University of Toronto instructors’ experiences with developing MOOCs
Bibliographic record
Abstract
<p>We interviewed eight University of Toronto (U of T) instructors who have offered MOOCs on Coursera or EdX between 2012 and 2014 to understand their motivation for MOOC instruction, their experience developing and teaching MOOCs, and their perceptions of the implications of MOOC instruction on their teaching and research practices. Through inductive analysis, we gleaned common motivations for MOOC development, including expanding public access to high quality learning resources, showcasing U of T teaching practices, and attempting to engage MOOC learners in application of concepts learned, even in the face of constraints that may inhibit active learning in MOOC contexts. MOOC design and delivery was a team effort with ample emphasis on planning and clarity. Instructors valued U of T instructional support in promoting systematic MOOC design and facilitating technical issues related to MOOC platforms. The evolution of MOOC support at U of T grew from a focus on addressing technical issues, to instructional design of MOOCs driven, first, by desired learning outcomes. Findings include changes in teaching practices of the MOOC instructors as they revised pedagogical practices in their credit courses by increasing opportunities for active learning and using MOOC resources to subsequently flip their classrooms. This study addresses the paucity of research on faculty experiences with developing MOOCs, which can subsequently inform the design of new forms of MOOC-like initiatives to increase public access to high quality learning resources, including those available through U of T.</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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".