Computational Thinking Assessment Model Based on BERT-HAN for Enhanced Human-Machine Dialogue
The precise quantification of cognitive processes and efficient diagnosis of cognitive qualities are outstanding challenges in the intelligent development of thinking-based pedagogy.Conventional methods for analyzing cognition typically exhibit static limitations,interfering with the influence of material logic and dynamic contexts on thinking.Human-machine dialogues,as vital mediums for external assessments of cognition,provide potential possibilities for automated assessment in computational thinking.To improve the accuracy and interpretability of predicting computational thinking levels within a human-machine dialogue environment,an automated computational thinking assessment model based on the Bidirectional Encoder Representations from Transformers-Heterogeneous graph Attention Network(BERT-HAN)is constructed.The temporal text acquired during human-machine dialogues is collected as an external representation of learners'computational thinking.Sentence-level semantic feature representations are extracted from the human-machine dialogue text data using the BERT-HAN model,which serves as node features in a heterogeneous graph fed into the HAN.The model integrates sentence semantic features,derived through cosine similarity,with meta-path embeddings generated from lists of relational words to further extract the semantic relationships between utterances.During this process,the relationship weights among learning nodes are computed through an attention mechanism,thereby creating an event graph enriched with semantic information.This construction of the event graph not only considers the direct relationship between utterances but also flexibly captures the features of different relationship types within the heterogeneous graph based on the multi-head attention mechanism.Leveraging these features,the level of computational thinking is identified and predicted using the Softmax classifier for automated assessment.Experimental results show that this model achieves a prediction accuracy of 0.869,a recall rate of 1,and an Area Under the receiver operating characteristic Curve(AUC)value of 0.998,surpassing the performance of BERT,Text Convolutional Neural Network(TextCNN),Long Short-Term Memory(LSTM)-HAN,and other models.
万方数据
维普
