Characterization of Students’ Thinking States Active Based on Improved Bloom Classification Algorithm and Cognitive Diagnostic Model

A student’s active thinking state directly affects their learning experience in the classroom. To help teachers understand students’ active thinking states in real-time, this study aims to construct a model which characterizes their active thinking states. The main research objectives are as follows: (1) to achieve accurate classification of the cognitive levels of in-class exercises; (2) to effectively quantify the active thinking state of students through analyzing the correlation between student cognitive levels and exercise cognitive levels. The research methods used in this study to achieve these objectives are as follows: First, LSTM and Chinese-RoBERTa-wwm models are integrated to extract sequential and semantic information from plain text while TBCC is used to extract the semantic features of code text, allowing for comprehensive determination of the cognitive level of exercises. Second, a cognitive diagnosis model—namely, the QRCDM—is adopted to evaluate students’ real-time cognitive levels with respect to knowledge points. Finally, the cognitive levels of exercises and students are input into a self-attention mechanism network, their correlation is analyzed, and the thinking activity state is generated as a state representation. The proposed text classification model outperforms baseline models regarding ACC, micro-F1, and macro-F1 scores on two sets of exercise datasets in Chinese containing mixed code texts, with the highest ACC, micro-F1, and macro-F1 values reaching 0.7004, 0.6941, and 0.6912, respectively. This proves the proposed model’s effectiveness in classifying the cognitive level of exercises. The accuracy of the thinking activity state characterization model reaches 61.54%. In particular, this is higher than the random baseline, thus verifying the model’s feasibility.