{"title":"一种使用机器学习算法的数据挖掘方法,用于早期检测表现不佳的学生","authors":"E. Khor","doi":"10.1108/ijilt-09-2021-0144","DOIUrl":null,"url":null,"abstract":"PurposeThe purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance.Design/methodology/approachFor the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4).FindingsThe paper found that the decision trees algorithm outperformed other machine earning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance.Originality/valueThe work meets the originality requirement.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A data mining approach using machine learning algorithms for early detection of low-performing students\",\"authors\":\"E. Khor\",\"doi\":\"10.1108/ijilt-09-2021-0144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThe purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance.Design/methodology/approachFor the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4).FindingsThe paper found that the decision trees algorithm outperformed other machine earning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance.Originality/valueThe work meets the originality requirement.\",\"PeriodicalId\":51872,\"journal\":{\"name\":\"International Journal of Information and Learning Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2022-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information and Learning Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/ijilt-09-2021-0144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information and Learning Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijilt-09-2021-0144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A data mining approach using machine learning algorithms for early detection of low-performing students
PurposeThe purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance.Design/methodology/approachFor the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4).FindingsThe paper found that the decision trees algorithm outperformed other machine earning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance.Originality/valueThe work meets the originality requirement.
期刊介绍:
International Journal of Information and Learning Technology (IJILT) provides a forum for the sharing of the latest theories, applications, and services related to planning, developing, managing, using, and evaluating information technologies in administrative, academic, and library computing, as well as other educational technologies. Submissions can include research: -Illustrating and critiquing educational technologies -New uses of technology in education -Issue-or results-focused case studies detailing examples of technology applications in higher education -In-depth analyses of the latest theories, applications and services in the field The journal provides wide-ranging and independent coverage of the management, use and integration of information resources and learning technologies.