大规模私有在线课程(MPOCs)学习者学习行为RFT分类研究  

RFT Classification of Learners'Learning Behaviors in Massive-scale Private Online Courses(MPOCs)

作　　者：杜俐蓉 张志华 陆宝华[2] Du Lirong;Zhang Zhihua;Lu Baohua(Jiangsu Open University)

机构地区：[1]江苏开放大学教学质量管理办公室 [2]江苏开放大学党委巡察工作领导小组办公室

出　　处：《终身教育研究》2025年第2期73-79,共7页Lifelong Education Research

基　　金：2022年江苏高校哲学社会科学研究一般项目课题“数据驱动视阈下开放教育教学质量监控机制研究”(2022SJYB0826);2023年度江苏省教育科学规划课题“开放教育数字化转型的成熟度模型构建与发展路径研究”(C/2023/02/05)。

摘　　要：随着教育大数据技术的深入发展,大量学习行为数据得以采集和分析,为教学研究与实践提供了数据支撑。本文借鉴RFM经典模型,创新性地构建了面向在线学习者群体分类的RFT改进模型,对大规模私有在线课程(MPOCs)中学习者的学习行为特征展开研究。基于对3门课程1.5万名学习者行为数据的深入分析,成功识别出八种学习者类型,从“强”到“弱”依次为忠诚高频深学型、忠诚高频浅学型、忠诚低频深学型、忠诚低频浅学型、一般高频深学型、一般高频浅学型、一般低频深学型和一般低频浅学型。研究进一步总结了不同类型学习者的行为特点,提出了针对性学习支持策略,以期提升学习者的学习成效和学习体验,为在线教育个性化支持与优化提供重要参考。With the in-depth development of big data technology in education,a large number of learning behavior data in the field of education can be collected and analyzed,which provides data support for teaching research and practice.Based on the classical RFM model in the field of e-commerce,this paper innovatively constructs an improved RFT model for group classification of online learners,and studies the learning behavior characteristics of learners in massive-scale private online courses(MPOCs).Based on the in-depth analysis of the behavior data of 15,000 learners in the three courses,eight types of learners were successfully identified,from"strong"to"weak",which were loyal high-frequency deep learning,loyal high-frequency deep learning,loyal low-frequency deep learning,loyal low-frequency shallow learning,general high-frequency deep learning,general high-frequency deep learning,general low-frequency deep learning,and general low-frequency shallow learning.This study further summarizes the behavioral characteristics of different types of learners,and proposes targeted learning support strategies to improve learners'learning effectiveness and learning experience,which provides an important reference for personalized support and optimization of online education.

关 键 词：RFT模型 MPOCs 学习行为分析 个性化学习支持服务 

分 类 号：G63[文化科学—教育学]