Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This short paper and presentation reviews a design implementation for scaled inquiry-based learning. A MOOC design resting on the community of inquiry (CoI) theoretical framework and the historical work of Bloom and Wahlberg was tested in a large, open, online course. Over multiple implementations, results indicate higher engagement and completion rates beyond what normally occurs in MOOCs. These results may be attributed to enhanced opportunities for engagement. Beyond a test of MOOC design, this design is in reference to the needs of education broadly. The iron triangle of education requires the adequate combination of cost-effectiveness or affordability, accessibility, and quality. Difficult to offer in combination, this is particularly challenging when learning opportunities are scaled to networks of learners. As one example of networked learning, this MOOC design offers suggestions for high engagement in technology-enabled learning for large groups of learners.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it