Cross Validating a Rubric for Automatic Classification of Cognitive Presence in MOOC Discussions
Bibliographic record
Abstract
As large-scale, sophisticated open and distance learning environments expand in higher education globally, so does the need to support learning at scale in real time. Valid, reliable rubrics of critical discourse are an essential foundation for developing artificial intelligence tools that automatically analyse learning in educator-student dialogue. This article reports on a validation study where discussion transcripts from a target massive open online course (MOOC) were categorised into phases of cognitive presence to cross validate the use of an adapted rubric with a larger dataset and with more coders involved. Our results indicate that the adapted rubric remains stable for categorising the target MOOC discussion transcripts to some extent. However, the proportion of disagreements between the coders increased compared to the previous experimental study with fewer data and coders. The informal writing styles in MOOC discussions, which are not as prevalent in for-credit courses, caused ambiguities for the coders. We also found most of the disagreements appeared at adjacent phases of cognitive presence, especially in the middle phases. The results suggest additional phases may exist adjacent to current categories of cognitive presence when the educational context changes from traditional, smaller-scale courses to MOOCs. Other researchers can use these findings to build automatic analysis applications to support online teaching and learning for broader educational contexts in open and distance learning. We propose refinements to methods of cognitive presence and suggest adaptations to certain elements of the Community of Inquiry (CoI) framework when it is used in the context of MOOCs.
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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 | 0.050 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".