Memorization is repugnant

I hate memorizing things. I may be good at it, but so what? This sentiment is shared by many, including many undergraduate students. Why then do we expect students to memorize a set of facts and figures only to regurgitate them on exams? Cramming (and then immediately forgetting) is a good strategy to succeed in such a course, but is useless for future application of that knowledge.

This is becoming increasingly true as we move into such a connected digital age. Wikipedia is a few clicks (or an “OK Google Now”) away with the details you may want. What students need in such an era are the tools to know what to look for, how to separate good information from bad, and how to then apply that knowledge.

For these reasons, I am working hard to make the way I teach comport with this philosophy. In my statistics courses, all of my exams were open-book, open-note, open-internet. Instead of testing whether or not students can memorize formulas, I tested whether or not they can apply them.

My Biology courses questions (particularly essay questions) focused on the application of knowledge, rather than a re-statement of facts. This was supported by the active learning approach I take in the classroom.

Active learning

People learn by doing. This is no longer a contentious claim, and it is backed up by a large body of research. This approach is not universally applicable, and there are certainly cases and disciplines where it is not appropriate. However, the trick now is to find ways of implementing this approach, generally called “active learning” in the cases where it is appropriate.

I had great support in doing this at both Viterbo and Juniata, as many of the faculty had already developed many active learning approaches. I took much of what I learned from them and applyed it in my own classrooms.

For example, I flipped the intro stats course that I teach for STEM majors. The notes that I used for this course are available online. These notes allow students to self-pace through the basic commands that they need to use, freeing classroom time for focusing on the bigger-picture and for working problems. This approach worked wonderfully, with students entering their upper-division STEM courses with the skills, and confidence, to conduct and understand statistical analyses.

In the bioinformatics course that I taught at Juniata College, we spent the first half of the course learning skills (by solving small problems) and the second half of the semester on a research project applying those skills. The manual that I used for that course, including several updates from when I taught using it for the GCAT-SEEK workshops, is available online and is more thoroughly described in a publication at CourseSource.

For the genetics lab that I aught, I developed a simulator that allows students to set up crosses between various types of dragons and observe the results. This allows them to conduct a large number of crosses in a short amount of time. This should help them to develop reliable genetic experiments for use on real organisms (e.g. Drosophila or FastPlants), and helps to shape their thinking.

Many of the other interactive teaching resources that I used in my classrooms are available on the shiny page.

Course materials

Below are a few of the course materials that I have developed and am sharing for broader use. Note that the hosting may change, particularly as I continue to polish each of these items.

  • Interactive apps
    • Many of the active learning modules I have developed utlizing R Shiny
    • The link above will take you to a complete list, along with links directly to each.
  • Lecture notes for learning R
    • Developed for Math 230 – Elements of Statistics
    • The first chapter covers the basics of installing R and loading data
    • Chapters 2 – 7 work through examples of plotting various types of data
    • Chapter 28 explains how to do a t-test in R (and proportion tests)
    • Chapters 29 – 33 work through other statistical tests (such as linear regression and ANOVA)
    • The remaining chapters are more about teaching the statistics behind the tests, which may be somewhat less useful for learning R though it includes some additional tips
    • Note, I will continue updating these notes, however the basics should stay roughly the same (though the chapter order/numbering is likely to change)
  • Teaching RNAseq to undergraduates
    • Developed for teaching a bioinformatics undergraduate class, and for teaching the GCAT-SEEK summer workshops.
    • Includes a complete (PDF) manual working through an RNAseq analysis for a novice user, along with links to some teaching materials.
    • More complete description is available in the publication on the project:
      • Peterson, MP; Malloy, JT*; Buonaccorsi, VP; Marden, JH. Teaching RNAseq at undergraduate institutions: A tutorial and R package from the Genome Consortium for Active Teaching. 2015. CourseSource, Vol 2. *Undergraduate co-author. link