The pedagogical enhancement of open education: An examination of problem-based learning
Bibliographic record
Abstract
Open education, as embodied in open educational resources (OER) and OpenCourseWare (OCW), has met and dealt with several key problems. The movement now has a critical mass of available content. Leveraging no small amount of funding and associated development, open education has the tools to collect, disseminate, and support the discovery of open materials. Now that the foundation for openness has been laid, practitioners are experimenting with new kinds of education and pedagogies associated with open content (Weller, 2009; di Savoia, 2009). Problem-based learning is one of many progressive pedagogies that might be combined with open education. This paper defines problem-based learning in the context of open education. Unique challenges are presented and discussed alongside possible solutions, realistic limitations, and calls for implementation in the future to test validity.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".