Understanding International Benchmarks on Student Engagement
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
There is an increasing trend to use national benchmarks to measure student engagement, with instruments such as North American National Survey of Student Engagement (NSSE) in the USA and Canada, Student Experience Survey (SES) in Australia and NZ (previously known as the University Experience Survey UES), and Student Engagement Survey (SES) in the UK. Unfortunately, Computer Science (CS) rates fairly poorly on a number of measures in these surveys, even when compared to related STEM disciplines. Initial research suggests reasons for this poor performance may include a lack of awareness by CS academics of these instruments and the student engagement measures they are based on, and a misalignment between these instruments and the research focus of computing educators, leading to misdirected efforts in research and teaching practice. In this working group we carry out an in-depth analysis of international student engagement instruments to facilitate a greater awareness of the international benchmarks and what aspects of student engagement they measure. The working group also examine the focus of current computing education research and its alignment to student engagement measures on which these instruments are based. Armed with this knowledge, the computing education community can make informed decisions on how best to respond to these measures and consider ways to improve our performance in relation to other disciplines. In particular it is important to understand why certain measures of student engagement are built into these instruments, how these align to our current research practice or even to provide feedback to the designers of these instruments from a CS perspective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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