基于学习行为的MOOC用户持续学习预测框架  

Behavior based MOOC user dropout predication framework

作　　者：陈辉[1] 白骏 殷传涛[2] 荣文戈 熊璋[1] CHEN Hui;BAI Jun;YIN Chuantao;RONG Wenge;XIONG Zhang(School of Computer Science and Engineering,Beihang University,Beijing 100191,China;School of Sino-French Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学计算机学院,北京100191 [2]北京航空航天大学中法工程师学院,北京100191

出　　处：《北京航空航天大学学报》2023年第1期74-82,共9页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(61977003)。

摘　　要：大型开放式网络课程(MOOC)的出现虽然极大地改变了人们的学习方式,但用户在MOOC平台开展学习的学习情况及完成率预测仍是目前一个重要的技术挑战。针对预测的需求,从用户的学习行为中对用户和课程进行分析,采用长短时记忆机对学习者的学习活动进行建模,采用多头注意力机制对用户和课程之间的交互活动情况进行分析,提出一个基于门控单元的特征融合框架,用于学习情况预测。在公开数据集上的结果表明:所提框架能够提升预测精度,使得MOOC平台能够尽可能早地对用户活动进行干预,从而提升整体的MOOC平台使用体验。Though the massive open online courses(MOOC) have greatly changed the way of learning, properly understanding the user’s behavior and then predication of dropout is one of the most challenging tasks. MOOC have significantly altered the way that people learn, yet one of the most difficult challenges is correctly interpreting user behavior and then predicting dropout. In this research, to improve the dropout prediction performance, we firstly analyzed users and courses from the perspective of activities by using the long short term memory mechanism. In this study, we used the long short term memory mechanism to analyze users and courses from the perspective of activities in order to improve the dropout prediction performance. Afterwards we further proposed a multi-attention based multi-perspective feature enhancement method to investigate the correlated activities among users and courses.Finally, we provided a gated mechanism-based feature integration framework for dropout prediction. The experiment study on the public dataset has shown our framework ’s promising potential, thereby making it possible to better investigate the reason beneath these phenomena and improve the overall study experience. The experiment study on the open dataset has demonstrated the promising potential of our framework, allowing us to more thoroughly explore the causes of these events and enhance the learning environment as a whole.

关 键 词：大型开放式网络课程 预测框架 用户 课程内容 学习行为 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] G434[自动化与计算机技术—计算机科学与技术]