Topic recommendation method and device based on deep learning and storage medium

Abstract

The invention relates to the technical field of deep learning, and provides a question recommendation method and device based on deep learning and machine learning, and a storage medium, and the method comprises the steps: obtaining the historical question-making data of a user, and inputting the historical question-making data into a deep knowledge tracking model, so as to obtain the mastering degree data of the user for each knowledge point; sorting the knowledge points according to the mastery degree data of the user for the knowledge points and the weights of the knowledge points, and obtaining target knowledge points according to a sorting result; based on the mastery degree data of the user for the target knowledge point, obtaining the correct answering probability of the user for each question under the target knowledge point through a parameter logarithm model; and recommending questions to the user based on the correct answering probability. According to the invention, the true mastery degree of students to knowledge points is calculated through the historical records of the students doing questions, and personalized question recommendation is provided for the students.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a deep learning-based topic recommendation method, device and computer-readable storage medium. Background technique [0002] According to different learning content and learning methods, the human learning process can be divided into three different types, including machine learning, teaching learning and adaptive learning, where adaptive learning usually refers to providing a corresponding learning environment for learning , examples or fields, through the learners themselves discovering and summarizing in learning, and finally forming a theory and a learning method that can solve problems independently. [0003] Among them, the core of adaptive learning is how to effectively evaluate students' problem-solving information and arrange corresponding learning paths through computers. The tools of adaptive learning mainly include adaptive content, adaptive evaluation ...

AI Technical Summary

Problems solved by technology

There are the following problems in recommending topics to students based on this exercise book: 1. Making mistakes in the topic does not necessarily mean that the students have not mastered the knowledge points

2. Answering the questions does not necessarily mean that the students have mastered the knowledge points

3. Asking students to practice repeatedly after collecting wrong questions will increase students' boredom

4. If you practice the same question repeatedly, students will only be able to master a single question type. If you encounter similar questions during or after the exam and involve other knowledge points, students may not be able to make extensions

[0005] It can be seen that the current topic recommendation products are relatively low in personalization, and cannot recommend topics based on students' true mastery of the topics, which can easily affect students' enthusiasm for learning and effective mastery of knowledge points.

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