Journal of Applied Science and Engineering

Published by Tamkang University Press

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Multi-objective reinforcement learning path planning model and cognitive load assessment in the context of music education

 2026

 Volume 32

Xiaoxuan Zhang

College of Education, Cangzhou Normal University, Cangzhou, 061000, China

Received: March 26, 2026
Accepted: April 28, 2026
Publication Date: June 4, 2026

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Block diagram of the proposed framework

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202609_32.066  

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Assessment of Cognitive Load (CL) is important in music education but current approaches do not take into account the performance of students. This work proposes SN-ARi-RNN for CL prediction based on performance aware personalization. The CL is pre-processed; features are extracted and selected with the help of SM-SSO algorithm along with EEG band mapping and MF analysis. SN-ARi-RNN predicts CL, whereas the student performance models states and clusters. Eventually, MOLERL conduct adaptive path planning with considering CL, group and task, achieving its convergence in 6412 ms.

Keywords: : Music Education, Cognitive Load (CL), student’s performance, ElectroEncephaloGram (EEG), Mental Fatigue (MF) score, Multi-Objective Reinforcement Learning (MORL).

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