{"title":"预测MOOC中参与度指标的下降","authors":"Miguel L. Bote-Lorenzo, E. Gómez-Sánchez","doi":"10.1145/3027385.3027387","DOIUrl":null,"url":null,"abstract":"Predicting the decrease of students' engagement in typical MOOC tasks such as watching lecture videos or submitting assignments is key to trigger timely interventions in order to try to avoid the disengagement before it takes place. This paper proposes an approach to build the necessary predictive models using students' data that becomes available during a course. The approach was employed in an experimental study to predict the decrease of three different engagement indicators in a MOOC. The results suggest its feasibility with values of area under the curve for different predictors ranging from 0.718 to 0.914.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":"{\"title\":\"Predicting the decrease of engagement indicators in a MOOC\",\"authors\":\"Miguel L. Bote-Lorenzo, E. Gómez-Sánchez\",\"doi\":\"10.1145/3027385.3027387\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting the decrease of students' engagement in typical MOOC tasks such as watching lecture videos or submitting assignments is key to trigger timely interventions in order to try to avoid the disengagement before it takes place. This paper proposes an approach to build the necessary predictive models using students' data that becomes available during a course. The approach was employed in an experimental study to predict the decrease of three different engagement indicators in a MOOC. The results suggest its feasibility with values of area under the curve for different predictors ranging from 0.718 to 0.914.\",\"PeriodicalId\":160897,\"journal\":{\"name\":\"Proceedings of the Seventh International Learning Analytics & Knowledge Conference\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"63\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Seventh International Learning Analytics & Knowledge Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3027385.3027387\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3027385.3027387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting the decrease of engagement indicators in a MOOC
Predicting the decrease of students' engagement in typical MOOC tasks such as watching lecture videos or submitting assignments is key to trigger timely interventions in order to try to avoid the disengagement before it takes place. This paper proposes an approach to build the necessary predictive models using students' data that becomes available during a course. The approach was employed in an experimental study to predict the decrease of three different engagement indicators in a MOOC. The results suggest its feasibility with values of area under the curve for different predictors ranging from 0.718 to 0.914.