Staff View: Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study

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 us...

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Main Authors:	Lestari, Widya, Abdullah, Adilah Syahirah, Ahmad Amin, Afifah Munirah, faridah, Nur, Sukotjo, Cortino, Ismail, Azlini, mohamad ibrahim, mohamad shafiq, Insani, Nashuha, Utomo, Chandra Prasetyo
Format:	Article
Language:	English English English
Published:	American Dental Education Association 2024
Subjects:	RK Dentistry
Online Access:	http://irep.iium.edu.my/113806/7/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance.pdf http://irep.iium.edu.my/113806/8/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance_Scopus.pdf http://irep.iium.edu.my/113806/9/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance_WoS.pdf http://irep.iium.edu.my/113806/ https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673 https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673
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spelling	my.iium.irep.1138062024-12-16T07:13:26Z http://irep.iium.edu.my/113806/ Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study Lestari, Widya Abdullah, Adilah Syahirah Ahmad Amin, Afifah Munirah faridah, Nur Sukotjo, Cortino Ismail, Azlini mohamad ibrahim, mohamad shafiq Insani, Nashuha Utomo, Chandra Prasetyo RK Dentistry 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. American Dental Education Association 2024-07-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/113806/7/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance.pdf application/pdf en http://irep.iium.edu.my/113806/8/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance_Scopus.pdf application/pdf en http://irep.iium.edu.my/113806/9/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance_WoS.pdf Lestari, Widya and Abdullah, Adilah Syahirah and Ahmad Amin, Afifah Munirah and faridah, Nur and Sukotjo, Cortino and Ismail, Azlini and mohamad ibrahim, mohamad shafiq and Insani, Nashuha and Utomo, Chandra Prasetyo (2024) Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study. Journal of Dental Education, Volume 88, Issue 12 (12). pp. 1-15. E-ISSN 1930-7837 https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673 https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English English English
topic	RK Dentistry
spellingShingle	RK Dentistry Lestari, Widya Abdullah, Adilah Syahirah Ahmad Amin, Afifah Munirah faridah, Nur Sukotjo, Cortino Ismail, Azlini mohamad ibrahim, mohamad shafiq Insani, Nashuha Utomo, Chandra Prasetyo Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
description	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.
format	Article
author	Lestari, Widya Abdullah, Adilah Syahirah Ahmad Amin, Afifah Munirah faridah, Nur Sukotjo, Cortino Ismail, Azlini mohamad ibrahim, mohamad shafiq Insani, Nashuha Utomo, Chandra Prasetyo
author_facet	Lestari, Widya Abdullah, Adilah Syahirah Ahmad Amin, Afifah Munirah faridah, Nur Sukotjo, Cortino Ismail, Azlini mohamad ibrahim, mohamad shafiq Insani, Nashuha Utomo, Chandra Prasetyo
author_sort	Lestari, Widya
title	Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
title_short	Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
title_full	Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
title_fullStr	Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
title_full_unstemmed	Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
title_sort	artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
publisher	American Dental Education Association
publishDate	2024
url	http://irep.iium.edu.my/113806/7/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance.pdf http://irep.iium.edu.my/113806/8/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance_Scopus.pdf http://irep.iium.edu.my/113806/9/113806_Artificial%20intelligence%20to%20predict%20pre-clinical%20dental%20student%20academic%20performance_WoS.pdf http://irep.iium.edu.my/113806/ https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673 https://onlinelibrary.wiley.com/doi/10.1002/jdd.13673
_version_	1818833705733455872
score	13.223943

Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study

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