Evaluation of the UNED MOOCs Implementation: Demographics, Learners' Opinions and Completion Rates
Bibliographic record
Abstract
The paper is a study about the MOOC experience at the Spanish National University of Distance Education (UNED), where we have collected initial and final information about learners' profiles and opinions, as well as enrolment, completion and certification rates. It is a survey-based study covering 17 MOOCs offered in UNED's own platform, and collects information from a sample of more than 24000 learners (initial survey) and 2003 learners (final survey). The paper first presents an overview of the MOOC experience at UNED, introducing the main features of these courses. Afterwards, it focuses on the methodology used in the study and in the information gathered in the second edition of UNED MOOCs, which took place from November 2013 until March 2014. Learners' average profile is a Spanish female, approximately 37 years old, with a University degree, and generally employed. For many of the participants, UNED MOOCs were their first experiences with these sorts of courses, and the main reasons for enrolment were the course topic and the perceived usefulness for professional development. The expectations regarding completion and certification where initially very high, but completion rates remain below 14%. In the final survey, the overall experience in the MOOCs and the different tools used in the courses were highly valued by learners, except the support figures (curator, facilitator, peers), which received lower ratings. These findings are of interest for the institution and further research, refining learning analytics, is encouraged.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".