Markdy Y. Orong, Roseclaremath A. Caroro, Geraldine d. Durias, J. A. Cabrera, Herwina Lonzon, Gretel T. Ricalde
{"title":"预测分析方法在确定菲律宾高等教育机构学生流失的预测因素","authors":"Markdy Y. Orong, Roseclaremath A. Caroro, Geraldine d. Durias, J. A. Cabrera, Herwina Lonzon, Gretel T. Ricalde","doi":"10.1145/3378936.3378956","DOIUrl":null,"url":null,"abstract":"The paper identified the predictors of student attrition in the Higher Education Institution (HEI) through predictive analytics approach. The prediction model used in the study includes variable optimization through Genetic Algorithm (GA) and decision tree generation phase through C4.5 algorithm. The college student leavers' data from one of the Higher Education in the Philippines from the school year 2008-2009 until the school year 2018-2019 was used as datasets of the study. Out of forty identified reasons for leaving as variables, there were nine (9) identified predictors of student attrition. Through the identified predictors, administrators of educational institutions may design intervention plans related to the student attrition.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Predictive Analytics Approach in Determining the Predictors of Student Attrition in the Higher Education Institutions in the Philippines\",\"authors\":\"Markdy Y. Orong, Roseclaremath A. Caroro, Geraldine d. Durias, J. A. Cabrera, Herwina Lonzon, Gretel T. Ricalde\",\"doi\":\"10.1145/3378936.3378956\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper identified the predictors of student attrition in the Higher Education Institution (HEI) through predictive analytics approach. The prediction model used in the study includes variable optimization through Genetic Algorithm (GA) and decision tree generation phase through C4.5 algorithm. The college student leavers' data from one of the Higher Education in the Philippines from the school year 2008-2009 until the school year 2018-2019 was used as datasets of the study. Out of forty identified reasons for leaving as variables, there were nine (9) identified predictors of student attrition. Through the identified predictors, administrators of educational institutions may design intervention plans related to the student attrition.\",\"PeriodicalId\":304149,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Software Engineering and Information Management\",\"volume\":\"146 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Software Engineering and Information Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3378936.3378956\",\"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 3rd International Conference on Software Engineering and Information Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3378936.3378956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Predictive Analytics Approach in Determining the Predictors of Student Attrition in the Higher Education Institutions in the Philippines
The paper identified the predictors of student attrition in the Higher Education Institution (HEI) through predictive analytics approach. The prediction model used in the study includes variable optimization through Genetic Algorithm (GA) and decision tree generation phase through C4.5 algorithm. The college student leavers' data from one of the Higher Education in the Philippines from the school year 2008-2009 until the school year 2018-2019 was used as datasets of the study. Out of forty identified reasons for leaving as variables, there were nine (9) identified predictors of student attrition. Through the identified predictors, administrators of educational institutions may design intervention plans related to the student attrition.