Professional task-based curriculum development for distance education practitioners at master’s level: A design-based research
Bibliographic record
Abstract
<p>Curriculum development for distance education (DE) practitioners is more and more focusing on practical requirements and competence development. Delphi and DACUM methods have been used at some universities. However, in the competency-based development area, these methods have been taken over by professional-task-based development in the last decade, which has not been applied in the open and distance education area so far. Is the professional-task-based curriculum development approach suitable for open and distance education? This study aims to develop a Master Degree curriculum for DE practitioners in China based on professional tasks. Design-based research (DBR) was used and two cycles of DBR were conducted. Interviews and observations were used to collect data. In the first round of DBR, the authors find that professional-task-based development is feasible and could direct more closely to practical requirements of competencies, and that meanwhile, this approach has some disadvantages and limitations. In the second round of DBR, the approach was revised and results showed that the revised approach was much more suitable and reasonable for DE practitioners. Results of this study include: 1) professional-task based curricula for DE practitioners in China; 2) a curriculum development approach for open and distance education revised from professional-task-based development.</p>
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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 | 0.056 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".