Guest Editors' Preface to the Special Issue on MOOCs An Academic Perspective on an Emerging Technological and Social Trend
Bibliographic record
Abstract
Higher education is entering a phase of dramatic change and innovation. Mainstream media often present massive open online courses (MOOCs) as both a reflection of the need for universities to undergo a metamorphosis and as a means of forcing a new perspective on digital teaching and learning practices (i.e., Lewin, 2013; Pappano, 2012). However, university faculty caution that there is not enough research evidence to support widespread adoption. Two significant challenges around the role of MOOCs in higher education are prevalent. First, the discussion on MOOCs to-date has occurred mainly in mainstream media and trade publications. Although some peer-reviewed articles on MOOCs currently exist (e.g., Fini, 2009; Kop, 2011), the amount of available research is generally limited. One of the goals of this special issue is to attempt to address this lack of peer reviewed literature. Second, the vast research available in online and distance education has been largely ignored by mainstream media and MOOC providers. Paying greater attention to what is already known about learning in online and virtual spaces, how the role of educators and learners is transformed in these contexts, and how social networks extend a learning network will enable mainstream MOOC providers and their partners to make evidencebased decisions in favor of educational reform. Thus, a second goal of this special issue is to highlight this research and provide an historical context for online and distance learning not currently evident in the mainstream media treatment of MOOCs.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".