Case Study as a Research Method for Analyzing MOOCs
Bibliographic record
Abstract
Educational research is one of the many fields of knowledge that frequently use case studies as a research method, particularly when applying an interpretive approach. Based on literature reviews and a systematic analysis of current scientific literature, this paper examines the prevalence and characteristics of the case study as a methodology for research on MOOCs. Ninety-two documents were selected from the search results returned by two of the most prestigious scientific databases: Web of Science (WOS) and SCOPUS. Findings showed that (a) even when searching solely for case studies, quantitative research paradigms were more prevalent than interpretive approaches; (b) geographical distribution of these studies was partially biased; (c) case studies were less prevalent in these databases than other empirical investigations on MOOCs; (d) the data collection and data analysis methods most frequently used in the case studies were more aligned with a quantitative approach; and (e) there is still very little instructor-focused research using this methodology. In the light of these findings and their discussion, future directions for research using case study methodology are proposed, given the potential of this method to illustrate certain issues for which other approaches have proved inadequate or insufficient.
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 | 0.083 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".