Curriculum Development

Curriculum Development Edward Bartlett

Our program goal three is to prepare undergraduate students for 21st century careers and/or graduate education in quantitative biology by infusing experimental and quantitative design into existing biology curricula. Our efforts will affect three populations of students as we reform existing coursework and initiate new courses that better integrate experimental design and quantitative analysis


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We are live on STEMEdHub! Please visit ( to check out some of our developed modules, see how they can be accessed, and if desired, download and use the modules.

HHMI module list

Author: Jim Forney

Summary: This lesson is part of a sophomore level biochemistry course to introduce the concepts of experimental design. Students are provided with the description of an experiment designed to examine the effect of an electric field on bacterial growth. They are given initial observations from the experiment (inhibition of growth and an elongation phenotype for the bacteria) and asked to consider control experiments to evaluate whether the electric field is directly responsible for the effect. Students are prompted with questions then work in groups and share ideas. Ultimately it is revealed that platinum in the wires reacts to form cisplatin compounds that prevent cell division. Next the instructor explains that ciplatin reacts covalently with DNA and causes a bend or kink at the site of attachment. This led to the isolation of proteins that preferentially bind cisplatin modified DNA. Quantitative analysis of DNA-protein binding is presented to evaluate the affinity of proteins for unmodified and ciplatin modified DNA. Students are provided data from multiple binding trials and asked to evaluate the data. Students discuss and share ideas in class. Averages and standard deviations are discussed and calculated. Finally, data is presented from published studies that include averages and standard deviations. The lesson is designed to reinforce the concepts of control groups, quantitative analysis, statistical variation and display of data.

Authors: Ann Rundell and Kate Stuart

Summary: This module is intended to refresh or introduce students to aspects of experimental design, enhance their understanding that the experiment design should support appropriate statistical analysis of the data, and promote the students' recognition that there is often more than one possible way to design an experiment. This module is easily adaptable for students of any background or any education level. Instructions for presenting the module are included here, an associated power point is available for modification and use, and possible modification and extensions are indicated.

This instructional teaching module was inspired and developed based upon the Experimental Design Assessment Tool (EDAT) developed by Karen Sirum from Bowling Green University and published in Bioscene: Journal of College Biology Teaching (2011). Sirum, K., and J. Humburg (2011). The Experimental Design Ability Test (EDAT). Bioscene: Journal of College Biology Teaching 37(1), 8-16.

Authors: Harmony J. Dalgleish and Nancy Pelaez

Summary: Biological Question: This module can be adapted easily for any biological content. Several figures included in the exercise as potential examples could be replaced by other examples.

Statistical Content: The focus of the module is on interpreting figures from the scientific literature. To do this, students must differentiate between categorical and continuous variables, identify the dependent and independent variable, understand treatments, interpret visual presentation of variation and draw and evaluate conclusions from the data presented.

What students do: Students are introduced to a two-step process for describing and interpreting a figure and are given two acronyms to help them remember the key parts of each step. They are given graphs to practice on in groups of two. After practicing with a partner, students are directed to find their own scientific paper and apply the two-step process to at least one of the figures within the paper. The module can be adapted for any size class – see Faculty Notes for suggestions.

Skills: interpreting graphs, finding and reading scientific literature

Student-active approaches: Working in pairs.

Authors: Harmony Dalgleish and Nancy Pelaez

Summary: The major goals of the module are for students to a) be exposed to the overwhelming importance of size in an animal's life; b) understand the relationships between surface area, volume, and size; and c) see how the relationship between surface area and its volume is fundamental to the operation of many animal systems.The module includes simple calculations of surface area, an introduction to the mathematical relationship between size and heat loss/metabolic rate, and a series of questions exploring the relationship between surface area and organ function. There is also an opportunity to students to extend this understanding to the cellular level in optional additional exercises.

Author: Stephanie Gardner



  1. Biological Question
    • How does the volume of a cell, as measured by weight, change when placed in environments of varying tonicity?
  2. Statistical Content
    • Organizing and summarizing data (cell weights vs. % change in cell weight)
    • Descriptive statistics (median, mean + SD/SEM)
    • Inferential statistics (paired and unpaired T tests, ANOVA)
    • Graphical representations of data
  3. What students do
    • Design hypotheses
    • Make predictions
    • Construct dialysis tubing cells
    • Use a balance
    • Organize, analyze and interpret data
  4. Skills (those developed and practiced in the exercise, including things like writing, computer skills, etc.)
    • Experimental design (formulate hypothesis, make predictions, identify controls, relate findings back to predictions and hypothesis)
    • Practice compiling and organizing data sets
    • Computer skills
    • Use an analytical balance
    • Learn to use data analysis programs (Excel, SPSS, SAS, etc.)
    • Determine the best way to analyze the raw data (% change, descriptive statistics)
    • Create various graphical representations of data
    • Interpret data (graphical representations and the results of inferential statistics)
  5. Student-active approaches (used in the exercise)
    • Students design hypotheses and make predictions
    • Students collect data
    • Students organize, represent, analyze, and interpret their data
    • Students compare graphical representations with each other and perform a peer review (constructive feedback)
    • Students brainstorm about the appropriate components of good graph (these will be used to compile a rubric for assessment of future graphs)
    • Students present their data analysis to the class for guided discussion
  6. Assessable outcomes
    • Depends on the level and experience of the students.
    • After completing this module students will be able to:
      1. Design an experiment (formulate a testable hypothesis, make predictions, incorporate experimental controls, design experimental groups, randomization)
      2. Organize a data set
      3. Perform descriptive statistics (mean, median, SD, SEM, each as appropriate)
      4. Create appropriate graphical representations of their data (tables vs. scatter plots vs. bar graphs vs. line graphs)
      5. Explain the advantages/disadvantages of different graphical representations for this data set
      6. Draw conclusions from their data and use the appropriate scope of inference


Author: Ed Bartlett


  • Biological Question
    • How do organisms detect changes in their environment, and how do scientists and their instruments detect changes in experimental conditions, such as: How does a photoreceptor detect the presence of a photon? How does a doctor detect the presence of a tumor? How does a cell detect the presence of a chemical, such as a morphogen, a chemoattractant, or a nutrient.
  • Statistical Content
    • Basic signal detection: True positive, True Negative, False Positive, False Negative
    • Senstivity vs specificity and tradeoffs
    • d-prime
    • Receiver Operating Characteristic curves
    • Signal+noise
  • What students do?
    • During class, students attempt to detect something, such as a sound or AM modulation in quiet and in noise.
    • Draw signal distributions in those cases
    • Construct table of 4 outcomes
    • Calculate sensitivity, specificity, and d'
    • Make and evaluate decision under different cost scenarios
  • Skills (those developed and practiced in the exercise, including things like writing, computer skills, etc.)
  • Student-active approaches (used in the exercise)
  • Assessable outcomes
    • The students are given an in-class assignment to compute d' values, and they are asked to assess the tradeoffs between sensitivity and specificity. They are re-examined on similar material during a summative assessment.


Author: Nancy Emery

Summary: We were estimating the diversity of two different prairie restoration sites at Prophetstown State Park.

Week 1: critically evaluate the data in a published paper on the impacts of fire on tallgrass prairie plant communities - interpret graphs and figures, and create new graphs with the published data to facilitate different types of comparisons;

Week 2: go into the field and do the same sample design that was implemented in the published paper, but at a different site with features that should lead to different patterns than was found in the published paper if the authors' underlying hypotheses about the mechanisms driving plant community structure were correct;

Week 3: analyze their own data that they collected in Week 2, and compare their results to those in the paper examined in Week 1

Week 4: explore the implications of their results and the broader social and economic challenges associated with tallgrass prairie restoration and management.


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