Eye Tracking the Feedback Assigned to Undergraduate Students in a Digital Assessment Game
Bibliographic record
Abstract
High-quality feedback exerts a crucial influence on learning new skills and it is one of the most common psychological interventions. However, knowing how to deliver feedback effectively is challenging for educators in both traditional and online classroom environments. This study uses psychophysiological methodology to investigate attention allocation to different feedback valences (i.e., positive and negative feedback), as the eye tracker provides accurate information about individuals’ locus of attention when they process feedback. We collected learning analytics via a behavioral assessment game and eye-movement measures via an eye tracker to infer undergraduate students’ cognitive processing of feedback that is assigned to them after completing a task. The eye movements of n = 24 undergraduates at a North American university were tracked by the EyeLink 1000 Plus eye tracker while they played Posterlet, a digital game-based assessment. In Posterlet, students designed three posters and received critical (negative) or confirmatory (positive) feedback from virtual characters in the game after completing each poster. Analyses showed that students attended to critical feedback more than to confirmatory feedback, as measured by the time spent on feedback in total, per word, and per letter, and by the amount of feedback revisits. The study summarizes the eye movement record on critical and confirmatory feedback, respectively. Implications of this research include enhancing our understanding of the differential temporal cognitive processing of feedback valences that may ultimately improve the delivery of feedback.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".