Preliminary Results Of A Longitudinal Study Into The Academic Success Of Students In Technology Focused Vs. Humanities Programs
Bibliographic record
Abstract
Attrition rates in junior years of technology-focused programs are much higher than in humanities.As well, in recent years technology-focused programs have been experiencing drops in enrollment, and difficulties in attracting qualified candidates, while admissions to other programs seem unaffected.Such trends are worrying and thus we, as educators, need to improve efforts to better understand our students that would in turn allow the university to better plan and tailor their student success, retention and recruitment programs.This paper reports on the background and initial hypotheses as well as on the first survey results of a new longitudinal study which is intended to provide insight into retention issues, including an investigation of a "filtering effect" of the traditional instruction that the authors hypothesize is taking place and is partly responsible for high dropout rates, as well as for the reduced diversity of the student body as they progress through the technology-focused versus humanities programs.The study will also provide recommendations to improve student engagement and success.
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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 | 0.011 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".