What public media reveals about <scp>MOOC</scp> s: A systematic analysis of news reports
Bibliographic record
Abstract
Abstract One of the striking differences between massive open online courses ( MOOC s) and previous innovations in the education technology field is the unprecedented interest and involvement of the general public. As MOOC s address pressing problems in higher education and the broader educational practice, awareness of the general public debate around MOOC s is essential. Understanding the public discourse around MOOC s can provide insights into important social and public problems, thus enabling the MOOC research community to better focus their research endeavors. While there have been some reports looking at the state of the MOOC ‐related research, the analysis of the public debate surrounding MOOC s is still largely missing. In this paper, we present the results of a study that looked at the content of the public discourse related to MOOC s. We identified the most important themes and topics in MOOC ‐related mainstream news reports. Our results indicate that coverage of MOOC s in public media is rapidly decreasing: by the middle of 2014, it decreased by almost 50% from the highest activity during 2013. In addition, the focus of those discussions is also changing. While the majority of discussions during 2012 and 2013 were focused on MOOC providers, the announcements of their partnerships, and million dollar investments, the current focus of MOOC discourse seems to be moving toward more productive topics focused on the overall position of MOOC s in the global educational landscape. Among different topics that this study discovered, government‐related issues and the use of data and analytics are some of the topics that seem to be growing in popularity during the first half of 2014.
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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 | 0.015 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".