Participating by activity or by week in MOOCs
Bibliographic record
Abstract
Purpose The purpose of this study was to provide a new characterization of the extent to which learners complete learning activities in massive open online courses (MOOCs), a central challenge in these contexts. Prior explorations of learner interactions with MOOC materials have often described these interactions through stereotypes, which accounts for neither the full spectrum of potential learner activities nor the ways those patterns differ across course designs. Design/methodology/approach To overcome these shortcomings, the authors apply confirmatory and exploratory factor analysis to learner activities within three MOOCs to test different models of participation across courses and populations found within those courses. Findings Courses varied in the extent to which participation was driven by learning activities vs time/topic or a mixture of both, but this was stable across offerings of the same course. Research limitations/implications The results call for a reconceptualization of how different learning activities within a MOOC are designed to work together, to better allow strong learning outcomes even within one activity form or more strongly encourage participation across activities. Originality/value The authors validate new continuous-patterns rather than a discrete-pattern participation model for MOOC learning.
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.
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 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".