What Research Says About MOOCs – An Explorative Content Analysis
Bibliographic record
Abstract
<p class="3">Since the first offering of a Massive Open Online Course (MOOC) in 2008, the body of literature on this new phenomenon of open learning has grown tremendously. In this regard, this article intends to identify and map patterns in research on MOOCs by reviewing 362 empirical articles published in peer-reviewed journals from 2008 to 2015. For the purposes of this study, a text-mining tool was used to analyse the content of the published research journal articles and to reveal the major themes and concepts covered in the publications. The findings reveal that the MOOC literature generally focuses on four lines of research: (a) the potential and challenges of MOOCs for universities; (b) MOOC platforms; (c) learners and content in MOOCs; and (d) the quality of MOOCs and instructional design issues. Prospective researchers may use these results to gain an overview of this emerging field, as well as to explore potential research directions.</p>
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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".