Predicting Success in University First Year Computing Science Courses
Bibliographic record
Abstract
Educators find that many students have difficulty succeeding in first-year university Computing Science (CS) courses. Initiatives are pursued to address this challenge and to support students' academic success. Instructors and institutions have reported providing different forms of academic support with programs where learning strategies are discussed with students, such as the Academic Enhancement Program (AEP). The AEP is a student focused proactive intervention developed and run by the School of Computing Science and the Student Learning Commons at Simon Fraser University, providing opportunities for self-reflection and exposure to study strategies activities, incorporated within and tailored to selected first year CS university courses, since 2006. To further enhance the students' learning experience, instructors also incorporate novel activities in class, such as peer instruction and active learning aided with the use of audience response systems (i-clickers). Experimental studies to determine whether the incorporation of these activities in a course cause a variation in some outcome measures (such as final exam scores) may be not feasible to do. In this paper we present instead results from performing statistical studies on course evaluation data, which even if they cannot prove causality, they may allow to determine if these activities are statistically significant predictors of course 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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".