Tracking academic buoyancy after embedding a transition to university learning component into a first-year calculus sequence.
Bibliographic record
Abstract
Not knowing how university learning is different from high-school learning often introduces challenges that can have a negative effect on first-year student wellness [1]. One alternative to help students develop the required learning skills is to embed this content into regular first-year courses [2]. We deployed screencasts on transition to university learning and student wellness (previously developed by Ostafichuk [3]) in a first-year calculus sequence for engineering students, and measured student academic buoyancy through the yearlong intervention [4]. Our aim was to investigate whether academic buoyancy increased through the year, and whether watching the screencasts correlated with any increases in academic buoyancy. Results show that student academic buoyancy increased through the year. The increase was statistically significant and had a large effect size for students who completed all three surveys during this period. The increase was not statistically significant and had a small effect size for students who completed any two surveys, but our analysis suggests this increase was not by chance. Although the intervention was well-received by students, our data did not show a correlation between the intervention and the increase in academic buoyancy. Limitations of this study include a small sample size, and our academic buoyancy data having been collected during the 2020-2021 remote learning year.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".