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摘要
The existing cognitive diagnosis models could not use enough information and relied on local response information. Aiming at the problem of low diagnostic accuracy, an adaptive cognitive diagnostic method based on improved cascade of broad learning was proposed. Firstly, the semantic features and parameters of the items were extracted and integrated into vectors via an unbiased weighted method. Then, an improved cascade of broad learning system (ICBLS) was put forward to acquire the full sequence of the response information, and solve the problem of long sequence learning and forgetting with the residual structure. The grid search method was used to determine the optimal combination of parameters, and then a cognitive diagnosis model was built. Finally, the classification of the knowledge state was realized through the nonlinear classifier. With BP neural network, Bi-LSTM, Bi-GRU as the baseline models, experimental verification was carried out on the actual receptive task. The results showed that ICBLS model achieved the highest model accuracy of 95. 74% and the average attribute accuracy rate of 98. 31%. Moreover, the ablation experiment indicated that the semantic information of items could help the model to detect the language comprehension ability of the learners more accurately. ? 2024 Editorial Department of Journal of Zhengzhou University.
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