Bringing order to chaos in MOOC discussion forums with content-related thread identification
Bibliographic record
Abstract
This study addresses the issues of overload and chaos in MOOC discussion forums by developing a model to categorize and identify threads based on whether or not they are substantially related to the course content. Content-related posts were defined as those that give/seek help for the learning of course material and share/comment on relevant resources. A linguistic model was built based on manually-coded starting posts in threads from a statistics MOOC (n=837) and tested on thread starting posts from the second offering of the same course (n=304) and a different statistics course (n=298). The number of views and votes threads received were tested to see if they helped classification. Results showed that content-related posts in the statistics MOOC had distinct linguistic features which appeared to be unrelated to the subject-matter domain; the linguistic model demonstrated good cross-course reliability (all recall and precision > .77) and was useful across all time segments of the courses; number of views and votes were not helpful for classification.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".