Contrasting CS student and academic perspectives and experiences of student engagement
Bibliographic record
Abstract
There is widespread acceptance of the use of national benchmarks to measure student engagement, including the North American National Survey of Student Engagement (NSSE) in the USA and Canada, the Student Experience Survey (SES) in Australia, and the United Kingdom Engagement Survey (UKES). The performance of Computer Science (CS) on these benchmarks has generally been poor over a number of years and is consistently low across a range of instruments with little sign of improvement. It is difficult to argue that the technical nature of the CS discipline is the issue as related STEM disciplines consistently rate higher on many measures. Given the deteriorating performance of CS across multiple student engagement instruments, the urgency of addressing this issue is increasing. Missing from computing education research on this issue to date is the CS student voice and a deeper understanding of why CS students rate their experience so poorly. It is essential to seek the perspectives of both sides of the dialogue primarily responsible for creating the student experience. We carried out an in-depth analysis of student perspectives and experiences relating to their engagement in CS courses and compared it to the perspectives and experiences of CS academics. The outcome of this Working Group was a better understanding of areas of difference between CS students and academics on: what constitutes student engagement; who is responsible for student engagement; examples of both positive and negative engagement experiences in the classroom; and current initiatives to improve student engagement in their CS courses.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".