Analysing the Importance-Competence Gap of Distance Educators With the Increased Utilisation of Online Learning Strategies in a Developing World Context
Bibliographic record
Abstract
The competence of distance educators has a significant impact on learners’ success. The paradigm shift for universities to become distance and electronic learning environments justifies the urgency to address competency gaps in distance educators’ competencies efficiently. From a strategic human resource development perspective, the systems theory is used to explain the idea of maximising outputs with the minimum inputs (Biddle, 1986). In this study, distance educators at Unisa reflected on their experienced competency gaps. Where previous studies mainly focus on the size of the gaps, the aim of this article is to highlight the competency gaps likely to have the biggest impact. For this study, we used stratified non-probability sampling, and selected 407 academics who were, at the time of the study, permanently employed at a mega ODL university in South Africa. These academics represented a wide range of colleges, campuses, ages, and genders. The results of this study have implications for capacity building of academic staff in developing world contexts and other contexts where resources are scarce.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".