Teacher Perspective on MOOC Evaluation and Competency-Based Open Learning
Bibliographic record
Abstract
Quality MOOCs (massive open online courses) ensure open learning under the top-down guidance of established criteria and standards. With an evaluative approach, course providers can use the guiding frameworks in designing and refining courses while fostering students’ targeted open learning competency. This study explores the openness embedded into MOOC course design and the anticipated core competency, gathering insights from interviews with in-service teachers preparing MOOC lessons. The findings suggest that teachers’ evaluative approach remains necessary in its cyclical practice, using prior experience as the primary foundation while also referencing national and international frameworks for course refinement. However, the teachers’ observed high reliance on early experience has resulted in an unstable foundation, where only a bottom-up experiential perspective is adopted, instead of an ideal balance with the top-down standards. From the teachers’ perspective, task completion is prioritized as the only primary learning outcome, despite open learning providing students with extensive opportunities to extend beyond in-class task challenges. Future studies should address this unbalanced perspective with a more diverse respondent pool and continue efforts to triangulate data through mixed-method approaches.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".