An Affective, Formative and Data-Driven Feedback Intervention in Teacher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 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 | 0.000 | 0.002 |
| 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 it