Research on Constructing Personalized Learner Profiles Based on Multi-Feature Fusion  

作　　者：Xing Pan Meixiu Lu 

机构地区：[1]Center for Contemporary Education Technology,Guangdong University of Foreign Studies,Guangzhou,China [2]School of Information Science and Technology(School of Cyber Security),Guangdong University of Foreign Studies,Guangzhou,China

出　　处：《Journal of Electronic Research and Application》2025年第2期274-284,共11页电子研究与应用

基　　金：This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124);Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.

摘　　要：This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.

关 键 词：Learner profile Multi-feature fusion Dynamic features Personalized recommendation Educational technology 

分 类 号：G434[文化科学—教育学]