced. To be balanced, a string must not have any unmatched, extra, or hanging brackets. For example, the string [hello][] is balanced, [[]({}a)] is balanced, []] is unbalanced, ][ is unbalanced, and ))))[cs225] is unbalanced. It’s possible to solve this problem without using a stack, but in the spirit of this lab, you should use one in your solution!

For this function, you may only create a single local variable of type stack<char> ! No other stack or queue local objects may be declared.

The scramble Function

Your task is to write a function called scramble that takes one argument: a reference to a std::queue .

You may use whatever local variables you need. The function should reverse the order of SOME of the elements in the queue, and maintain the order of others, according to the following pattern:

• The first element stays on the front of the queue.
• Then the next two elements are reversed.
• Then the next three elements are placed on the queue in their original order.
• Then the next four elements are reversed.
• Then the next five elements are place on the queue in their original order.

Hint : You’ll want to make a local stack variable.

For example, given the following queue,

we get the following result:

Any “leftover” numbers should be handled as if their block was complete. (See the way 15 and 16 were treated in our example above.)

STL Queue We are using the Standard Template Library (STL) queue in this problem. Its pop function works a bit differently from the queue we built. Try searching for “STL queue” to learn how to use it.

Testing Your Code

Run the Catch tests as follows:

Grading Information

The following files are used in grading:

• exercises.cpp
• exercises.h
• quackfun.hpp

All other files including any testing files you have added will not be used for grading.

All other files, including quacks.cpp and any testing files you have added will not be used for grading. Remember that submissions must be done through Prairielearn !

• Peter Fischer Appointed Director of Berkeley Lab’s Materials Sciences Division

Following an international search, Peter Fischer, an internationally recognized expert in magnetic materials, has been selected to serve as the next division director of Lawrence Berkeley National Laboratory’s (Berkeley Lab) Materials Sciences Division . Fischer has served as the deputy director of the Materials Sciences Division since 2015 and as interim division director since 2023.

“We are thrilled that Peter has accepted this appointment,” said Jeff Neaton, the associate laboratory director for the Energy Sciences Area. “He has a deep knowledge of and appreciation for the Materials Sciences Division’s scientific programs and operations, including the Center for X-Ray Optics, experience working across Berkeley Lab, and a history of successful partnership with program managers in Basic Energy Sciences.”

The Materials Sciences Division is one of four divisions that form the Energy Sciences Area, Berkeley Lab’s home for fundamental research in materials and chemical sciences. In the Materials Sciences Division, researchers advance the fundamental science of materials within the context of global energy-related challenges, developing techniques to design, discover and understand materials for a wide range of applications, including solar cells, computer chips, and nanoscale devices. Through the Division’s core programs and research centers, they cultivate a collaborative and interdisciplinary approach to materials research and help train the next generation of materials scientists.

“I am excited and honored to have the opportunity to lead the Materials Sciences Division,” said Fischer. “I look forward to working with such excellent and talented scientists, postdocs, and students supported by a dedicated team of admins and technicians to achieve our scientific goals in advancing materials science and solving important problems of the future.”

Fischer received his Ph.D. in Physics (Dr.rer.nat.) from the Technical University in Munich, Germany in 1993 on pioneering work with X-ray magnetic circular dichroism in rare earth systems. In 1996 he performed groundbreaking experiments at the BESSY synchrotron in Berlin/Germany in using soft x-ray microscopy for imaging magnetic nanostructures.

Since joining Berkeley Lab in 2004, Fischer has held multiple roles and leadership positions. In addition to his senior management roles in the Materials Sciences Division, he headed the soft X-ray microscopy beamline 6.1.2 at the Advanced Light Source, and is a major participant in the Non-Equilibrium Magnetic Materials Program, where his research is focused on the use of polarized synchrotron radiation for the study of fundamental problems in magnetism. Fischer has been a senior scientist at Berkeley Lab since 2016, and he is also an adjunct professor at UC Santa Cruz and a fellow of the American Physical Society and the Institute of Electrical and Electronics Engineers.

“Throughout my career, I have done materials research at various institutions in different parts of the world, but being at Berkeley Lab has been the most rewarding experience for me,” said Fischer. “My goal is to provide this experience to anyone working with us in the Materials Sciences Division.”

Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to delivering solutions for humankind through research in clean energy, a healthy planet, and discovery science. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 16 Nobel Prizes. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science .

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A new genetic analysis of animals in the Wuhan market in 2019 may help find COVID-19’s origin

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LONDON (AP) — Scientists searching for the origins of COVID-19 have zeroed in on a short list of animals that possibly helped spread it to people, an effort they hope could allow them to trace the outbreak back to its source.

Researchers analyzed genetic material gathered from the Chinese market where the first outbreak was detected and found that the most likely animals were racoon dogs , civet cats and bamboo rats. The scientists suspect infected animals were first brought to the Wuhan market in late November 2019, which then triggered the pandemic.

Michael Worobey, one of the new study’s authors, said they found which sub-populations of animals might have transmitted the coronavirus to humans. That may help researchers pinpoint where the virus commonly circulates in animals, known as its natural reservoir.

“For example, with the racoon dogs, we can show that the racoon dogs that were (at the market) … were from a sub-species that circulates more in southern parts of China,” said Worobey, an evolutionary biologist at the University of Arizona. Knowing that might help researchers understand where those animals came from and where they were sold. Scientists might then start sampling bats in the area, which are known to be the natural reservoirs of related coronaviruses like SARS.

While the research bolsters the case that COVID-19 emerged from animals, it does not resolve the polarized and political debate over whether the virus instead emerged from a research lab in China .

Mark Woolhouse, a professor of infectious diseases at the University of Edinburgh, said the new genetic analysis suggested that the pandemic “had its evolutionary roots in the market” and that it was very unlikely COVID-19 was infecting people before it was identified at the Huanan market.

“It’s a significant finding and this does shift the dial more in favor of an animal origin,” Woolhouse, who was not connected to the research, said. “But it is not conclusive.”

An expert group led by the World Health Organization concluded in 2021 that the virus probably spread to humans from animals and that a lab leak was “extremely unlikely.” WHO chief Tedros Adhanom Ghebreyesus later said it was “premature” to rule out a lab leak .

An AP investigation in April found the search for the COVID origins in China has gone dark after political infighting and missed opportunities by local and global health officials to narrow the possibilities.

Scientists say they may never know for sure where exactly the virus came from.

In the new study, published Thursday in the journal Cell , scientists from Europe, the U.S. and Australia analyzed data previously released by experts at the Chinese Center for Disease Control and Prevention. It included 800 samples of genetic material Chinese workers collected on Jan. 1, 2020 from the Huanan seafood market, the day after Wuhan municipal authorities first raised the alarm about an unknown respiratory virus.

Chinese scientists published the genetic sequences they found last year, but did not identify any of the animals possibly infected with the coronavirus. In the new analysis, researchers used a technique that can identify specific organisms from any mixture of genetic material collected in the environment.

Worobey said the information provides “a snapshot of what was (at the market) before the pandemic began” and that genetic analyses like theirs “helps to fill in the blanks of how the virus might have first started spreading.”

Woolhouse said the new study, while significant, left some critical issues unanswered.

“There is no question COVID was circulating at that market, which was full of animals,” he said. “The question that still remains is how it got there in the first place.”

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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FDA Faces Blowback Over Stricter Regulation of Lab-Developed Tests

September 18, 2024

The US Food and Drug Administration (FDA) plans to scrutinize the safety and efficacy of lab-developed tests — those designed, manufactured, and used in a single laboratory — far more thoroughly in the future.

Under a rule finalized in April , the FDA will treat facilities that develop and use lab tests as manufacturers and regulate tests as medical devices. That means that most lab tests will need an FDA review before going on sale.

The FDA will also impose new quality standards, requiring test manufacturers to report adverse events and create a registry of lab tests under the new rule, which will be phased in over 4 years.

FDA officials have been concerned for years about the reliability of commercial lab tests, which have ballooned into a multibillion-dollar industry.

Consumer groups have long urged the FDA to regulate lab tests more strictly, arguing that the lack of scrutiny allows doctors and patients to be exploited by bad actors such as Theranos, which falsely claimed that its tests could diagnose multiple diseases with a single drop of blood.

"When it comes to some of these tests that doctors are recommending for patients, many doctors are just crossing their fingers and relying on the representation of the company because nobody is checking " to verify a manufacturer's claims, said Joshua Sharfstein, MD, vice dean for public health practice and community engagement at the Johns Hopkins Bloomberg School of Public Health, Baltimore.

Nearly 12,000 Labs Making Medical Tests

Although the FDA estimates there are nearly 12,000 labs manufacturing medical tests, agency officials said they don't know how many tests are being marketed. The FDA already requires that home test kits marketed directly to consumers, such as those used to detect COVID-19, get clearance from the agency before being sold.

"There's plenty of time for industry to get its act together to develop the data that it might need to make a premarket application," said Peter Lurie, MD, PhD, a former associate commissioner at the FDA. In 2015, Lurie led a report outlining some of the dangers of unregulated lab tests.

For the average physician who orders lab tests, nothing is going to immediately change due to the final rule, said Lurie, now president of the Center for Science in the Public Interest, a nonprofit consumer watchdog.

"Tomorrow, this will look just the same as it does today," Lurie said. "For the next 3 years, the companies will be scurrying behind the scenes to comply with the early stages of implementation. But most of that will be invisible to the average practitioner."

Lurie predicts the FDA will focus its scrutiny on tests that pose the greatest potential risk to patients, such as ones used to diagnose serious diseases or guide treatment for life-threatening conditions. "The least significant tests will likely get very limited, if any, scrutiny," said Lurie, adding that the FDA will likely issue guidance about how it plans to define low- and high-risk tests. "My suspicion is that it will be probably a small minority of products that are subject to full premarket approval."

Lab Industry Groups Push Back

But imposing new rules with the potential to affect an industry's bottom line is no easy task.

The American Clinical Laboratory Association, which represents the lab industry, said in a statement that the FDA rule will "limit access to scores of critical tests, increase healthcare costs, and undermine innovation in new diagnostics." Another industry group, the Association for Molecular Pathology, has warned of "significant and harmful disruption to laboratory medicine."

The two associations have filed separate lawsuits, charging that the FDA overstepped the authority granted by Congress. In their lawsuits, groups claim that lab tests are professional services , not manufactured products. The groups noted that the Centers for Medicare & Medicaid Services (CMS) already inspects lab facilities . CMS does not assess the tests' quality or reliability.

A recent Supreme Court decision could make those lawsuits more likely to succeed, said David Simon, JD, LLM, PhD, an assistant professor of law at the Northeastern University School of Law.

In the case of Loper Bright Enterprises v. Raimondo , decided in June, justices overturned a long-standing precedent known as Chevron deference, which required courts to defer to federal agencies when interpreting ambiguous laws. That means that courts no longer have to accept the FDA's definition of a device, Simon said.

"Because judges may have more active roles in defining agency authority, federal agencies may have correspondingly less robust roles in policymaking," Simon wrote in an JAMA editorial co-authored with Michael J. Young, MD, MPhil, of Harvard Medical School, Boston, and published last month.

The Supreme Court ruling could pressure Congress to more clearly define FDA's ruling in regulating lab tests, Simon and Young wrote.

Members of Congress first introduced a bill to clarify the FDA's role in regulating lab tests, called the VALID Act, in 2020. The bill stalled and, despite efforts to revive it , still hasn't passed.

FDA officials have said they remain "open to working with Congress," noting that any future legislation about lab-developed tests would supersede their current policy.

In an interview, Simon noted the FDA significantly narrowed the scope of the final rule in response to comments from critics who objected to an earlier version of the policy proposed last year. The final rule carves out several categories of tests that won't need to apply for "premarket review."

Notably, a "grandfather clause" will allow some lab tests already on the market to continue being sold without undergoing FDA's premarket review process. In explaining the exemption, FDA officials said they did not want doctors and patients to lose access to tests on which they rely. But Lurie noted that because the FDA views all these tests as under its jurisdiction, the agency could opt to take a closer look "at a very old device that is causing a problem today."

The FDA also will exempt tests approved by New York State's Clinical Laboratory Evaluation Program, which conducts its own stringent reviews. And the FDA will continue to allow hospitals to develop tests for patients within their healthcare system without going through the FDA approval process, if no FDA-approved tests are available.

Hospital-based tests play a critical role in treating infectious diseases, said Amesh Adalja, MD, an infectious diseases specialist and senior scholar at the Johns Hopkins Center for Health Security. For example, a large research hospital treating a patient with cytomegalovirus may need to develop its own test to determine whether the infection is resistant to antiviral drugs, Adalja said.

"With novel infectious disease outbreaks, researchers are able to move quickly to make diagnostic tests months and months before commercial laboratories are able to get through regulatory processes," Adalja said.

To help scientists respond quickly to emergencies, the FDA published special guidance for labs that develop unauthorized lab tests for disease outbreaks.

Medical groups such as the American Hospital Association and Infectious Diseases Society of America remain concerned about the burden of complying with new regulations.

"Many vital tests developed in hospitals and health systems may be subjected to unnecessary and costly paperwork," said Stacey Hughes, executive vice president of the American Hospital Association, in a statement.

Other groups, such as the American Society of Clinical Oncology, praised the new FDA policy. In comments submitted to the FDA last year, the cancer group said it "emphatically supports" requiring lab tests to undergo FDA review.

"We appreciate FDA action to modernize oversight of these tests and are hopeful this rule will increase focus on the need to balance rapid diagnostic innovation with patient safety and access" Everett Vokes, MD, the group's board chair, said in a statement released after the FDA's final rule was published.

Send comments and news tips to [email protected] .

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• Jet Propulsion Laboratory

Re-Creating Mars in the Lab

What’s next for plume testing, news media contacts.

Tests on Earth appear to confirm how the Red Planet’s spider-shaped geologic formations are carved by carbon dioxide.

Since discovering them in 2003 via images from orbiters, scientists have marveled at spider-like shapes sprawled across the southern hemisphere of Mars. No one is entirely sure how these geologic features are created. Each branched formation can stretch more than a half-mile (1 kilometer) from end to end and include hundreds of spindly “legs.” Called araneiform terrain, these features are often found in clusters, giving the surface a wrinkled appearance.

The leading theory is that the spiders are created by processes involving carbon dioxide ice, which doesn’t occur naturally on Earth. Thanks to experiments detailed in a new paper published in The Planetary Science Journal, scientists have, for the first time, re-created those formation processes in simulated Martian temperatures and air pressure.

“The spiders are strange, beautiful geologic features in their own right,” said Lauren Mc Keown of NASA’s Jet Propulsion Laboratory in Southern California. “These experiments will help tune our models for how they form.”

The study confirms several formation processes described by what’s called the Kieffer model: Sunlight heats the soil when it shines through transparent slabs of carbon dioxide ice that built up on the Martian surface each winter. Being darker than the ice above it, the soil absorbs the heat and causes the ice closest to it to turn directly into carbon dioxide gas — without turning to liquid first — in a process called sublimation (the same process that sends clouds of “smoke” billowing up from dry ice). As the gas builds in pressure, the Martian ice cracks, allowing the gas to escape. As it seeps upward, the gas takes with it a stream of dark dust and sand from the soil that lands on the surface of the ice.

When winter turns to spring and the remaining ice sublimates, according to the theory, the spiderlike scars from those small eruptions are what’s left behind.

For Mc Keown and her co-authors, the hardest part of conducting these experiments was re-creating conditions found on the Martian polar surface: extremely low air pressure and temperatures as low as minus 301 degrees Fahrenheit (minus 185 degrees Celsius). To do that, Mc Keown used a liquid-nitrogen-cooled test chamber at JPL, the Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE.

“I love DUSTIE. It’s historic,” Mc Keown said, noting that the wine barrel-size chamber was used to test a prototype of a rasping tool designed for NASA’s Mars Phoenix lander. The tool was used to break water ice, which the spacecraft scooped up and analyzed near the planet’s north pole.

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For this experiment, the researchers chilled Martian soil simulant in a container submerged within a liquid nitrogen bath. They placed it in the DUSTIE chamber, where the air pressure was reduced to be similar to that of Mars’ southern hemisphere. Carbon dioxide gas then flowed into the chamber and condensed from gas to ice over the course of three to five hours. It took many tries before Mc Keown found just the right conditions for the ice to become thick and translucent enough for the experiments to work.

Once they got ice with the right properties, they placed a heater inside the chamber below the simulant to warm it up and crack the ice. Mc Keown was ecstatic when she finally saw a plume of carbon dioxide gas erupting from within the powdery simulant.

“It was late on a Friday evening and the lab manager burst in after hearing me shrieking,” said Mc Keown, who had been working to make a plume like this for five years. “She thought there had been an accident.”

The dark plumes opened holes in the simulant as they streamed out, spewing simulant for as long as 10 minutes before all the pressurized gas was expelled.

The experiments included a surprise that wasn’t reflected in the Kieffer model: Ice formed between the grains of the simulant, then cracked it open. This alternative process might explain why spiders have a more “cracked” appearance. Whether this happens or not seems dependent on the size of soil grains and how embedded water ice is underground.

“It’s one of those details that show that nature is a little messier than the textbook image,” said Serina Diniega of JPL, a co-author of the paper.

Now that the conditions have been found for plumes to form, the next step is to try the same experiments with simulated sunlight from above, rather than using a heater below. That could help scientists narrow down the range of conditions under which the plumes and ejection of soil might occur.

There are still many questions about the spiders that can’t be answered in a lab. Why have they formed in some places on Mars but not others? Since they appear to result from seasonal changes that are still occurring, why don’t they seem to be growing in number or size over time? It’s possible that they’re left over from long ago, when the climate was different on Mars— and could therefore provide a unique window into the planet’s past.

For the time being, lab experiments will be as close to the spiders as scientists can get. Both the Curiosity and Perseverance rovers are exploring the Red Planet far from the southern hemisphere, which is where these formations appear (and where no spacecraft has ever landed). The Phoenix mission, which landed in the northern hemisphere, lasted only a few months before succumbing to the intense polar cold and limited sunlight.

Andrew Good Jet Propulsion Laboratory, Pasadena, Calif. 818-393-2433 [email protected]

Karen Fox / Molly Wasser Headquarters, Washington 202-358-1600 [email protected]  / [email protected]

Related Terms

• Mars Reconnaissance Orbiter (MRO)

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