Examining MOOCs: A Comparative Study among Educational Technology Experts in Traditional and Open Universities
Bibliographic record
Abstract
The proliferation of Massive Open Online Courses (MOOCs) in recent years has generated much debate. MOOCs have been presented as technology-based educational practices, but many researchers question if this kind of open courses really respects some of the consolidated principles behind the education offered at universities. In light of this situation, consulting the teachers most closely tied to this type of course can provide an authoritative view of the issue and can allow the most important elements to be highlighted in order to carry out further research. Using a qualitative methodology based on an open questionnaire, this work presents the opinions and perceptions of teachers/lecturers in educational technology regarding these new courses key elements. These key elements are analysed through analysing its controversial definition, their pedagogical advantages and limitations, the functions of a tutor in a MOOC and their assessment (or accreditation). In addition, a comparison is made between the contributions of teachers from a traditional university with a face-to-face model and those from a distance university, which is based entirely on a virtual training offer and which has a greater possibility of coming into direct competition with these Massive Open Online Courses.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".