Variations in Pedagogical Design of Massive Open Online Courses (MOOCs) Across Disciplines
Bibliographic record
Abstract
Given that few studies have formally examined pedagogical design considerations of Massive Online Open Courses (MOOCs), this study explored variations in the pedagogical design of six MOOCs offered at the University of Toronto, while considering disciplinary characteristics andexpectations of each MOOC. Using a framework (Neumann et al., 2002) characterizing teaching and learning across categories of disciplines, three of the MOOCs represented social sciences and humanities, or “soft” MOOCs, while another three represented sciences, or “hard” MOOCS. We utilized a multicase study design for understanding differences and similarities across MOOCs regarding learning outcomes, assessment methods, interaction design, and curricular content. MOOC instructor interviews, MOOC curricular documents, and discussion forum data comprised the data set. Learning outcomes of the six MOOCs reflected broad cognitive competencies promoted in each MOOC, with the structure of curricular content following disciplinary expectations. The instructors of soft MOOCs adopted a spiral curriculum and created new content in response to learner contributions. Assessment methods in each MOOC aligned well with stated learning outcomes. In soft MOOCs, discussion and exposure to diverse perspectives were promoted while in hard MOOCs there was more emphasis on question and answer. This study shows disciplinary-informed variations in MOOC pedagogy, and highlights instructors’ strategies to foster disciplinary ways of knowing, skills, and practices within the parameters of a generic MOOC platform. Pedagogical approaches such as peer assessment bridged the disciplines. Suggestions for advancing research and practice related to MOOC pedagogy are also included.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".