Tutors and Their Feedback in Online Tutorials: The Case in a Distance Teaching University
Bibliographic record
Abstract
The purpose of this study was to analyze the extent to which tutors provided feedback in online tutorials at Indonesia Open University (Universitas Terbuka or UT), as well as tutors' constraint in providing that feedback. This qualitative study used both content analysis of 20 online tutorial classes to see tutors’ feedback and in-depth interviews with the tutors. The results of the study showed that only some tutors in the online tutorial classes provided feedback on discussions and assignments and that some tutors did not provide any feedback. The analysis of the feedback derived from the types of feedback coined by Alvarez, Espasa, and Guasch (2011), namely, corrective feedback, epistemic feedback, suggestive feedback, and epistemic+suggestive feedback. In the online tutorial classes at UT, some feedback from the tutors corresponded with the types of feedback from Alvarez, Espasa and Guasch. The tutors who provided feedback did so despite constraints such as a lack of time to give feedback to each student. Provision of feedback offered benefits and satisfaction to students, leading them to become more successful in online tutorials.
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How this classification was reachedexpand
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 | 0.011 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".