Using MOOCs at Learning Centers in Northern Sweden
Bibliographic record
Abstract
This paper describes the use of globally accessible Massive Open Online Courses, MOOCs, for addressing the needs of lifelong learners at community learning centers in Northern Sweden, by the forming “glonacal” or “blended” MOOCs. The Scandinavian “study circle” concept is used to facilitate the studying of MOOCs. Although the technical possibilities for Swedish universities to offer accessible education are constantly increasing, most Swedish universities do not, at present, prioritize courses for off-campus students. The available web courses in asynchronous formats are difficult to master for untraditional learners and leaves the learning centers with limited possibilities. Therefore, a Nordplus Horizontal project 2014-2016 with partners in three Nordic countries is developing models for the use of MOOCs in learning centers and organisations. A small pilot course case at the learning centre in Arvidsjaur and its outcomes is presented, including the interactions with Lund University which has an ongoing piloting project on use and examination of MOOCs. This concept development is discussed as a blended learning design and as a “glonacal” phenomenon with Marginson and Rhoades’ “glonacal agency heuristics” (2002) forming a background for an actor analysis. Future scenarios are outlined.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".