Reinforcing equity and justice in learning: Using digital co-created rubrics and audio feedback as/for assessment
Bibliographic record
Abstract
The paper explores a study of a foreign learning experience during the pandemic that was unique due to the co-creative engagement with students. This study examines the effects of using technologically novel feedback strategies and assessment practices to analyze student performance within the frame of the justice theory. Using targeted blended means of assessment to provide leveled methods included auditory and co-created rubric conversations to support bilingual learners in a diverse environment. Qualitatively, using a questionnaire to collect student feedback on co-created decoding of rubrics with thematic analysis and quantitatively using a paired sample t-test by comparing scores for the first and final draft provided fulsome results. Findings indicate how these uses of technology can promote learner autonomy by allowing students to take agency of their own learning and increase students’ performance. The findings reflect the need for using technological avenues to assist bilingual learners in the development of their language skills through extended feedback.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".