Learning path optimization method based on deep knowledge tracking and reinforcement learning

Abstract

The invention discloses a learning path optimization method based on deep knowledge tracking and reinforcement learning, and belongs to the field of adaptive learning. The method specifically comprises the following steps: for a certain student, selecting all unlearned knowledge points and knowledge points which are not repaired first as knowledge points to be selected; and carrying out one-hot coding by utilizing knowledge points of historical learning, inputting the coded knowledge points into the DKT model, and outputting a mastering level prediction value of each knowledge point to be selected. Then, selecting the knowledge point K with the highest prediction result and recommending the knowledge point K to students for learning; achieving the learning process by using a learning path optimization algorithm in the knowledge points; after the current knowledge point K passes learning, judging whether subsequent knowledge points exist or not, and if yes, adding the subsequent knowledge points into the knowledge point set to be selected, and moving out the current knowledge point K; otherwise, directly moving out the current knowledge point K, and selecting the next knowledge point to predict and learn again until the knowledge point set to be selected is empty. According to the method, the recommendation precision can be greatly improved, and the efficiency is improved under the condition of obtaining the same learning effect.

Description

technical field [0001] The invention belongs to the field of self-adaptive learning, in particular to a learning path optimization method based on deep knowledge tracking and reinforcement learning. Background technique [0002] In the process of adaptive learning, one of the key problems to be solved is to recommend the most suitable learning path for students according to their level of mastery of knowledge points, so as to obtain the best learning efficiency and effect as the goal. [0003] Learning path recommendation includes learning path recommendation between knowledge points and learning path recommendation within knowledge points. [0004] For the recommendation of learning paths between knowledge points, the most commonly used is the probabilistic graphical model technology. The specific implementation process is to use the Markov network of the probabilistic graphical model to track the mastery of a single knowledge point of a single learner; then, The Bayesian ...

AI Technical Summary

Problems solved by technology

[0009] In the existing technology, deep knowledge tracking has strong perception ability to perceive the current learning state of students, but lacks certain decision-making ability; while reinforcement learning has decision-making ability, but lacks the ability to perceive the state

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