Bibliographic record
Abstract
The Academic Enhancement Program (AEP) aims to help students succeed in their post-secondary studies by incorporating learning strategies and academic reflection activities into core first-year Computing Science (CS). Initially offered in a single CS course at our institution, the AEP has since been run as a required component in several CS courses. It has also been adapted collaboratively in other universities, and is customizable to other disciplines with plans to expand into other departments. We have regularly evaluated AEP based on students', academic advisors' and instructors' perceptions to support the continual improvement of the program. The current study relied on both students' self-perception and course performance data in two sections of the same course, with the same instructor, contents, and exams, where only students in one section participated in the program. Employing linear regression, this study sought to determine what non-trivial factors account for student success, measured by final exam scores. We determined that 22% of the variance in student success measured by final exam scores can be accounted for by basis of admission, admission GPA, number of credits registered in, and a newly defined construct embodying student CS programming background experience. While our model did not include AEP as a predictive factor, we are encouraged that 77% of AEP participants said that they would continue to use study skills learned in AEP in future courses. Implications for further investigation of learning support programs are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".