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

2025-06-16

AI Summary

To address the challenge of simultaneously satisfying complex pedagogical constraints and ensuring optimization stability in Adaptive Course Sequencing (ACS) for personalized online learning, this paper formulates course sequence generation as a multi-objective constrained optimization problem. Our method integrates biologically inspired optimization (Walrus Optimizer), memetic algorithms, and multi-objective constraint modeling. We propose three key innovations: (1) an expert-guided aging mechanism to enhance population diversity and convergence robustness; (2) a dynamic exploration–exploitation balancing framework to improve search efficiency; and (3) a three-tier priority educational semantic sequencing mechanism that explicitly incorporates instructional logic, cognitive load, and knowledge dependencies. Evaluated on the OULAD dataset, our approach achieves a 95.3% difficulty progression rate—significantly outperforming baselines—with a convergence standard deviation of only 18.02, demonstrating markedly improved stability. Effectiveness is further validated across multiple benchmark functions.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Learning to Search
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Balancing complex educational constraints in adaptive curriculum sequencing
• Escaping local optima in personalized online learning optimization
• Maintaining optimization stability while generating meaningful learning sequences

Innovation

• Expert-guided strategy with aging mechanism
• Adaptive control signal framework
• Three-tier priority mechanism for sequences