Adaptive optimization methods, systems, electronic devices and storage media for course structure

By constructing learner state vectors and multi-objective optimization models, and dynamically adjusting course structure parameters, the problem of learner state adaptation in online courses is solved, realizing personalized course control and dynamic management of cognitive load.

CN122089532AActive Publication Date: 2026-05-26BEISEN CLOUD COMPUTING CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-22
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing online courses lack fine-grained adaptation to individual learner states, do not systematically model cognitive load, use simplistic ability estimation methods, do not unify the modeling of explicit learner profiles and implicit ability states, and do not systematize dynamic support/fading strategies, resulting in a lack of dynamic adjustment capabilities in the course structure.

Method used

By constructing learner state vectors and integrating learner profiles, IRT/3PL ability estimation, and cognitive load estimation, the curriculum structure parameters are dynamically adjusted through a multi-objective optimization decision model to achieve adaptive optimization of the curriculum structure.

Benefits of technology

It enables fine-grained, personalized control over the course structure, dynamically adjusts the support strength, avoids cognitive overload or underload, and improves learning outcomes.

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Abstract

This invention provides a method, system, electronic device, and storage medium for adaptive optimization of curriculum structure, relating to the fields of educational technology and artificial intelligence. Based on existing curriculum structures, this invention combines learner profiling, ability estimation, and cognitive load estimation to perform real-time adaptive control of the curriculum structure for individual learners, achieving fine-grained personalized control of the curriculum structure. Furthermore, it systematizes dynamic support / fading strategies into calculable support strength parameters, enabling dynamic control of cognitive load.
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Citation Information

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