Knowledge cognitive structure analysis method and system, computer equipment, medium and terminal

Abstract

The invention belongs to the technical field of personalized learning, and discloses a knowledge cognitive structure analysis method and system, computer equipment, a medium and a terminal, and the method comprises the steps: obtaining a joint prior feature based on a learning interaction sequence of a learner; designing a hierarchical convolutional neural network to perform spatial analysis on the learning state of the learner, and extracting spatial features including the personalized learning ability of the learner; outputting the response condition of the learner to the practice under the given heterogeneous features, and constructing the learner time-space fusion features which influence the knowledge cognitive structure and expression of the learner in the learning process; and introducing a bidirectional gate circulation unit, constructing a knowledge cognitive structure analysis model based on long-time dependence and fused spatial-temporal characteristics to dynamically diagnose the knowledge cognitive structure of the learner, and predicting the learning performance of the learner. The method is beneficial for improving the prediction precision of the knowledge cognitive structure analysis model in predicting the learning performance of the learner under the specific resources, and has certain reference significance for the development of personalized teaching.

Description

technical field [0001] The invention belongs to the technical field of personalized learning, and in particular relates to a knowledge cognitive structure analysis method, system, computer equipment, media, and terminal. Background technique [0002] At present, with the development of online teaching technology and educational informatization, various e-learning systems, such as Coursera, Edx, Khan Academy, MOOC and other large-scale open online open course platforms, intelligent tutoring systems, computer-aided education systems, etc. Gradually popularized. However, whether it is an offline teaching mode or an online teaching plan, it will be constrained by limited educational resources, resulting in educators being unable to provide personalized teaching guidance, and learners unable to obtain personalized learning services. Therefore, people try to fill the vacancy of this service through artificial intelligence technology. As a branch of artificial intelligence in the ...

AI Technical Summary

Problems solved by technology

However, the knowledge cognitive structure analysis method based on the probability map also has its shortcomings: 1) the representation of learners' knowledge cognitive structure is insufficient; 2) it relies too much on the understanding of teaching scenarios by educational experts; 3) it cannot model learners' long-term learning Timing dependence

[0005] (1) The traditional knowledge cognitive structure analysis method ignores other learning factors that affect the learner's knowledge cognitive structure and performance in the learning process, which cannot represent the real learning scene of the learner, and will also lead to inaccurate prediction of learner performance

[0006] (2) The traditional knowledge cognitive structure analysis method has a single representation method, and the deep knowledge tracking model expressed by these single features lacks consideration of various aspects of information that affects learning performance, resulting in inaccurate models when analyzing learners' cognitive structure of knowledge

[0007] (3) The results of the traditional knowledge cognitive structure analysis method are not stable enough when predicting learners' knowledge cognitive structure, but the change process of learners' knowledge cognitive structure should be relatively stable

[0008] (4) Although the traditional knowledge cognitive structure analysis method uses LSTM to model the learner interaction sequence, when the interaction sequence between the learner and the exercise is too long, the problem of gradient disappearance and gradient explosion will still occur, which leads to the prediction of learner performance. Inaccurate

[0012] (3) How to solve the problem that the prediction results of the knowledge cognitive structure analysis model are relatively fluctuating

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