软件导刊2026，Vol.25Issue(4)：191-198,8.DOI:10.11907/rjdk.251112
可变形卷积神经网络赋能学生在线学习退课预测
Student Drop-out Prediction for Online Learning with Deformable Convolutional Networks
摘要
Abstract
To address the issue of high dropout rates in Massive Open Online Courses(MOOCs),this paper has built a predictive model based on deformable convolutional networks to predict student dropouts.With the KDD Cup 2015 dataset,experimental results demonstrate that this method outperforms existing machine learning and deep learning models.Additionally,by analyzing student behaviors to explain drop-out predictions,this paper has improved the credibility of the results.This analysis of dropout prediction assists teachers in providing personal-ized guidance and help students master the learning as an intelligent assistant.关键词
大规模开放在线课程/退课预测/学业帮扶/智能助教/智能伴学Key words
massive open online courses/dropout prediction/academic support/intelligent teaching assistant/intelligent companion learn-ing分类
信息技术与安全科学引用本文复制引用
邵永伟,马坤,荆山,陈贞翔..可变形卷积神经网络赋能学生在线学习退课预测[J].软件导刊,2026,25(4):191-198,8.基金项目
山东省本科教学改革研究项目(Z2024139) （Z2024139）
山东省高等教育学会高等教育研究专项课题(SDGJ2023C05) （SDGJ2023C05）
济南大学教学研究重点项目(JZ2411) （JZ2411）
山东省电化教育馆人工智能教育研究课题(SDDJ202501004) （SDDJ202501004）
