Research on Exercise Recommendation Fusing Student Knowledge State and Chaotic Firefly Algorithm

doi:10.3778/j.issn.1673-9418.2407092

Abstract

Abstract: The rapid development of information technology and artificial intelligence is driving progress in the field of education towards greater intelligence and personalization. Personalized education aims to provide personalized learning content and exercises based on students’ knowledge status and learning characteristics, in order to optimize learning outcomes and experiences. Personalized exercise recommendation is a key link in the field of smart education. By perceiving students’ knowledge status and recommending suitable exercises, it effectively improves learning efficiency and grades. However, traditional recommendation methods overlook the personalized needs and changes in knowledge status of students, resulting in inaccurate recommendation results. In response to the above issues, this paper proposes an exercise recommendation model (SKS-CFA-ER) that integrates student knowledge state and chaotic firefly algorithm. The algorithm framework consists of two core modules: the student knowledge state perception (KSP) module and the exercise list recommendation (REL) module. The KSP module utilizes deep learning techniques to perceive the coverage and mastery level of student knowledge concepts, and construct a student knowledge state model. The REL module uses KSP to construct a student knowledge state model. The prediction results of the module are filtered and optimized through the chaotic firefly algorithm to generate the optimal personalized exercise recommendation list. Finally, extensive exercise recommendation experiments are conducted on three datasets, and the effectiveness and superiority of the proposed model are verified.

Key words: personalized exercise recommendation, online education, collaborative filtering, deep learning, chaotic firefly algorithm

摘要： 信息技术和人工智能的迅速发展正推动教育领域实现更智能化和个性化的进步，个性化教育旨在根据学生的知识状态和学习特点，提供个性化的学习内容和习题，以优化学习效果和体验。个性化习题推荐是智慧教育领域的关键环节，通过感知学生的知识状态推荐适合的习题，有效提高学习效率和成绩。然而，传统的推荐方法忽视了学生的个性化需求和知识状态变化，导致推荐结果不准确。针对上述问题，提出了融合学生知识状态与混沌萤火虫算法的习题推荐模型（SKS-CFA-ER）。该算法框架包含两个核心模块：学生知识状态感知（KSP）模块和习题列表推荐（REL）模块。KSP模块利用深度学习技术感知学生的知识概念覆盖率和学习掌握程度，构建学生的知识状态模型；REL模块根据KSP模块的预测结果，通过混沌萤火虫算法过滤和优化习题集，生成最优的个性化习题推荐列表。在三个数据集上进行了广泛的习题推荐实验，并验证了所提模型的有效性与优越性。

ZHOU Nan, DONG Yongquan, YAN Linke, JIN Jiayong, HE Bugui. Research on Exercise Recommendation Fusing Student Knowledge State and Chaotic Firefly Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(6): 1620-1631.

周楠, 董永权, 闫林克, 金家永, 贺步贵. 融合学生知识状态与混沌萤火虫算法的习题推荐研究[J]. 计算机科学与探索, 2025, 19(6): 1620-1631.

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