Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout predictio...Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.展开更多
To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.Thi...To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.This paper demonstrates the whole process of educational knowledge graph construction for reference.And this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 triples.Notably,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links.Besides,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph.This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.展开更多
基金partially supported by the National Natural Science Foundation of China (Nos. 61866007, 61363029, 61662014, 61763007, and U1811264)the Natural Science Foundation of Guangxi District (No. 2018GXNSFDA138006)+2 种基金Guangxi Key Laboratory of Trusted Software (No. KX201721)Humanities and Social Sciences Research Projects of the Ministry of Education (No. 17JDGC022)Chongqing Higher Education Reform Project (No. 183137)
文摘Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.
基金supported by the National Key Research and Development Program of China under Grant No.2018YFB1004502the National Natural Science Foundation of China under Grant Nos.61532001,61702532 and 61303190.
文摘To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.This paper demonstrates the whole process of educational knowledge graph construction for reference.And this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 triples.Notably,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links.Besides,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph.This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.
