Instructors’ Perspectives of Challenges and Barriers to Providing Effective Feedback
Bibliographic record
Abstract
Instructor perspectives regarding the challenges they experience in enacting effective feedback processes have not been the focus in the literature on effective feedback processes. This study investigated the challenges that instructors experienced in providing effective feedback to students between January and April 2020, particularly considering campus closures and the shift to online learning in response to the COVID-19 pandemic. This study consisted of six focus groups held between January and April 2020 with five instructors from different disciplines at the same institution with class sizes ranging from 14 to 82. Through a thematic analysis using a constant comparison method, it was found that the biggest challenges instructors experienced in providing effective feedback was their own workload, the disruption that student inaction on feedback brought to the feedback process, and how the instructors managed their own affective responses and mindsets towards feedback. These findings are discussed within the context of the COVID-19 pandemic and based on these findings, recommendations for instructors include considering their own limitations when designing feedback processes and checking their beliefs about feedback with their students’ perspectives on feedback in order to align understanding.
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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 | 0.028 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".