{"title":"深度知识追踪与mooc参与","authors":"Kritphong Mongkhonvanit, K. Kanopka, David Lang","doi":"10.1145/3303772.3303830","DOIUrl":null,"url":null,"abstract":"MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted interventions can be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"Deep Knowledge Tracing and Engagement with MOOCs\",\"authors\":\"Kritphong Mongkhonvanit, K. Kanopka, David Lang\",\"doi\":\"10.1145/3303772.3303830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted interventions can be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding.\",\"PeriodicalId\":382957,\"journal\":{\"name\":\"Proceedings of the 9th International Conference on Learning Analytics & Knowledge\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th International Conference on Learning Analytics & Knowledge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3303772.3303830\",\"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 9th International Conference on Learning Analytics & Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3303772.3303830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted interventions can be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding.