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Time Optimization
Feedback
Youreka Edmonton will be heading into Session 3 this week and thankfully we can benefit from their challenges and pivot across Canada. The main feedback we have been getting is that the sessions are taking much longer than expected and that the pace of the R labs is too fast for students. We have identified areas where we can improve in order to deal with these points and have included guidelines for implementation below.
Time Optimization
Clicker questions have been identified as a significant time investment in the session. Deployment of questions is estimated to take 60-90 seconds per question with an additional 2-3 minutes spent on going over every answer.Additionally, student feedback suggests that the length of the breakout room is more important for student engagement than the number. Students feel that 5-8 minutes is too short for any meaningful discussion within their groups.
Recommendations
For clicker questions, the pre-questions are strategically places as a segue into a new topic. We do not expect students to know the answer to the question. Please ensure that you stress that students should just guess if they do not know the answer to these questions and implement more stringent time limits (can be done through ClassQuestion directly). A small note on time constraints is that students cannot see the timer on their side of the interface so please be sure to use verbal ques. The idea is that the following slides in the lecture will cover the material in the pre-question so there is no need to go over the answers of a pre-question. As for post-questions at the beginning of the lecture, please reserve discussing the answers to the questions in-depth for questions that were especially difficult for students (i.e. <50% correct, use discretion).
The current model from Edmonton has been 2-3 breakout sessions of 5-8min during the lecture portion and 1 breakout session during the lab. The overwhelming consensus is that 5-8 min is not enough. This is especially the case for Session 2 where groups have to cover multi-part questions. We recommend decreasing the frequency of the breakout sessions to 1-2 maximum but increasing the time to 10-12 min. Alternatively, you could assign specific groups aspects of a larger question and have them discuss smaller pieces for the original 5min. Both of these strategies should help optimize the time in the session. We highly recommend, having a hard 90 min cut-off at which point groups can get together and discuss their projects.
R Lab Comprehension
Feedback also indicates that the pace of the R labs is too fast for some students. With the additional time constraints, instructors have had to either rush through segments of the lab or not cover certain sections. Additionally, students are expressing anxiety over having to absorb an entire language on a short timeline, memorizing commands, and do not yet understand the application of R within the context of their projects since we have not started the statistics block of the program yet.
Recommendations
Be sure to stress to students that the approach to R is to get an appreciation for the tool during the lab but most of their learning will be through applying those concepts in their analyses later on in the program. The textbook and the additional R resources provided will always be at their finger-tips for reference throughout that application process.Take a few minutes to cover the R documentation commands as well as the R cheatsheets built into RStudio (help/Cheatsheets…). We will release a supplementary video on that shortly however, please take the time to familiarize yourself and your students with these tools and encourage their use.
Contextualize the use of R within a project timeline either through your personal experience or through highlighting some of the topics that will be covered in later sessions. The confusion with the relevance of R is likely because the first two sessions have focused on scientific fundamentals rather than analyses. Once we dive into the statistics and the session content is better aligned with the lab content, these concerns will be alleviated. However, to avoid early-onset apathy, take a few minutes to contextualize why R is important to their projects and to science as a whole.