Multi-source heterogeneous learning path planning method based on personalized constraints

Through a multi-source heterogeneous learning path planning method based on personalized constraints, neural collaborative filtering and cognitive diagnostic models are used to dynamically evaluate learners' interest and mastery status, and combined with principal component analysis and multi-objective optimization algorithm to generate the optimal learning path, the problem of single path generation and poor adaptability in the existing technology is solved, and personalized and dynamic learning path planning is realized.

CN120409857AInactive Publication Date: 2025-08-01SICHUAN QIMINGDAREN TECH CO LTD
6 Cites 4 Cited by

Patent Information

Application Number
CN202510926531.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-07
Publication Date
2025-08-01
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing learning path planning system has a single path generation method, poor adaptability, static ability evaluation, insufficient data structure utilization, lack of multi-objective optimization capabilities, and cannot dynamically adapt to learner development.

Method used

A multi-source heterogeneous learning path planning method based on personalized constraints is adopted to model features by obtaining learners' multi-source behavior data, using neural collaborative filtering algorithm to predict interest and mastery probability, dynamic evaluation is performed in combination with cognitive diagnostic models, and the optimal learning path is generated through principal component analysis and multi-objective optimization algorithm.

Benefits of technology

It realizes personalized and dynamic learning path planning, improves the accuracy of interest prediction and path generation, accurately adjusts the learning path, and improves the synchronization of learning effects and multi-objective optimization capabilities.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.
Need to check novelty before this filing date? Find Prior Art