Assessing Whether Students Seek Constructive Criticism: The Design of an Automated Feedback System for a Graphic Design Task
Bibliographic record
Abstract
We introduce a choice-based assessment strategy that measures students’ choices to seek constructive feedback and to revise their work. We present the feedback system of a game we designed to assess whether students choose positive or negative feedback and choose to revise their posters in the context of a poster design task, where they learn graphic design principles from feedback. We then describe an empirical study that sampled one hundred and six students from a US middle school to evaluate the feedback system. We make the following contributions: (1) describe the design and implementation of a novel feedback system embedded in an assessment game, Posterlet, (2) outline an approach to analyze graphic design principles automatically to provide contextual feedback in a novel poster design domain, (3) show that choices to seek negative feedback and to revise correlate with in-game performance, and most importantly, (4) show that choices correlate with in-school achievement: the choice to revise correlated with both in-school performance measures (Science and Mathematics grades), while the choice to seek negative feedback correlated with students’ prior standardized scores in Mathematics.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".