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<p>Improving the effectiveness of education requires progress on both sides of the learning equation: students need to enhance their learning capabilities, and teachers need to refine their teaching strategies. The proposed approach introduces a sophisticated algorithm for improving education, enhancing students’ learning capabilities, and refining teachers’ instructional strategies. A multi-objective educational text clustering algorithm is introduced, combining the K-means algorithm’s simplicity and efficiency with the Krill Swarm Algorithm’s robust global search capabilities. The algorithm uses K-means clustering results as the initial population for the krill swarm, ensuring a well-distributed starting point for the optimization process. Genetic crossover and mutation operations are introduced to overcome the potential stagnation of krill swarm populations in local optima. The krill population’s motion is guided by three key components: induced motion, foraging motion, and random diffusion. These strategies ensure a balance between exploration and exploitation. The algorithm employs a fitness function based on cosine similarity and Euclidean distance, capturing semantic similarity between educational text items and ensuring clusters are compact and well-separated. This hybrid algorithm achieves a sophisticated balance between computational efficiency and clustering quality, offering a powerful tool for improving educational outcomes through advanced text analysis. For students, it enables better organization and access to learning materials, facilitating personalized learning pathways. At the same time, it provides insights into educational content, helping teachers identify patterns, gaps, and areas for improvement in their teaching resources.</p>

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RETRACTED ARTICLE: Analysis of college students’ physical education learning effect and quality based on text data fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月14日

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  • Zhiqing Dai

摘要

Improving the effectiveness of education requires progress on both sides of the learning equation: students need to enhance their learning capabilities, and teachers need to refine their teaching strategies. The proposed approach introduces a sophisticated algorithm for improving education, enhancing students’ learning capabilities, and refining teachers’ instructional strategies. A multi-objective educational text clustering algorithm is introduced, combining the K-means algorithm’s simplicity and efficiency with the Krill Swarm Algorithm’s robust global search capabilities. The algorithm uses K-means clustering results as the initial population for the krill swarm, ensuring a well-distributed starting point for the optimization process. Genetic crossover and mutation operations are introduced to overcome the potential stagnation of krill swarm populations in local optima. The krill population’s motion is guided by three key components: induced motion, foraging motion, and random diffusion. These strategies ensure a balance between exploration and exploitation. The algorithm employs a fitness function based on cosine similarity and Euclidean distance, capturing semantic similarity between educational text items and ensuring clusters are compact and well-separated. This hybrid algorithm achieves a sophisticated balance between computational efficiency and clustering quality, offering a powerful tool for improving educational outcomes through advanced text analysis. For students, it enables better organization and access to learning materials, facilitating personalized learning pathways. At the same time, it provides insights into educational content, helping teachers identify patterns, gaps, and areas for improvement in their teaching resources.