Knowledge tracking method and system integrating long short-term memory and Bayesian network

Abstract

The invention discloses a knowledge tracking method and system integrating long-short-term memory and Bayesian network, which calculates the Bayesian value of knowledge components corresponding to the current time series by establishing a cognitive data set including time series and a long-term short-term memory neural network. The parameter group of the Yassian knowledge tracking model, so that the Bayesian knowledge tracking model is used to calculate the correct probability prediction value of the learner's answer to the topic of the current time series, and by comparing the correctness of the answer to the topic of the current time series in the cognitive data set The true value of the long-short-term memory neural network model loss function corresponding to the current time series is obtained, thereby obtaining the optimized value of the weight parameter matrix and the optimized value of the bias parameter matrix; traversing all the time series of the cognitive data set, and obtaining the long-term short-term memory The optimal value of the weight parameter matrix of the neural network model and the optimal value of the deviation parameter matrix; thereby realizing the prediction of the cognitive state of the learner to be tested, and planning and / or learning path of the learner according to the prediction of the cognitive state of the learner Or the construction of knowledge graphs.

Description

technical field [0001] The invention belongs to the field of knowledge tracking, and in particular relates to a knowledge tracking method and system integrating long-short-term memory and Bayesian network. Background technique [0002] Knowledge tracking is to model the learner's knowledge learning state, so that we can track the learner's mastery of knowledge points, and further predict the learner's performance in the next answer. Knowledge tracking can capture the current real needs of learners, and is the core task in learner modeling. However, due to the diversity of knowledge and the complexity of the human brain, the human learning process is complex and changeable, which is why knowledge tracking is very difficult. [0003] There are two classic solution models in the field of knowledge tracking. One of the classic models is Hidden Markov Model (Hidden Markov Model), represented by Bayesian Knowledge Tracing (BKT), which models two learning states of learners for K...

AI Technical Summary

Problems solved by technology

The advantage of this model is that the cyclic neural network is easy to train and it is easy to obtain better model prediction results; the disadvantage is that the "black box" problem of the neural network has always existed, and it cannot provide a clear semantic explanation for the model

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