Honing in on social learning networks in MOOC forums
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 study examines the impact of content-based network partitioning and tie definition on social network structures and interpretation for MOOC discussion forums. Using dynamic interrelated post and thread categorization [5] based on a previously developed natural language model [27], 817 threads containing 3124 discussion posts from 567 learners in a MOOC on the use of statistics in medicine were characterized as either related to the learning of course content or not. Content-related, non-content, and unpartitioned interaction networks were constructed based on five different tie definitions: Direct Reply, Star, Direct Reply+Star, Limited Copresence, and Total Copresence. Results showed content-related and non-content networks to have distinct characteristics at the network, community, and individual node levels, validating the usefulness of the content/non-content distinction as an analytic tool. Network properties were less sensitive to differences in tie definition with the exception of Total Copresence, which showed distinct characteristics presenting dangers for general use, but usefulness for detecting inflated social status due to "superthread" initiation.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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