Knowledge tracking method and system fusing question difficulty

Abstract

The invention discloses a knowledge tracking method and system fusing question difficulty. The method comprises the following steps: acquiring a question qt and an answer rt answered by a learner at a t moment and interaction information when the learner answers the question; obtaining a concept matrix Mk and a concept mastering matrix at the moment t, and calculating question difficulty dt according to the interaction information; obtaining the relevancy wt (i) of the question qt and the ith concept; calculating to obtain the overall mastering degree kst of the learner to the concepts related to the question qt; outputting the prediction accuracy prt of the question qt answered by the learner and the prediction difficulty pdt of the question qt for the learner; training a prediction model, wherein the training target is to minimize the difference between the prediction accuracy prt and the answer rt and the difference between the prediction difficulty pdt and the question difficulty dt; and updating the concept mastering matrix of the next time step. While predicting the accuracy of the learner, the invention also predicts the difficulty of the question to the learner, and enhances the expression of the model in the aspect of the knowledge mastering state of the learner.

Description

technical field [0001] The invention belongs to the technical field of knowledge tracking, and more specifically relates to a knowledge tracking method and system that integrates the difficulty of questions. Background technique [0002] The purpose of knowledge tracing (Knowledge Tracing) is to represent the state of knowledge mastery of learners. The current knowledge tracking model mainly models the learner's knowledge learning process based on the learner's historical question answer records, and predicts the learner's performance in answering questions in the future. Its input is generally the learner's question answer record, and the output is the probability of the learner correctly answering the next question. [0003] It can be seen that the current knowledge tracking model regards the "probability of the learner to correctly answer the next question" as the learner's knowledge mastery status. Obviously, only using the "probability of the learner to correctly answ...

AI Technical Summary

Problems solved by technology

First, it is relatively rough and does not objectively reflect the difficulty of the topic

That is, the time spent by the learner, the help used, the number of attempts, etc. are not reflected

Second, DKVMN only uses the representation of the difficulty of the questions in the middle of the model, and the model does not give the prediction results of the difficulty of the questions

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