Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

Abstract

To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time.

Enhancing Online Learning Experiences through Personalization Utilizing Recommendation Algorithms

“…Their research focuses on optimizing the learning path recommendation process, emphasizing real-time, high-quality recommendations. This aligns with the current study's interest in enhancing the learning experience through advanced recommendation algorithms [19]. The related work discussed here provides valuable insights into the field of personalized learning recommendation, encompassing user profiling, collaborative filtering, recommendation algorithms, and dynamic personalization.…”

“…The study's insights into user-centric recommendation systems contribute to the broader understanding of personalized learning [18]. Li et al (2023) proposes an online personalized learning path recommendation model based on the saltatory evolution ant colony optimization (SEACO) algorithm [19]. Their research focuses on optimizing the learning path recommendation process, emphasizing real-time, high-quality recommendations.…”

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

“…The ant colony optimization algorithm has been applied in diverse contexts [27,28]. For example, to provide quick, accurate, real-time interactive, and high-quality learning path recommendations, Li et al [29] developed the online personalized learning path recommendation model (OPLPRM), which was based on the saltatory evolution ant colony optimization (SEACO) algorithm. The model tackles the delayed convergence and low accuracy difficulties that occur when online learning route recommendation problems are solved using the conventional ant colony optimization (ACO) technique.…”