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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.031 |
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".