Effects of a Fall Reading Break on First Year Students' Course Performance in Programming
Bibliographic record
Abstract
This paper presents a mixed methods study into the effects of a fall break on course performance in a first semester programming course in Mechatronics Engineering at the University of Waterloo.In 2016, the University of Waterloo instituted a two-day fall break immediately following Thanksgiving Monday, on a three-year pilot. The stated rationale for this break was to address student wellness and mental health issues, especially as this pertains to students transitioning from high school and their “looming midterms”. As of October 2017, there are now 20 institutions in Ontario with a fall break of between one five days in length after the Thanksgiving holiday.A linear regression model was calculated to examine the impact of the fall break on students. This model predicts students who regretted how they spent the fall break will earn 6% less in their first programming course. A logistic regression model was calculated which predicted inexperienced, struggling students have the highest odds of regretting how they spent the break.Three focus groups were conducted with students who experienced the fall break in fall of 2016 or 2017. These focus groups examined student perceptions of the fall break, how they recalled using their time during the break, and their reflections on the br
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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 | 0.001 |
| 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 | 0.000 |
| 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".