Constructing Quality Feedback to the Students in Distance Learning: Review of the Current Evidence with Reference to the Online Master Degree in Transplantation
Bibliographic record
Abstract
Introduction: It was a challenge to design a feedback pathway for distance learning course that deals with complexand ambiguous clinical subject like organ transplantation. This course attracts mature clinicians (n=117 spread overthree modules) from 27 countries where in addition to the time and zone barriers; there are cultural, institutionalbackground and also ethnic barriers. In addition to the challenges faced in designing the curriculum and assessmentthat match this diverse group of students, we have to deliver a quality feedback to achieve our leaning objective. Howwould we construct and deliver this feedback to students you have not seen (in a virtual classroom) and may be on adifferent continent of this busy planet?Methods: We analysed the published data on feedback with reflection on the nature of this course and the pedagogyused while considering the diversity of the students joined this courseConclusion: In this distance-learning course constructing a quality feedback to the students is more technicallydemanding compared to a traditional course. Students in distance learning need much more support and feedback thanin a traditional course. There is a potential threat that these students feel isolated in their own online world and may notengage with this virtual educational environment properly.
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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 | 0.067 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".