MOOCs and the claim of education for all: A disillusion by empirical data
Bibliographic record
Abstract
MOOCs have shaped the discussion on learning with digital media for the last few years. One claim of MOOCs in the tradition of Open Educational Resources is to expand access to education, mainly in the field of higher education. But do MOOCs meet this claim? The empirical data in this article confirm the suspicion that, despite all the heterogeneity of the participants, MOOCs are mostly used by people with a higher level of education. Data of participants from two MOOCs from Germany, as well as, empirical data from large providers and universities are used. But due to the different forms of MOOCs there is no comprehensive proof possible. With respect to the Knowledge Gap Theory and the Digital Divide, a theoretical framework is provided to explain possible causes of a different usage. The aim of the article is to point out the risks of an increase of inequalities as a consequence of hyping MOOCs and to stimulate a discussion about possible answers to make MOOCs an instrument of education for all.
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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 | 0.030 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".