Variations in Pedagogical Design of Massive Open Online Courses (MOOCs) Across Disciplines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it