your assignment in biology lab is to design an experiment

Biology 116 Lab Manual

Designing the experiment.

Once you have a research question and a clear and testable a priori hypothesis, you can design an experiment to test it.

Ideally, experiments should be conducted in such a way that the experimenter has control over every variable that might have an influence on your results (in reality this is much harder than it sounds!). A variable is any factor that might affect the outcome of the experiment. The experimenter therefore manipulates the independent variable and observes the effects of this manipulation on the dependent (or response ) variable .

For example, if the goal is to determine the effects of temperature on plant height after 14 days, then height is the dependent variable because it "depends" on the temperature to which the plant is exposed. All other variables must be controlled or held constant, to the extent possible. Temperature is the independent variable because that is the variable which is manipulated by the experimenter.

The ideal way to perform such an experiment is to arrange a set of tests that are identical in all ways (light, soil moisture, etc.) except for the one specific factor that is being tested (in this case, temperature). Thus, for our plant experiment, a greenhouse with multiple temperature-control chambers would be ideal; each chamber would host a different temperature "treatment group". It's worth noting here that we don't always have the option to do what's ideal, but that doesn't mean we shouldn't work to control as much as possible. We should also consider carefully how the things we didn't or couldn't control might be affecting the results of our experiment.

Crucially, one of the treatment groups must serve as a control group against which all other treatment groups are compared. The importance of the control group cannot be over emphasized . It is essential to know how the system you are investigating works under normal circumstances (i.e., before you started messing with it), before you can be sure the results obtained from the experimentation are actually due to the manipulation of the independent variable(s).

To continue our example above, if you wanted to investigate the effects of temperature (the independent variable) on the height of plants after 14 days (the dependent variable), you would measure the heights of plants grown at their normal, expected temperature (most likely room temperature) as the control group, and then compare the data collected from this group to the heights of plants exposed to higher and / or lower temperatures, depending on what you're hoping to learn. The control group would provide the "normal standard" against which the other treatment groups would be compared.

Sample Size

Another important rule governing experimentation is that each treatment group (which includes the control group) should include a decent number of individual test subjects or "replicates" (and ideally an equal number of individuals in each group). The more replicates you include in your experiment, or in other words the larger your "sample size" (indicated by the letter n ) per treatment group, the more confident you would be in your results and the more power your study has. However, in most situations, increasing the number of replicates increases the cost and / or logistical difficulty of the experiment.

The key is to have a sufficient number of replicates per group to ensure your experiment has the power to detect meaningful treatment effects (if they exist). Determining what the minimum sample size per group should be is beyond our scope here, but for our purposes you can assume that three is the bare minimum, and ten or more is desirable.

Variation & Random Assignment

Biological variation is the inherent differences among organisms in a study that arise due to differences in genetic makeup, age, sex, health, etc. This natural variation has the potential to obscure or confuse experimental treatment effects. Thus, it is important to attempt to minimize this variation when designing your experiment (e.g., by using organisms of the same age, sex, etc.). Even when potential sources of variation are accounted for, it is crucial that subjects be randomly assigned to the treatment groups, so that any inherent variation among individuals will be distributed at random among all treatments, including the control group.

The Biology Corner

Biology Teaching Resources

Experimental Design

Students in most science classes start with an overview of scientific processes. For advanced students, I use this cheat sheet to remind them of the major features of the scientific method, such as a control group, dependent and independent variables .

AP Bio students are also introduced to the concept of the null and alternative hypotheses as well as how to statistically analyze data. Though this was made for AP Biology, you could make a copy of it (google docs) and then edit it to work other groups.

This guide can be used with introductory lessons like “ The Fortune Telling Fish ” where students can be asked to describe each feature of the design they used to determine what was causing the fish’s movements.

My anatomy and physiology classes and AP Bio classes do an investigation where they determine trends of lung capacity and physical features like height and sex.

This guide also clearly describes the difference between a hypothesis and a prediction. Many biology books describe them as being the same thing, but they are not. “ Teaching the Hypothesis ” is a great article on why the difference matters.

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A Biologist’s Guide to Design of Experiments

Biology is a notoriously difficult research area, especially for replicating results. To paraphrase from a film that has inspired thousands of people to get into this field: life finds a way (of behaving unexpectedly). Because everything is so interconnected in biology, the one-factor-at-a-time (OFAT) approach is usually taken to investigate biological systems. But what if there were a better way to gain insights into the holistic nature of biology and explore the interconnectedness of various factors while maintaining scientific accuracy?

Well, there is. It’s called Design of Experiments (DOE).

The term ‘Design of Experiments’ can be a confusing one. Of course a scientist is going to design their experiment. In fact, what we are referring to when we say Design of Experiments (or DOE) is   a branch of applied statistics that can be applied to experimental design.   This systematic method allows scientists to simultaneously investigate the impact of different factors on an experimental process, while also taking the interactions between factors into consideration. 

There are plenty of benefits to performing DOE. Compared to other experimental approaches, DOE saves time and resources when performing experiments, whilst providing deeper insight into complex systems. 

If DOE is the key to unlocking biological complexity, why is it not used all the time?

Before we dive into that, let’s first look at the more traditional experimental approaches that scientists use to study complex systems. 

OFAT vs DOE

In order to fully comprehend the power of DOE, it’s helpful to have an understanding of more commonly used approaches, such as investigating a single factor at a time. 

One-factor-at-a-time (OFAT) methods are incredibly common in biological research.  One component (factor) is picked at a time and its values (levels) are varied, keeping all other known components constant. In this way, the impact of the selected component can be tested at each variation.

However, experimental optimization using OFAT methods limits the breadth of the possible design space and, by neglecting certain factors or their interactions, often identifies an incorrect optimal state of the system. By testing biological factors in isolation, scientists can be left blind to the interactions between other factors. 

Changing one factor at a time (OFAT, left) means effects are easy to distinguish but there is less information on how factors interact, a critical feature of complex systems. Using statistical techniques to design experiments that explore combinations of factor settings allows their effects to be understood in combination (DOE, right). Optimal results which would otherwise be missed can then be discovered.

In contrast to OFAT experimentation, the systematic structure of experimental conditions in DOE allows researchers to vary and test multiple factors in one go. By simultaneously investigating the effect of many factors on a process of interest, researchers are provided with a more complete understanding of the biological system they are studying.

DOE requires fewer resources for the amount of information obtained , saving on time and materials. By measuring multiple factors at once, you are reducing the number of biological and technical replicates required for a statistically accurate measurement compared to measuring those factors individually. 

Additionally, because some factors have a direct or indirect relationship with others, measuring the effect of these factors simultaneously can give better insights into a biological process. These relationships or “interactions” often underpin complex and non-intuitive trends in the data which, in turn, hold key insight into the underlying biological complexity of a system or process. 

Interactions between experimental factors are everywhere in bioprocessing but, with traditional experimentation, they are hard to investigate, and often go ignored or unrecognized. In fermentation, for example, pH readout is affected by the temperature of the medium and will shift as temperature changes, even before the medium is inoculated. By using a DOE approach researchers can pin down crucial interacting factors and gain crucial understanding and insight into how they can be exploited or controlled to improve system performance. When working in highly complicated systems and processes, such as in the production of biological therapeutics, DOE is the best approach to optimizing a process.

Even with all its benefits, many biologists still don’t perform DOE. This is for a number of reasons. DOE can be daunting to execute when the interactions of large numbers of factors need to be measured.  Many biologists are still unfamiliar with DOE if they didn’t study it or haven’t used it before, and it may be hard to know where to start.

Getting started with DOE 

As with anything, there can be a learning curve to setting up and starting to perform DOE. 

DOE can be difficult to plan and analyze; however experimental execution of a DOE can be particularly challenging, especially for those less comfortable with automation. DOE can be performed manually for two or three factors simultaneously, but as the number of factors increases or if you have liquid handling robots to carry out more complex experiments, the planning and attention to detail required to execute complex DOE designs becomes a significant burden,  you will need to use specialized software such as JMP to help design and model your experiments, and build a statistically accurate picture of your process. Whatever software package you use, there is plenty of support and information to help you design and analyze your experiment. 

One benefit of the COVID pandemic is that companies have put a lot of demos and resources online. You can see DOE in action with   liquid handling and automation here , and how software tools can   help design and carry out DOE experiments . Another great resource that we highly recommend for people starting out with DOE is the book DOE Simplified: Practical Tools for Effective Experimentation by Mark J. Anderson and Patrick J. Whitcomb.

Let us help you get started with DOE. Join our DOE masterclass webinar for biologists.

DOE and Machine Learning 

Machine Learning (ML), whereby computational algorithms interpret complex data, is a methodological approach to solving optimization problems when there is a lot of data available.   DOE can help ML approaches become more effective by finding the optimal algorithmic parameter settings, while ML can support DOE by better detecting the effects of factors and their interactions. Biological experimentation can be expensive, but through the use of DOE, coupled with ML, it may be possible to build Machine Learning capabilities using smaller (and less costly) data sets. This is especially useful as experiments scale up and the amount of data generated is difficult to collect and process manually.

Liquid handling technologies allow us to   consider more complex DOE experiments than ever before as they transcend the human limitations of carrying out physical work. This results in much more data captured by the software, as well as metadata that contextualizes the main data points of the factors under examination. By leveraging the power of ML in data analysis, the effect of the metadata can be considered in addition to the main data points in how outputs are affected by a process.

Transforming the biological research landscape with DOE

DOE is a powerful statistical and experimental design tool that allows biological researchers to make their processes more defined and predictable. The methodology is well suited to automating liquid handling and an array of software tools exist to help translate DOE designs into viable experiments. The data bottleneck can be addressed manually for small-scale DOE designs, but new software tools like ML can tease out new insights from the data allowing for more predictable and accurate research in the traditionally unpredictable field of biology. 

DOE is already a cornerstone of industry standards   supporting Quality by Design principles and the adoption of Computer-Aided Biology tools in this space is well underway. DOE and supporting CAB technologies are poised to transform the biological research landscape, uncovering new insights from data and ensuring biological research is more robust and precise than ever.

Learn more about DOE from our in-house experts by clicking here .

Synthace Team

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How to Teach Your Students to Design an Experiment

• State a hypothesis that is testable.
• Write out detailed steps to their procedure.
• Determine the independent and the dependent variables.
• Include a description of their control and how it served as a control.
• Include a description of their experimental groups.
• Identify factors that must remain constant throughout the experiment.
• Design a data table.
• Graph their germination rates.
• Form a conclusion based on the data gathered.

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How To Write A Lab Report | Step-by-Step Guide & Examples

Published on May 20, 2021 by Pritha Bhandari . Revised on July 23, 2023.

A lab report conveys the aim, methods, results, and conclusions of a scientific experiment. The main purpose of a lab report is to demonstrate your understanding of the scientific method by performing and evaluating a hands-on lab experiment. This type of assignment is usually shorter than a research paper .

Lab reports are commonly used in science, technology, engineering, and mathematics (STEM) fields. This article focuses on how to structure and write a lab report.

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Table of contents

Structuring a lab report, introduction, other interesting articles, frequently asked questions about lab reports.

The sections of a lab report can vary between scientific fields and course requirements, but they usually contain the purpose, methods, and findings of a lab experiment .

Each section of a lab report has its own purpose.

• Title: expresses the topic of your study
• Abstract : summarizes your research aims, methods, results, and conclusions
• Introduction: establishes the context needed to understand the topic
• Method: describes the materials and procedures used in the experiment
• Results: reports all descriptive and inferential statistical analyses
• Discussion: interprets and evaluates results and identifies limitations
• Conclusion: sums up the main findings of your experiment
• References: list of all sources cited using a specific style (e.g. APA )
• Appendices : contains lengthy materials, procedures, tables or figures

Although most lab reports contain these sections, some sections can be omitted or combined with others. For example, some lab reports contain a brief section on research aims instead of an introduction, and a separate conclusion is not always required.

If you’re not sure, it’s best to check your lab report requirements with your instructor.

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Your title provides the first impression of your lab report – effective titles communicate the topic and/or the findings of your study in specific terms.

Create a title that directly conveys the main focus or purpose of your study. It doesn’t need to be creative or thought-provoking, but it should be informative.

• The effects of varying nitrogen levels on tomato plant height.
• Testing the universality of the McGurk effect.
• Comparing the viscosity of common liquids found in kitchens.

An abstract condenses a lab report into a brief overview of about 150–300 words. It should provide readers with a compact version of the research aims, the methods and materials used, the main results, and the final conclusion.

Think of it as a way of giving readers a preview of your full lab report. Write the abstract last, in the past tense, after you’ve drafted all the other sections of your report, so you’ll be able to succinctly summarize each section.

To write a lab report abstract, use these guiding questions:

• What is the wider context of your study?
• What research question were you trying to answer?
• How did you perform the experiment?
• What did your results show?
• How did you interpret your results?
• What is the importance of your findings?

Nitrogen is a necessary nutrient for high quality plants. Tomatoes, one of the most consumed fruits worldwide, rely on nitrogen for healthy leaves and stems to grow fruit. This experiment tested whether nitrogen levels affected tomato plant height in a controlled setting. It was expected that higher levels of nitrogen fertilizer would yield taller tomato plants.

Levels of nitrogen fertilizer were varied between three groups of tomato plants. The control group did not receive any nitrogen fertilizer, while one experimental group received low levels of nitrogen fertilizer, and a second experimental group received high levels of nitrogen fertilizer. All plants were grown from seeds, and heights were measured 50 days into the experiment.

The effects of nitrogen levels on plant height were tested between groups using an ANOVA. The plants with the highest level of nitrogen fertilizer were the tallest, while the plants with low levels of nitrogen exceeded the control group plants in height. In line with expectations and previous findings, the effects of nitrogen levels on plant height were statistically significant. This study strengthens the importance of nitrogen for tomato plants.

Your lab report introduction should set the scene for your experiment. One way to write your introduction is with a funnel (an inverted triangle) structure:

• Start with the broad, general research topic
• Narrow your topic down your specific study focus
• End with a clear research question

Begin by providing background information on your research topic and explaining why it’s important in a broad real-world or theoretical context. Describe relevant previous research on your topic and note how your study may confirm it or expand it, or fill a gap in the research field.

This lab experiment builds on previous research from Haque, Paul, and Sarker (2011), who demonstrated that tomato plant yield increased at higher levels of nitrogen. However, the present research focuses on plant height as a growth indicator and uses a lab-controlled setting instead.

Next, go into detail on the theoretical basis for your study and describe any directly relevant laws or equations that you’ll be using. State your main research aims and expectations by outlining your hypotheses .

Based on the importance of nitrogen for tomato plants, the primary hypothesis was that the plants with the high levels of nitrogen would grow the tallest. The secondary hypothesis was that plants with low levels of nitrogen would grow taller than plants with no nitrogen.

Your introduction doesn’t need to be long, but you may need to organize it into a few paragraphs or with subheadings such as “Research Context” or “Research Aims.”

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A lab report Method section details the steps you took to gather and analyze data. Give enough detail so that others can follow or evaluate your procedures. Write this section in the past tense. If you need to include any long lists of procedural steps or materials, place them in the Appendices section but refer to them in the text here.

You should describe your experimental design, your subjects, materials, and specific procedures used for data collection and analysis.

Experimental design

Briefly note whether your experiment is a within-subjects  or between-subjects design, and describe how your sample units were assigned to conditions if relevant.

A between-subjects design with three groups of tomato plants was used. The control group did not receive any nitrogen fertilizer. The first experimental group received a low level of nitrogen fertilizer, while the second experimental group received a high level of nitrogen fertilizer.

Describe human subjects in terms of demographic characteristics, and animal or plant subjects in terms of genetic background. Note the total number of subjects as well as the number of subjects per condition or per group. You should also state how you recruited subjects for your study.

List the equipment or materials you used to gather data and state the model names for any specialized equipment.

List of materials

35 Tomato seeds

15 plant pots (15 cm tall)

Light lamps (50,000 lux)

Nitrogen fertilizer

Measuring tape

Describe your experimental settings and conditions in detail. You can provide labelled diagrams or images of the exact set-up necessary for experimental equipment. State how extraneous variables were controlled through restriction or by fixing them at a certain level (e.g., keeping the lab at room temperature).

Light levels were fixed throughout the experiment, and the plants were exposed to 12 hours of light a day. Temperature was restricted to between 23 and 25℃. The pH and carbon levels of the soil were also held constant throughout the experiment as these variables could influence plant height. The plants were grown in rooms free of insects or other pests, and they were spaced out adequately.

Your experimental procedure should describe the exact steps you took to gather data in chronological order. You’ll need to provide enough information so that someone else can replicate your procedure, but you should also be concise. Place detailed information in the appendices where appropriate.

In a lab experiment, you’ll often closely follow a lab manual to gather data. Some instructors will allow you to simply reference the manual and state whether you changed any steps based on practical considerations. Other instructors may want you to rewrite the lab manual procedures as complete sentences in coherent paragraphs, while noting any changes to the steps that you applied in practice.

If you’re performing extensive data analysis, be sure to state your planned analysis methods as well. This includes the types of tests you’ll perform and any programs or software you’ll use for calculations (if relevant).

First, tomato seeds were sown in wooden flats containing soil about 2 cm below the surface. Each seed was kept 3-5 cm apart. The flats were covered to keep the soil moist until germination. The seedlings were removed and transplanted to pots 8 days later, with a maximum of 2 plants to a pot. Each pot was watered once a day to keep the soil moist.

The nitrogen fertilizer treatment was applied to the plant pots 12 days after transplantation. The control group received no treatment, while the first experimental group received a low concentration, and the second experimental group received a high concentration. There were 5 pots in each group, and each plant pot was labelled to indicate the group the plants belonged to.

50 days after the start of the experiment, plant height was measured for all plants. A measuring tape was used to record the length of the plant from ground level to the top of the tallest leaf.

In your results section, you should report the results of any statistical analysis procedures that you undertook. You should clearly state how the results of statistical tests support or refute your initial hypotheses.

The main results to report include:

• any descriptive statistics
• statistical test results
• the significance of the test results
• estimates of standard error or confidence intervals

The mean heights of the plants in the control group, low nitrogen group, and high nitrogen groups were 20.3, 25.1, and 29.6 cm respectively. A one-way ANOVA was applied to calculate the effect of nitrogen fertilizer level on plant height. The results demonstrated statistically significant ( p = .03) height differences between groups.

Next, post-hoc tests were performed to assess the primary and secondary hypotheses. In support of the primary hypothesis, the high nitrogen group plants were significantly taller than the low nitrogen group and the control group plants. Similarly, the results supported the secondary hypothesis: the low nitrogen plants were taller than the control group plants.

These results can be reported in the text or in tables and figures. Use text for highlighting a few key results, but present large sets of numbers in tables, or show relationships between variables with graphs.

You should also include sample calculations in the Results section for complex experiments. For each sample calculation, provide a brief description of what it does and use clear symbols. Present your raw data in the Appendices section and refer to it to highlight any outliers or trends.

The Discussion section will help demonstrate your understanding of the experimental process and your critical thinking skills.

In this section, you can:

• Interpret your results
• Compare your findings with your expectations
• Identify any sources of experimental error
• Explain any unexpected results
• Suggest possible improvements for further studies

Interpreting your results involves clarifying how your results help you answer your main research question. Report whether your results support your hypotheses.

• Did you measure what you sought out to measure?
• Were your analysis procedures appropriate for this type of data?

Compare your findings with other research and explain any key differences in findings.

• Are your results in line with those from previous studies or your classmates’ results? Why or why not?

An effective Discussion section will also highlight the strengths and limitations of a study.

• Did you have high internal validity or reliability?
• How did you establish these aspects of your study?

When describing limitations, use specific examples. For example, if random error contributed substantially to the measurements in your study, state the particular sources of error (e.g., imprecise apparatus) and explain ways to improve them.

The results support the hypothesis that nitrogen levels affect plant height, with increasing levels producing taller plants. These statistically significant results are taken together with previous research to support the importance of nitrogen as a nutrient for tomato plant growth.

However, unlike previous studies, this study focused on plant height as an indicator of plant growth in the present experiment. Importantly, plant height may not always reflect plant health or fruit yield, so measuring other indicators would have strengthened the study findings.

Another limitation of the study is the plant height measurement technique, as the measuring tape was not suitable for plants with extreme curvature. Future studies may focus on measuring plant height in different ways.

The main strengths of this study were the controls for extraneous variables, such as pH and carbon levels of the soil. All other factors that could affect plant height were tightly controlled to isolate the effects of nitrogen levels, resulting in high internal validity for this study.

Your conclusion should be the final section of your lab report. Here, you’ll summarize the findings of your experiment, with a brief overview of the strengths and limitations, and implications of your study for further research.

Some lab reports may omit a Conclusion section because it overlaps with the Discussion section, but you should check with your instructor before doing so.

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A lab report conveys the aim, methods, results, and conclusions of a scientific experiment . Lab reports are commonly assigned in science, technology, engineering, and mathematics (STEM) fields.

The purpose of a lab report is to demonstrate your understanding of the scientific method with a hands-on lab experiment. Course instructors will often provide you with an experimental design and procedure. Your task is to write up how you actually performed the experiment and evaluate the outcome.

In contrast, a research paper requires you to independently develop an original argument. It involves more in-depth research and interpretation of sources and data.

A lab report is usually shorter than a research paper.

The sections of a lab report can vary between scientific fields and course requirements, but it usually contains the following:

• Abstract: summarizes your research aims, methods, results, and conclusions
• References: list of all sources cited using a specific style (e.g. APA)
• Appendices: contains lengthy materials, procedures, tables or figures

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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• Prof. Jing-Ke Weng
• Dr. Nelly Cruz

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Experimental molecular genetics, assignments, data analysis and interpretation, technical skill, quality of effort, and lab notebook.

The greatest part of your final grade will be based on how you perform in the laboratory. You need to demonstrate a basic level of competence and invest genuine effort towards research progress. We do not expect that all your experiments will be successful, but we do expect that you will have carried them out in a rigorous, well-controlled, and well-documented manner, and have made an effort to understand why an experiment did not work. Not all students enter this course with the same level of laboratory research experience. The teaching staff recognizes this and encourages you to take advantage of this course to improve your skills and learn new ones. Your efforts to extend your knowledge beyond the immediate need of the experiment will be reflected favorably in the grade for this area.

You must keep a physical lab notebook. It is an important tool and key to good experimental practice. The notebook should be a complete record of all the experiments as they were actually performed. Experimental results, tables, images, etc. should be included in your notebook for a permanent record. A good notebook will enable someone to reconstruct, long after the fact, exactly what was done and why. In addition to the physical lab book, it is recommended that you also keep all or part of the experimental information digitally. Digital notebook is a growing trend in experimental research, and is beneficial for many obvious reasons, such as searching, replicating repetitive procedures, and data sharing. Software, such as Microsoft Word or OneNote, can be used. Tips about maintaining a complete and useful digital notebook in addition to the physical notebook will be given.

Oral Presentations and Participation

We will meet weekly to discuss research results (group meetings) or primary literature (journal club), as well as to have each student present a final research talk. Guidelines on what is expected for each of these presentations will be provided in the course.

Journal Club

Starting from week 3 (session 11), we will dedicate the first hour of one afternoon each week to a journal club. Each student will lead two journal clubs for a total of 8 journal clubs for the semester. For each journal club, there will be one main paper, which is to be discussed in detail, along with one or two additional papers, which contain background studies or extended research related to the main paper. The list of papers can be found in the Readings section.

Serving as the discussion leader of a journal club, you’re expected to prepare a powerpoint presentation to walk your audience through the paper.

Written Assignments

Written communication has been the mode of disseminating scientific achievements and new knowledge for centuries. Improving your skills in scientific writing is, therefore, a critical component of this course. The technical writing instructor will be the primary instructor for the writing / communications section of the course, although other members of the teaching staff will read your drafts and final assignments and provide comments and feedback. Throughout the semester Dr. Pepper will lead lectures / workshops and discussions about scientific writing. You will submit a number of writing assignments. The purpose of such arrangement is to work on various parts of a typical scientific paper at different time points during the semester, i.e. Introduction, materials and methods, results, and discussion. These parts then can be integrated into a complete paper on your particular research project towards the end of the semester. Please note that written assignments must be turned in on time. Late assignments will not be graded and you will receive a 0 for a missed assignment. However, we will read late assignments if you would like to have our feedback.

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Engaging Students with Experimentation in an Introductory Biology Laboratory Module

• First Online: 12 May 2022

Cite this chapter

• Annwesa Dasgupta 21 ,
• Swapnalee Sarmah 22 ,
• James A. Marrs 22 &
• Kathleen A. Marrs 22  

Part of the book series: Contributions from Biology Education Research ((CBER))

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Gaining practice in experimental design is a key skill for undergraduates, particularly science majors who have interests in research. To engage students in the process of experimental design, including using visualizations, we developed a laboratory module for a large enrollment introductory biology laboratory course using zebrafish as an experimental organism. This laboratory module focused on familiarizing students with scientific literature and developing biology competencies such as Identify a problem, ask a Question and formulate a hypothesis, Conduct and execute their research Plan , Analyze data and interpret results, and finally Communicate findings to different audiences. Assessing the outcomes of and learning gains from such a module requires deliberate selection of an assessment instrument that can engage students in performing an experimental design. Here, we provide a framework with practical considerations to guide faculty in developing a course based experimental design module, highlighting specific validated assessment tools to provide guidance to students as they address specific design constraints.

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Acknowledgments

This work was supported by a SEIRI Seed grant from IUPUI to Dr. Kathleen Marrs and Dr. James Marrs (2016-2019).

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Dasgupta, A., Sarmah, S., Marrs, J.A., Marrs, K.A. (2022). Engaging Students with Experimentation in an Introductory Biology Laboratory Module. In: Pelaez, N.J., Gardner, S.M., Anderson, T.R. (eds) Trends in Teaching Experimentation in the Life Sciences. Contributions from Biology Education Research. Springer, Cham. https://doi.org/10.1007/978-3-030-98592-9_13

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