Learning through reflection: Supervising DRC master’s degree students within the open distance and learning context
Bibliographic record
Abstract
The internationalisation of higher education is a global imperative that impacts on students and supervision practices in various ways. Culture and language diversity, as well as the characteristics of the students themselves in Open and Distance Learning, have been given little attention and the impact is not always taken into account. When implementing a scholarship development programme across language borders, factors such as culture and socio-economic background need to be taken into account because both can have an effect on the supervisory practices and success of such a programme. Supervision in a language not understood by the supervisor and the master’s degree students in the DRC challenged traditional western methodologies and paradigms. A qualitative narrative reflection was therefore undertaken to both critically reflect on the challenges encountered and initiate innovative ideas. Indeed, I can say that, in my supervisory practice, the western body of knowledge was challenged. As a result, new research methodology initiatives to improve distance education research supervision had to be initiated and implemented.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".