Speed-Accuracy Tradeoff-based Deep Cognitive Diagnostic Model

In intelligent education,cognitive diagnosis analyzes students' learning behavior data to understand their cognitive state.Existing cognitive diagnostic models based on deep learning methods assume by default that students have enough reaction time to fully exert the level of knowledge mastery during the response process,and do not consider the impact of the trade-off strategy between the speed and accuracy of student's response during the response process on the exertion of the level of know-ledge mastery.Aiming at the above problem,a deep cognitive diagnostic model based on speed-accuracy trade-off is proposed,which firstly constructs a cognitive style fuzzy set to explain the students' trade-off strategy,and then simulates the speed-accuracy trade-off relationship in the process of the learners' response through the dynamic logistic regression function,so as to rea-lize the differentiated diagnosis of the students' theoretically highest level of knowledge mastery from the level of knowledge mastery they have played out in the actual response.In addition,the reaction time attribute and exercise type attribute are introduced to more accurately characterize the topic parameters in the cognitive diagnostic interaction function.Numerous experiments show that the model not only improves the accuracy by 2.58%,2.86%,and 5.18% compared to similar optimal models on the three publicly available datasets,but also provides a superior explanation of the prediction results at the level of response time.