Detecting cognitive engagement in online course forums: A review of frameworks and methodologies
Bibliographic record
Abstract
A key aspect of online learning in higher education involves the utilization of course discussion forums. Assessing the quality of posts, such as cognitive engagement, within online course discussion forums, and determining students’ interest and participation is challenging yet beneficial. This research investigates existing literature on identifying the cognitive engagement of online learners through the analysis of course discussion forums. Essentially, this review examines three educational frameworks - Van Der Meijden’s Knowledge Construction in Synchronous and Asynchronous Discussion Posts (KCSA), Community of Inquiry (CoI), and Interactive, Constructive, Active, and Passive (ICAP) , which have been widely used for students’ cognitive engagement detection analyzing their posts in course discussion forums. This study also examines the natural language processing and deep learning approaches employed and integrated with the above three educational frameworks in the existing literature concerning the detection of cognitive engagement in the context of online learning. The article provides recommendations for enhancing instructional design and fostering student engagement by leveraging cognitive engagement detection. This research underscores the significance of automating the identification of cognitive engagement in online learning and puts forth suggestions for future research directions.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".