Improving Student Feedback Literacy in e-Assessments: A Framework for the Higher Education Context
Bibliographic record
Abstract
For students, feedback received from their instructors can make a big difference in their learning by translating their assessment performance into future learning opportunities. To date, researchers have proposed various feedback literacy frameworks, which concern one’s ability to interpret and use feedback for their learning, to promote students’ feedback engagement by repositioning them as active participants in the learning process. However, the current feedback literacy frameworks have not been adapted to digital or e-Assessment settings despite the increasing use of e-Assessments (e.g., computer-based tests, intelligent tutoring systems) in practice. To address this gap, this conceptual paper introduces a feedback literacy model in the context of e-Assessments to present an intersection between e-Assessment features and the ecological model of feedback literacy for more effective feedback practices in digital learning environments. This paper could serve as a guideline to improve feedback effectiveness and its perceived value in e-Assessment to enhance student feedback literacy.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".