Users Perform Better in a First-Year Science Course: Assessment, Learning and Well-Being in the Undergraduate Context
Bibliographic record
Abstract
The secondary to post-secondary transition is of critical importance for student learning and well-being. This transition is examined in the context of an undergraduate science course at a large Canadian university. A survey was completed by 20 students to assess different components of their mental health, as well as the study strategies they employ. A semi-structured interview that included a Q-sort activity was conducted following the survey. 12 students’ grades were included in the analysis (60%). A strong negative correlation was found between anxiety and task performance (r = -0.697, p = 0.012). Results from a Mann-Whitney U test found that students who preferentially studied using the repetition of information (e.g., flashcards, re-reading), a strategy termed rehearsal, had significantly higher task grades (p = 0.029) than those who didn’t. Themes that arose from the interviews echoed those found in similar studies: grouping assessments within close proximity and in a cumulative format contributes to assessment anxiety, and remote assessment formats include both positive and negative effects. Preliminary findings highlight the often overlooked benefits of rehearsal learning when used in the appropriate context. Increased sample size and a categorization scheme that better describes different learner profiles should be sought in future studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".