Dual-view cross attention enhanced semi-supervised learning method for discourse cognitive engagement classification in online course discussions

Cognitive engagement classification is an important task for analyzing learners' interactive discourse texts in online education. Highly accurate classification results can facilitate efficient instructional decisions and interventions in online course discussions. However, existing cognitive engagement classification models fail to achieve accurate and reliable results due to two primary challenges: (1) the inherent sparsity of discourse features and single-view representation limitations, and (2) insufficient performance in scenarios with limited labeled data. To address these challenges, this study proposes a Dual-view Cross Attention enhanced Semi-supervised Learning method (DCASL) for discourse cognitive engagement classification. A dual-view cross-attention mechanism is built to fuse cognitive psychological and generic semantic features to achieve complementary discourse representations to enhance the feature fusion depth and receptive field of the classification model. By combining the dual-view cross-attention mechanism with supervised training and consistency training, we develop a semi-supervised cognitive engagement classification model. The dual-view feature enhancement significantly improves classification accuracy and model performance, particularly in scenarios with scarce labeled data. The results of experiments on three datasets from real-world online course discussions with eleven benchmark models show that DCASL effectively enhances model representation, improves cognitive engagement classification accuracy and is interpretable in the case of a small annotated corpus.