Predicting Success in University First Year Computing Science Courses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it