Nudging student learning strategies using formative feedback in automatically graded assessments
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
Automated assessment tools are widely used as a means for providing formative feedback to undergraduate students in computer science courses while helping those courses simultaneously scale to meet student demand. While formative feedback is a laudable goal, we have observed many students trying to debug their solutions into existence using only the feedback given, while losing context of the learning goals intended by the course staff. In this paper, we detail two case studies from second and third-year undergraduate software engineering courses indicating that using only nudges about where students should focus their efforts can improve how they act on generated feedback. By carefully reasoning about errors uncovered by our automated assessment approaches, we have been able to create feedback for students that helps them to revisit the learning outcomes for the assignment or course. This approach has been applied to both multiple-choice feedback in an online quiz taking system and automated assessment of student programming tasks. We have found that student performance has not suffered and that students reflect positively about how they investigate automated assessment failures.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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