A Cognitive Topic Modeling Approach for Detailed Learner Analysis in Asynchronous Discussions

Asynchronous discussion platforms are crucial components of online education: these sites promote knowledge exchange and enhance the learning experience. Students’ discussion data contain abundant information, including personal interests and preferences. Such content also offers insight into learners’ cognitive states. Uncovering the features of various cognitive processes holds value for the education field. We thus propose a cognitive topic modeling method for asynchronous online discussions called SLPoK–ENA. This approach consists of two parts: 1) SLPoK, a topic modeling technique that uses sentence embedding to improve latent Dirichlet allocation (LDA); and 2) the integration of SLPoK and epistemic network analysis (ENA) to perform cognitive topic modeling. Results from two real-world course datasets show that SLPoK outperforms LDA in topic modelling when dealing with educational data, achieving Normalized Mutual Information (NMI) values of 0.62 (Dataset 1) and 0.75 (Dataset 2), Adjusted Rand Index (ARI) values of 0.45 and 0.73, Silhouette Coefficient (SC) of 0.225 and 0.308, and Calinski-Harabasz Index (CH) of 196.350 and 464.415 on the two datasets, respectively —all significantly higher than the traditional LDA baseline model. Also, this method is better at capturing students’ cognitive characteristics. Experiments demonstrate that SLPoK–ENA can identify learners’ topic preferences under different cognitive conditions. This method has practical value for understanding students and improving teaching quality in online education settings.