Blurring Boundaries in Education: Context and Impact of MOOCs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The opinions on MOOCs go to extremes, covering a wide variety of topics, affecting economy, pedagogy, and computer science which makes it hard to keep oversight. Despite the many excellent research reports and articles, an overview of the bigger picture, providing a holistic qualitative summary of the different opinions, is still very welcome. Special attention is given to the broader social, cultural, and technological context in which these developments come about. In this paper, it is discussed how the educational industry has received a “wake up call” provoking a global discussion on learning and teaching, accordingly disturbing established boundaries between formal and informal learning, public and for-profit education, teachers and learners and quite intriguingly, between software and teaching practices. Golden opportunities for Artificial Intelligence and Technology Enhanced Learning are unfolding, evidenced by the digitalization movement of education, gamification, and the stringent need for massively scalable (and therefore computerized) personal learning experiences.</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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it