计算机科学 ›› 2026, Vol. 53 ›› Issue (5): 1-12.doi: 10.11896/jsjkx.250600184
孙奕菲, 李咏桉
SUN Yifei, LI Yongan
摘要： 个性化学习资源推荐是技术与教育深度融合的高级表现形式,近年来受到学术界的广泛关注。现有综述在基础理论框架、推荐方法和评估指标等方面进行了有价值的论述。在此基础上,可以进一步丰富个性化学习资源推荐的分类体系及算法梳理,尤其是对传统推荐方法的全面回顾和对新兴大语言模型在个性化学习推荐中的应用研究。通过构建多维分类框架,从应用场景、算法类别和学习者特征建模3个维度对个性化学习资源推荐进行了系统分类,全面解析了传统推荐算法、基于知识的算法、机器学习算法、智能优化算法、深度学习算法及强化学习算法的技术原理及最新进展,系统分析了个性化学习资源推荐面临的技术挑战和学习者角度面临的困境,并据此提出了融合多种推荐方法、引入教育理论指导和提升数据质量等未来研究方向。该综述旨在为教育技术研究者与实践者提供系统化的理论框架与技术路线图,促进个性化学习生态系统的持续优化与创新发展。
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