E. Lauría, Joshua D. Baron, Mallika Devireddy, V. Sundararaju, Sandeep M. Jayaprakash
{"title":"挖掘学术数据以提高大学生保留率:一个开源的视角","authors":"E. Lauría, Joshua D. Baron, Mallika Devireddy, V. Sundararaju, Sandeep M. Jayaprakash","doi":"10.1145/2330601.2330637","DOIUrl":null,"url":null,"abstract":"In this paper we report ongoing research on the Open Academic Analytics Initiative (OAAI), a project aimed at increasing college student retention by performing early detection of academic risk using data mining methods. The paper describes the goals and objectives of the OAAI, and lays out a methodological framework to develop models that can be used to perform inferential queries on student performance using open source course management system data and student academic records. Preliminary results on initial model development using several data mining algorithms for classification are presented.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"83","resultStr":"{\"title\":\"Mining academic data to improve college student retention: an open source perspective\",\"authors\":\"E. Lauría, Joshua D. Baron, Mallika Devireddy, V. Sundararaju, Sandeep M. Jayaprakash\",\"doi\":\"10.1145/2330601.2330637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we report ongoing research on the Open Academic Analytics Initiative (OAAI), a project aimed at increasing college student retention by performing early detection of academic risk using data mining methods. The paper describes the goals and objectives of the OAAI, and lays out a methodological framework to develop models that can be used to perform inferential queries on student performance using open source course management system data and student academic records. Preliminary results on initial model development using several data mining algorithms for classification are presented.\",\"PeriodicalId\":311750,\"journal\":{\"name\":\"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"83\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2330601.2330637\",\"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 2nd International Conference on Learning Analytics and Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2330601.2330637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining academic data to improve college student retention: an open source perspective
In this paper we report ongoing research on the Open Academic Analytics Initiative (OAAI), a project aimed at increasing college student retention by performing early detection of academic risk using data mining methods. The paper describes the goals and objectives of the OAAI, and lays out a methodological framework to develop models that can be used to perform inferential queries on student performance using open source course management system data and student academic records. Preliminary results on initial model development using several data mining algorithms for classification are presented.