Instructors’ Perspectives of Challenges and Barriers to Providing Effective Feedback
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
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.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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