Measuring cognitive presence in online discussions: automated detection and instructional insights from a MOOC context

Researchers have developed and validated an interpretable natural language processing model that automatically detects cognitive presence in online discussions across massive open online courses (MOOCs). By fine-tuning the RoBERTa architecture with theoretically derived structural cues from the Community of Inquiry framework, the model successfully classified 227,863 sentences from 4,260 learners across six Social Work MOOCs into four distinct cognitive presence phases and a fifth non-cognitive category with validated pedagogical interpretability.

This research demonstrates that scalable, automated natural language processing can reliably measure higher-order learning and cognitive depth in large-scale digital education environments. Establishing theoretically grounded and interpretable diagnostic models allows institutions to bridge the gap between large-scale learning analytics and practical instructional intervention.

The cue-augmented RoBERTa model achieved strong discourse classification performance with a macro F1 score of 0.714 and a Cohen's kappa of 0.666.

Forward consideration, not a verified fact.