文章基本信息

标题：Predictive model selection for completion rate in Massive Open Online Courses
本地全文：下载
作者：Annamaria De Santis ; Katia Sannicandro ; Claudia Bellini 等
期刊名称：Je-LKS
印刷版ISSN：1826-6223
电子版ISSN：1971-8829
出版年度：2019
卷号：15
期号：3
页码：145-159
DOI：10.20368/1971-8829/1135034
出版社：Casalini Libri
摘要：In this paper we introduce an approach for selecting a linear model to estimate, in a predictive way, the completion rate of massive open online courses (MOOCs). Data are derived from LMS analytics and nominal surveys.The sample comprises 722 observations (users) carried out in seven courses on EduOpen, the Italian MOOCs platform. We used 24 independent variables (predictors), categorised into four groups (User Profile, User Engagement, User Behaviour, Course Profile). As response variables we examined both the course completion status and the completion rate of the learning activities.A first analysis concerned the correlation between the predictors within each group and between the different groups, as well as that between all the dependent variables and the two response variables.
关键词：MOOCs; Predictive Model; User Profile; Completion Rate; Learning Analytics