Bibliographic record
Abstract
Discussion forums in Massive Open Online Courses (MOOCs) represent a unique opportunity for insight into the formation of learning communities. Discussions are the locus of a MOOC’s social experience and the forum space a testing ground of instructor presence. In MOOCs, the global scale of peer-to-peer contact represents a network of cross-cultural sharing and collaborative problem-solving, a relationship that generates the opportunity for experts to scaffold a novice’s learning (Anderson, A. (Ed.) (2008). Theory and practice of online learning . Edmonton, AB. Athabasca Press). How learners acquire and build upon prior knowledge sets, sharing them with others in discussion forums, contributes to the robustness of learning communities. As extant literature suggests, collaborative learning accelerates content acquisition, providing a diverse approach to intellectual inquiry based upon the social construction of meaning. This paper outlines a framework for diagnosing a scaffolding of knowledge based on the social and contextual patterning in MOOC discussion forums.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".