Massive online obsessive compulsion: What are they saying out there about the latest phenomenon in higher education?
Bibliographic record
Abstract
This article is a review of ideas, comments, and inquiries about massive open online courses (MOOCs) gathered from a wide variety of online journal and magazine articles, and web blogs. As a seasoned “traditional” online educator, as well as a student participant in several MOOCs, I also take the opportunity to share my personal insight from my own learning experiences, with the goal of illustrating some of the concerns unearthed in my research. One serious issue regarding MOOCs is that some learners can feel isolated and/or neglected, particularly when they perceive that other course participants and/or the professor are ignoring their contributions. Our era has witnessed “the McDonaldization of Education” (Lane & Kinser, 2012), in which one size fits all and information is delivered to student “customers” via systematically managed “factories” whose overseers frown upon any supposed waste of valuable resources or human effort. In the mass-appeal environment of a MOOC, it is quite possible that a student will receive no customized feedback from nominal experts in the field. Lack of meaningful interaction is likely a key factor driving high attrition numbers in the online education environment – numbers that are apparently even higher in the case 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 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".