An Affective, Formative and Data-Driven Feedback Intervention in Teacher Education
Bibliographic record
Abstract
Educators and researchers have long contemplated the most effective ways to provide feedback to students, to build sustainable feedback practices, and to establish feedback literacy. While a considerable amount of research, theory, and practical approaches exist to support the effect of formative feedback practices, less research exists on the impact of affective elements related to feedback. This study set out to explore pre-service teachers’ perceptions of a feedback intervention that included affective, formative, and data-driven aspects. A mixed-reality simulation environment was selected as the context for the study, and eight pre-service teachers performing in the simulation were selected as participants. This qualitative multicase study included three rounds of simulation observations, a feedback intervention, and interviews. Data were analyzed using a thematic analysis framework. Findings showed that the application of confirmation, empathy, and reciprocity in the feedback intervention prompted the development of helping relationships that promoted personal growth. Humanism became a useful framework for these emergent findings. In addition, findings included participants’ preferences for formative feedback over data-feedback, particularly formative feedback that introduced engaging language, purposeful organization, and details and examples. Lastly, findings revealed participants’ perceived personal growth in feedback literacy, especially in managing emotions and committing to the feedback process.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".