Personalized learning supported by learning analytics: a systematic review of functions, pathways, and educational outcomes

被引:1
作者
Ning, Jing [1 ]
Ma, Zhiqiang [2 ]
Yao, Jiajia [2 ]
Wang, Qiyun [3 ]
Zhang, Binger [2 ]
机构
[1] Jiangnan Univ, Sch Design, Wuxi, Peoples R China
[2] Jiangnan Univ, Sch Humanities, Wuxi, Peoples R China
[3] Nanyang Technol Univ, Natl Inst Educ, Singapore, Singapore
关键词
Personalized learning; learning analytics; systematic review; educational outcomes; data-driven analysis; pathways of technological integration;
D O I
10.1080/10494820.2025.2478437
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This systematic review explores the integration of learning analytics within personalized learning frameworks, highlighting its functions, pathways, and educational outcomes. Personalized learning, characterized by tailored educational experiences, is increasingly supported by learning analytics, which leverages data to enhance instructional design and learner engagement. The review synthesizes findings from 39 empirical studies published between 2019 and 2024, revealing three primary functions of learning analytics: data collection and analysis, personalized feedback mechanisms, and optimization of teaching strategies. Additionally, it identifies key pathways for effective integration, including data-driven feedback systems and the dynamic adjustment of learning paths through technological platforms. The outcomes associated with these practices demonstrate significant improvements in academic achievement, learning processes, and learner motivation. This research underscores the potential of learning analytics to foster adaptive learning environments that meet diverse learner needs while addressing challenges related to data privacy and ethical considerations. The findings provide valuable insights for educators and researchers aiming to enhance personalized learning through data-informed strategies, ultimately contributing to the advancement of educational practices in a rapidly evolving digital landscape.
引用
收藏
页数:23
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