A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing
This addresses the challenge of balancing educational constraints in online learning with an incremental method that enhances optimization performance.
The paper tackled the problem of Adaptive Curriculum Sequencing for personalized online learning by proposing a Memetic Walrus Optimizer, which achieved a 95.3% difficulty progression rate compared to 87.2% in baselines and improved convergence stability with a standard deviation of 18.02 versus 28.29-696.97 in competing algorithms.
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.