A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing

TL;DR

This paper introduces a Memetic Walrus Optimizer with expert-guided strategies for adaptive curriculum sequencing, significantly improving personalization and stability in online learning sequence optimization.

Contribution

It presents a novel optimization algorithm with expert-guided aging, adaptive control, and priority mechanisms tailored for personalized educational sequencing.

Findings

Achieved 95.3% difficulty progression rate on OULAD dataset.

Demonstrated superior convergence stability with lower standard deviation.

Validated robustness across diverse benchmark functions.

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.