Developing A Data Driven Framework To Improve Feedback For Educators
Bibliographic record
Abstract
This paper presents a framework for the use of weekly reflections to drive iterative course improvement throughout a term. This data includes weekly student reflections, where students anonymously self-assess both their behavior (through attendance and use of course resources) and their comprehension of course concepts. Results from a pilot of this framework for a large first-year introduction to computer programming course are used to illustrate the use case. Improvements to this framework from a previous term are made and discussed. Results demonstrate student reflections can offer actionable insights for targeted interventions in lectures and tutorials. Key observations include the impact of incoming experience, patterns of students falling behind, and differences across cohorts. By automating a portion of this data collection and analysis, student feedback can be integrated with minimal administrative burden. The authors plan to further automate this framework and develop predictive models based on specific course factors.
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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 | 0.138 | 0.261 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".