Bibliographic record
Abstract
Since the launch of the first massive open online course (MOOC) in 2008, numerous claims have been made about MOOCs’ power to ‘fix’ broken education systems, including those in the Global South. However, some (e.g. Altbach, 2014) argue that MOOCs are strengthening the dominant academic culture of the West, to the exclusion of alternative voices. Subsequently, there has been a growing call for the creation of more localised MOOCs in the Global South, in addition to demand for rigorous evaluation of MOOCs’ long term impact in order to ascertain whether individual courses are meeting their intended outcomes for learners and other stakeholders in diverse contexts. This paper outlines a new approach to investigating MOOCs’ long-term impact, developed in connection with a long-term impact evaluation of the ‘Introduction to Technology-Enabled Learning (TEL) MOOC’ (https://www.telmooc.org/) - a collaboration between Athabasca University, Canada, and the Commonwealth of Learning. A ‘theory of change’ approach has been applied as the framework for the TEL MOOC evaluation, allowing for investigation of complex mechanisms of change and causality. The evaluation findings themselves will be shared at PCF9 and will be the focus of a subsequent report.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".