Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady
{"title":"基于高粒度时间信息的慕课行为预测","authors":"Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady","doi":"10.1145/2724660.2728687","DOIUrl":null,"url":null,"abstract":"In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Behavior Prediction in MOOCs using Higher Granularity Temporal Information\",\"authors\":\"Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady\",\"doi\":\"10.1145/2724660.2728687\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.\",\"PeriodicalId\":20664,\"journal\":{\"name\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2724660.2728687\",\"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 Second (2015) ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2724660.2728687","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Behavior Prediction in MOOCs using Higher Granularity Temporal Information
In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.