Using Peer Instruction Pedagogy for Teaching Dynamics: Lessons Learned from Pre-Class Reading Quizzes
Bibliographic record
Abstract
Peer Instruction (PI) is a widely used pedagogy which generally includes the use of two main teaching strategies: student pre-class preparation with an associated online quiz, and active in-class engagement including small-group discussions about conceptual questions. As an instructor trying this pedagogy for the first time, my purpose was to investigate both students’ learning and attitudes in my first/second year engineering dynamics course, using their answers to the reading quizzes as the main source of data. In short, students with the highest quiz marks did well in the course, indicating successful reading and learning strategies. Similarly, students with the lowest quiz marks attained lower overall marks. Students who did less well in the course were also more negative about the PI format (the class size of 17 did not allow for statistical analysis). Negative comments tended to be related to an expectation that the teacher should lecture more, indicating less understanding of cognitive principles. These results will provide a baseline for evaluating future teaching efforts which will include examining whether more directly encouraging deep learning strategies will be more effective for student learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".