Factors for Development of Learning Content and Task for MOOCs in an Asian Context
Bibliographic record
Abstract
The rapid advancement of emergent learning technologies has led to the introduction of massive open online courses (MOOCs) which offer open-based online learning courses to a large number of students. In line with the advancement, the Malaysia Ministry of Education has recently initiated Malaysia MOOCs via collaboration with four public universities. This paper proposes factors that could be used in development of MOOC learning content, which are: (i) type of MOOC, (ii) type of video lectures, (iii) integration of cultural aspects in video lectures, (iv) communication style in video lectures; and (v) humor effect in video lectures. The paper also proposes factors in developing MOOC learning tasks, namely: (i) structure of learning tasks; (ii) dialog in learning tasks; (iii) learner autonomy in learning tasks; (iv) social settings of learning tasks; and (v) transactional distance of learning tasks. The factors are based on experiences during development of MOOC for ethnic relations and are aligned with learning concepts and strategies such as the transactional distance theory and the theory of the computer model of a sense of humor. Future directions on the development and research on MOOCs are also proposed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".