Seven lessons I learned in my quest for transforming my teaching (draft, version: 0.1)

(N.B. — This is a work in progress, and the final conclusions/final form of the article may be different)

Last year, I had two revelations — first, that I could use a workshop style delivery of lecture with active learning from the students and teach any topic I wanted that way; second — I could teach without worrying about grading at all, I could go gradeless. This year, I wanted to put both these ideas in the space of one course on research methods I teach at the university and tried to test how it would be if I were to apply both these lessons to my teaching practice. This effort is still going on, and therefore this is a work in progress.

What I changed

Two things:

  1. Gradeless grading. — I have three assignments in the course. The course is about research methods in health sciences for postgraduate students; the classes are held in a “block format”: the students attend three blocks of two days each (9AM — 3PM each day per block). As part of the course, the students will have to come up with a research proposal on a topic of their choice. The first of the three assignments is about critical thinking, where they write a draft of a research idea where they outline their topic of choice, review current knowledge, identify the gaps in the current knowledge, and provide an outline of what is new in their work and propose a line of investigation. The critical thinking component draws on a text by Tom Chatfield “Critical Thinking”. In the second assignment, I teach the students data analysis and development of surveys (survey design) and how to validate surveys and find validity and reliability of surveys. I also teach them how to use the free and open source statistical programming environment R, using the tidyverse package and teach them data science using R. This draws on the work by Hadley Wickham and the gitbook contents “Data Science in R”. The graphs are drawn on ggplot2. You can see the interactive python notebook I use for the second component here. I used the codes in the interactive python but demonstrated and worked with the students using Rstudio. This was done so that they could work on the codes in a different environment and practice after the class was over.

I built these three features in the modules I taught in the course. The live coding was straightforward for teaching programming R; for designing surveys, I worked along side the students using the hackmd.io document, and similarly worked on other examples during the discussion on critical reflection. Repeated feedbacks were based on using red and white post-it notes in the class where the students indicated how they were learning; also using questions and answers. Faded examples were used in teaching R and dplyr and functions.

The lessons I learned

  1. Need to arrange for backup computers when needed. — I emailed all students a week ahead of class asking them to bring their laptops to the class. Some brought, others didn’t. I had to arrange for additional and backup computers. Again, while some brought computers to the class, these computers were not all the same. Some had old Windows machines and some had Macbook Airs that needed some handholding.

Associate Professor of Epidemiology and Environmental Health at the University of Canterbury, New Zealand. Also in: https://refind.com/arinbasu

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