Massive Open Online Courses and Higher Education
Bibliographic record
Abstract
Since the first MOOC was launched at the University of Manitoba in 2008, this new form of the massification of higher education has been a rollercoaster ride for the university sector. The New York Times famously declared 2012 to be the year of the MOOC. However, by 2014, the number of academic leaders who believed the model was unsustainable doubled to more than 50%. While the MOOC hype has somewhat subsided, the attitudes and anxieties of this peak time can still be seen influencing universities and their administrations. This is the first volume that addresses Massive Open Online Courses from a post-MOOC perspective. We move beyond the initial hype and revolutionary promises of the peak-MOOC period and take a sober look at what endures in an area that is still rapidly growing, albeit without the headlines. This book explores the future of the MOOC in higher education by examining what went right, what went wrong and where to next for the massification of higher education and online learning and teaching. The chapters in this collection address these questions from a wide variety of different backgrounds, methodologies and regional perspectives. They explore learner experiences, the move towards course for credit, innovative design, transformations and implications of the MOOC in turn. This book is valuable reading for students and academics interested in education, eLearning, globalisation and information services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".