A framework for developing competencies in open and distance e-learning
Bibliographic record
Abstract
Many open universities and distance education institutions have shifted from a predominantly print-based mode of delivery to an online mode characterised by the use of virtual learning environments and various web technologies. This paper describes the impact of the shift to open and distance e-learning (ODeL), as this trend might be called, on the course design practices of faculty members at a small single-mode distance education university in the Philippines. Specifically, the paper presents and analyses the faculty’s perspectives on how their course design practices have changed and issues and challenges arising from these changes. The findings suggest that faculty training programs in ODeL should aim to develop a comprehensive range of ODeL competencies in a systematic and coherent way. Based on the findings, as well as research on practitioner development in teaching effectively with technology, a framework for developing ODeL competencies among faculty is proposed. Aside from covering the four areas of change in course design practice identified in the study, the framework also specifies levels of expertise (basic, intermediate, and advanced), indicating degrees of complexity of the knowledge and skills required for each area at each level. All of the competencies listed for all four areas at the basic level comprise the minimum competencies for teaching an online distance education course.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".