iMOOC on Climate Change: Evaluation of a Massive Open Online Learning Pilot Experience
Bibliographic record
Abstract
<p class="BODYTEXT">MOOCs are a recent phenomenon, although given its impact, have been subject to a large debate. Several questions have been raised by researchers and educators alike as regarding its sustainability both economical and as an efficient mode of education provision. In this paper we contribute to this discussion by presenting a case study, a Portuguese MOOC about lived experiences in climate change which piloted the iMOOC pedagogical model developed at Universidade Aberta. The iMOOC is an hybrid model which incorporates elements from existing MOOCs but adds other features drawn from UAb's experience with online learning and aim at better integrate in the larger context of the institutional pedagogical culture. The iMOOC implied also an integration of platforms - Moodle and Elgg. The course had more than one thousand participants, and it was the largest MOOC course on Portuguese language delivered so far. We discuss the effort required to design and deliver the course, the technological solution developed, and the results obtained. We registered a moderate effort to create and run the course, ensured by internal staff from the University. The technological solution was a success, an integrated architecture combining well-established, well-tested open software. The completion rate was 3.3%, but the high success of this innovative learning experience is demonstrated by the active involvement of participants, almost 50% of the ones that followed the course until the end, and the satisfaction survey results, with 90% of approval. Lessons learned from this experience and future research on the field are also discussed.</p><strong>Keywords</strong>: Massive open online course, iMOOC, pedagogical model, learning effectiveness,<strong> </strong>completion rate, cost analysis.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".