Fixing higher education through technology: Canadian media coverage of massive open online courses
Bibliographic record
Abstract
The popularization of massive open online courses (MOOCs) has been shrouded in promises of disruption and radical change in education. In Canada, official partnerships struck by higher education institutions with platform providers such as Coursera, Udacity and edX were publicized by dailies and professional magazines. This print coverage of MOOCs captures the contemporary ideological struggle over the meaning of both technology and higher education. By means of a thematic analysis of the English Canadian print coverage of MOOCs (2012–2014), this paper shows that both online educational technologies and higher education are constructed through an economic frame. However, this frame does not go unchallenged. Where newspapers construct MOOCs as an easy fix for an allegedly inefficient and outdated higher education system, professional magazines question the relationship between technology, higher education and money. These different representations point to the efforts of academic communities to develop alternative social imaginaries of education as public good within a dominant neoliberal framing of MOOCs and of the higher education system. In conclusion, the paper reflects on how the academic community can create alternative discursive spaces by shifting the discussion of MOOCs from economic concerns to civic goals.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".