A Tale of Two Classes: Student and Instructor Perceptions of Two-Stage Tutorials in Introductory Genetics Classes
Bibliographic record
Abstract
Two-stage exams are those in which students first write a test independently and then, immediately after, write the same, or similar, exam as small groups, effectively teaching and learning from each other. This approach has been reported to improve students’ performance on subsequent individual tests, encourages a collaborative approach to problem solving, and turns exams into learning experiences (e.g., Gilley & Clarkston, 2014). To enhance genetics problem solving skills of undergraduate students, the second year genetics classes at the University of Toronto Mississauga (n=440) and York University (n=250) were redesigned to incorporate not only two-stage exams, but also a two-stage model adapted for both our in-class activities (i.e., Peer Instruction) and course-associated tutorials. Here we report on our studies on repurposing of the two-stage exam approach for tutorials to encourage collaborative learning and problem solving. In tutorials, students completed a short problem set assignment (2 to 3 questions, related to the previous week’s material) on their own, followed by completion of the same, or a more challenging problem set, in groups of 3 to 4. In this session, we will provide a discussion of the relevant research on two-stage exams, describe and model the set-up of these tutorials at our respective institutions, and explore student* and instructor perceptions, including those of TAs, as well as lessons learned. We welcome participants’ input, discussion, and feedback to help improve the use of two-stage tutorials in future.\n*IRB-approval to collect and share perceptions of this implementation.\nReferences: Gilley BH and Clarkston B. 2014. Collaborative testing: evidence of learning in a controlled in-class study of undergraduate students. Journal of College Science Teaching 43(3): 83-91.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".