Widya Lestari DDS, MDSc, PhD, Adilah S. Abdullah BDS, Afifah M. A. Amin BDS, Nurfaridah BSc, Cortino Sukotjo DDS, PhD, MMSc, MHPE, Azlini Ismail B Biotech, PhD, Mohamad Shafiq Mohd Ibrahim BSc, MSc, PhD, Nashuha Insani BSc, Chandra P. Utomo BSc, MS
{"title":"基于大学前成绩的人工智能预测口腔医学临床前学生的学习成绩:初步研究。","authors":"Widya Lestari DDS, MDSc, PhD, Adilah S. Abdullah BDS, Afifah M. A. Amin BDS, Nurfaridah BSc, Cortino Sukotjo DDS, PhD, MMSc, MHPE, Azlini Ismail B Biotech, PhD, Mohamad Shafiq Mohd Ibrahim BSc, MSc, PhD, Nashuha Insani BSc, Chandra P. Utomo BSc, MS","doi":"10.1002/jdd.13673","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Purpose/Objectives</h3>\n \n <p>Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clinical dental students based on admission results using artificial intelligence machine learning (ML) models, and Pearson correlation coefficient (PCC).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. Academic performance prediction in pre-clinical years was made using three input parameters: age during admission, pre-university Cumulative Grade Point Average (CGPA), and total matriculation semester. PCC was deployed to identify the correlation between pre-university CGPA and dental school grades. The proposed models’ classification accuracy ranged from 29% to 57%, ranked from highest to lowest as follows: RF, SVM, DT, and LR. Pre-university CGPA was shown to be predictive of dental students’ academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. In forecasting failure, LR predicted three grades with the highest recall, SVM predicted two grades, and DT predicted one. RF performance was insignificant.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The findings demonstrated the application of ML algorithms and PCC to predict dental students' academic performance. However, it was limited by several factors. Each algorithm has unique performance qualities, and trade-offs between different performance metrics may be necessary. No definitive model stood out as the best algorithm for predicting student academic success in this study.</p>\n </section>\n </div>","PeriodicalId":50216,"journal":{"name":"Journal of Dental Education","volume":"88 12","pages":"1681-1695"},"PeriodicalIF":1.4000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study\",\"authors\":\"Widya Lestari DDS, MDSc, PhD, Adilah S. Abdullah BDS, Afifah M. A. Amin BDS, Nurfaridah BSc, Cortino Sukotjo DDS, PhD, MMSc, MHPE, Azlini Ismail B Biotech, PhD, Mohamad Shafiq Mohd Ibrahim BSc, MSc, PhD, Nashuha Insani BSc, Chandra P. Utomo BSc, MS\",\"doi\":\"10.1002/jdd.13673\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose/Objectives</h3>\\n \\n <p>Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clinical dental students based on admission results using artificial intelligence machine learning (ML) models, and Pearson correlation coefficient (PCC).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. Academic performance prediction in pre-clinical years was made using three input parameters: age during admission, pre-university Cumulative Grade Point Average (CGPA), and total matriculation semester. PCC was deployed to identify the correlation between pre-university CGPA and dental school grades. The proposed models’ classification accuracy ranged from 29% to 57%, ranked from highest to lowest as follows: RF, SVM, DT, and LR. Pre-university CGPA was shown to be predictive of dental students’ academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. In forecasting failure, LR predicted three grades with the highest recall, SVM predicted two grades, and DT predicted one. RF performance was insignificant.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>The findings demonstrated the application of ML algorithms and PCC to predict dental students' academic performance. However, it was limited by several factors. Each algorithm has unique performance qualities, and trade-offs between different performance metrics may be necessary. No definitive model stood out as the best algorithm for predicting student academic success in this study.</p>\\n </section>\\n </div>\",\"PeriodicalId\":50216,\"journal\":{\"name\":\"Journal of Dental Education\",\"volume\":\"88 12\",\"pages\":\"1681-1695\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Dental Education\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Dental Education","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study
Purpose/Objectives
Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clinical dental students based on admission results using artificial intelligence machine learning (ML) models, and Pearson correlation coefficient (PCC).
Methods
ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. Academic performance prediction in pre-clinical years was made using three input parameters: age during admission, pre-university Cumulative Grade Point Average (CGPA), and total matriculation semester. PCC was deployed to identify the correlation between pre-university CGPA and dental school grades. The proposed models’ classification accuracy ranged from 29% to 57%, ranked from highest to lowest as follows: RF, SVM, DT, and LR. Pre-university CGPA was shown to be predictive of dental students’ academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. In forecasting failure, LR predicted three grades with the highest recall, SVM predicted two grades, and DT predicted one. RF performance was insignificant.
Conclusion
The findings demonstrated the application of ML algorithms and PCC to predict dental students' academic performance. However, it was limited by several factors. Each algorithm has unique performance qualities, and trade-offs between different performance metrics may be necessary. No definitive model stood out as the best algorithm for predicting student academic success in this study.
期刊介绍:
The Journal of Dental Education (JDE) is a peer-reviewed monthly journal that publishes a wide variety of educational and scientific research in dental, allied dental and advanced dental education. Published continuously by the American Dental Education Association since 1936 and internationally recognized as the premier journal for academic dentistry, the JDE publishes articles on such topics as curriculum reform, education research methods, innovative educational and assessment methodologies, faculty development, community-based dental education, student recruitment and admissions, professional and educational ethics, dental education around the world and systematic reviews of educational interest. The JDE is one of the top scholarly journals publishing the most important work in oral health education today; it celebrated its 80th anniversary in 2016.