Bibliographic record
Abstract
There is a growing body of literature that recognizes the role Massive Open Online Courses (MOOCs) can play in improving access to education globally, and particularly to thousands of people in developing and developed countries. There is increasing concern, however, that the millions of displaced refugee learners throughout Europe, the Middle East, and other regions are still disadvantaged when it comes to engaging in learning through MOOCs. The reasons for this disadvantage range from a lack of appropriate infrastructure or other supporting structures, to a lack of contextualized content. So far, little attention has been paid to contextualized MOOC models, which may both impact policies and be adapted to the specific needs of these learners who often do not have the means to access many education opportunities. Therefore, the purpose of this paper is to propose a frugally-engineered MOOC model that addresses the barriers of access and participation for refugees. This paper engages in an exploratory research methodology, using findings from the literature and expert opinions gathered through interviews. These findings lead to the development of what the authors call a Frugal MOOC Model which can be contextualized to meet the needs of refugee learners. The paper goes on to highlight the development of the Frugal MOOC Model as the first phase of an ongoing study. It concludes with recommendations for the next phase of the study: how to implement the newly developed model.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".