Making ‘MOOCs’: The construction of a new digital higher education within news media discourse
Bibliographic record
Abstract
One notable ‘disruptive’ impact of massive open online courses (MOOCs) has been an increased public discussion of online education. While much debate over the potential and challenges of MOOCs has taken place online confined largely to niche communities of practitioners and advocates, the rise of corporate ‘xMOOC’ ventures such as Coursera, edX and Udacity has prompted popular mass media interest at levels not seen with previous educational innovations. This article addresses this important societal outcome of the recent emergence of MOOCs as an educational form by examining the popular discursive construction of MOOCs over the past 24 months within mainstream news media sources in United States, Australia and the UK. In particular, we provide a critical account of what has been an important phase in the history of educational technology—detailing a period when popular discussion of MOOCs has far outweighed actual use/participation. We argue that a critical analysis of MOOC discourse throughout the past two years highlights broader societal struggles over education and digital technology—capturing a significant moment before these debates subside with the anticipated normalization and assimilation of MOOCs into educational practice. This analysis also sheds light on the influences underpinning how many people perceive MOOCs thereby leading to a better understanding of acceptance/adoption and rejection/resistance amongst various professional and popular publics.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".