Is My MOOC Learner-Centric? A Framework for Formative Evaluation of MOOC Pedagogy
Bibliographic record
Abstract
MOOCs popularly support the diverse learning needs of participants across the globe. However, literature suggests well-known scepticism regarding MOOC pedagogy which questions the effectiveness of the educational experience offered by it. One way to ensure the quality of MOOCs is through systematic evaluation of its pedagogy with the goal to improve over time. Most existing MOOCs’ quality evaluation methods do not account for the increasing significance of learner-centric pedagogy towards providing a richer learning experience. This paper presents a MOOC evaluation framework (MEF), designed with a strong pedagogical basis underpinned by theory and MOOC design practices, which evaluates the integration of learner-centric pedagogy in MOOCs. Using mixed-methods research, the internal validation was conducted through expert reviews (N = 2), and external validation (N = 13) was conducted in the field to test model usability and usefulness. The framework was classified as “good” (SUS: 78.46) in terms of usability. A high perception of usefulness (84%–92%) was observed for the framework as a formative evaluation tool for assessing the integration of learner-centric pedagogy and bringing a positive change in MOOC design. Different participants acknowledged new learning from varied dimensions of the framework. Participants also recognized that the scores obtained using the MEF truly reflected the efforts taken to incorporate learner-centric design strategies in the evaluated dimensions. The framework focuses on learner-centric evaluation of MOOC design with a goal to facilitate improved pedagogy.
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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 | 0.259 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".