Mirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, Mahmoud Abou Naaj
{"title":"使用随机森林算法预测学生课程成绩:预测因子的重要性分析","authors":"Mirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, Mahmoud Abou Naaj","doi":"10.1016/j.tine.2023.100214","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.</p></div><div><h3>Method</h3><p>In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.</p></div><div><h3>Results</h3><p>Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.</p></div><div><h3>Conclusion</h3><p>Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.</p></div>","PeriodicalId":46228,"journal":{"name":"Trends in Neuroscience and Education","volume":"33 ","pages":"Article 100214"},"PeriodicalIF":3.4000,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Student course grade prediction using the random forest algorithm: Analysis of predictors' importance\",\"authors\":\"Mirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, Mahmoud Abou Naaj\",\"doi\":\"10.1016/j.tine.2023.100214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.</p></div><div><h3>Method</h3><p>In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.</p></div><div><h3>Results</h3><p>Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.</p></div><div><h3>Conclusion</h3><p>Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.</p></div>\",\"PeriodicalId\":46228,\"journal\":{\"name\":\"Trends in Neuroscience and Education\",\"volume\":\"33 \",\"pages\":\"Article 100214\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Neuroscience and Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211949323000170\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Neuroscience and Education","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211949323000170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Student course grade prediction using the random forest algorithm: Analysis of predictors' importance
Background
Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.
Method
In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.
Results
Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.
Conclusion
Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.