A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing
Journal:
arXiv
Published Date:
Jun 16, 2025
Abstract
Adaptive Curriculum Sequencing (ACS) is essential for personalized online
learning, yet current approaches struggle to balance complex educational
constraints and maintain optimization stability. This paper proposes a Memetic
Walrus Optimizer (MWO) that enhances optimization performance through three key
innovations: (1) an expert-guided strategy with aging mechanism that improves
escape from local optima; (2) an adaptive control signal framework that
dynamically balances exploration and exploitation; and (3) a three-tier
priority mechanism for generating educationally meaningful sequences. We
formulate ACS as a multi-objective optimization problem considering concept
coverage, time constraints, and learning style compatibility. Experiments on
the OULAD dataset demonstrate MWO's superior performance, achieving 95.3%
difficulty progression rate (compared to 87.2% in baseline methods) and
significantly better convergence stability (standard deviation of 18.02 versus
28.29-696.97 in competing algorithms). Additional validation on benchmark
functions confirms MWO's robust optimization capability across diverse
scenarios. The results demonstrate MWO's effectiveness in generating
personalized learning sequences while maintaining computational efficiency and
solution quality.
