Teacher Education during the COVID-19 Lockdown: Insights from a Formative Intervention Approach Involving Online Feedback
Bibliographic record
Abstract
This paper examines preservice teachers’ perspectives on assessment feedback developed in a teacher education course during the first lockdown due to the COVID-19 pandemic. As initially negotiated with students, feedback was learner-centred and involved a formative intervention approach applied iteratively by the teacher educator over the course of one semester. Although such feedback was initially face-to-face, it had to be given exclusively online following the unexpected closure of the university. Analysis of student teachers’ perspectives, which were collected through an online questionnaire completed after their final assessment, reveals both positive and critical aspects regarding the feedback provided by the teacher educator. While reaffirming the significance of feedback as a crucial element for learning in online teacher education contexts, the findings also show that the clarity, affective bonding and multimodal meaning-making involved in face-to-face interaction are particularly challenging when the communication of feedback is digitally mediated. The implications and limitations of such findings are discussed.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".