ONE-A-DAY PROBLEMS FOR IMPROVING STUDENT LEARNING AND STUDY HABITS
Bibliographic record
Abstract
At the University of Waterloo, Mechatronics Engineering students take their first programming course in their first academic term. In 2016, Waterloo introduced a two-day long fall break immediately following the Thanksgiving weekend. The fall break back-to-back with their midterm week means students have as many as 18 days between programming lectures. The breaks also interrupt the schedule of weekly assignments that provide students’ primary means of practicing programming. In an attempt to mitigate any negative effects of the break on those students who are not experienced programmers and may not know how to use their time effectively, "One-a-Day Problems" were tried. Students were expected to work on that one problem for the day, which was expected to take roughly 30-minutes to complete, and were encouraged to contact the instructor or other members of the teaching team with any questions or concerns. The problems remained available on the LMS throughout the term and no solutions to these problems were posted on the LMS. Students enjoyed receiving extra practice problems using this format, and engaging with these questions resulted in higher performance on both the midterm and final exam. Engagement with the problems was lower than desired, however, especially with students with no prior programming experience.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".