Who studies MOOCs? Interdisciplinarity in MOOC research and its changes over time
Bibliographic record
Abstract
The complexity of digital and online education is becoming increasingly evident in the context of research into networked learning/participation. Interdisciplinary research is often proposed as a way to address complex scientific problems and enable researchers to bring novel perspectives into a field other than their own. The degree to which research on Massive Open Online Courses (MOOCs) is interdisciplinary is unknown. We apply descriptive and inferential statistics to bibliometric data to investigate interdisciplinarity in MOOC research. Results show that MOOC research published in 2013-2015 was (a) mostly conducted by researchers affiliated with Education and Computer Science disciplines, (b) far from monolithic, (c) had a greater representation of authors from Computer Science than in the past, and (d) showed a trend toward being more interdisciplinary than MOOC research published in 2008-2012. Our results also suggest that empirical research on xMOOCs may be more interdisciplinary than research on cMOOCs. Greater interdisciplinarity in xMOOC research could reflect the burgeoning interest in the field, the general familiarity with the xMOOC pedagogical model, and the hype experienced by xMOOCs. Greater interdisciplinarity in the field may also provide researchers with rich opportunities to improve our understanding and practice of digital and online learning.
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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 | 0.024 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".