Artificial Intelligence (AI) to predict dental student academic performance based on pre university results
The dental school admission process involves establishing criteria for evaluating applicants, weighing the various admission criteria, and then comparing applicants based on selected criteria and weighting. In the Kulliyyah of Dentistry, International Islamic University Malaysia (IIUM), admission is...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/98672/19/98672_Artificial%20Intelligence%20%28AI%29%20to%20predict%20dental%20student.pdf http://irep.iium.edu.my/98672/ |
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| Summary: | The dental school admission process involves establishing criteria for evaluating applicants, weighing the various admission criteria, and then comparing applicants based on selected criteria and weighting. In the Kulliyyah of Dentistry, International Islamic University Malaysia (IIUM), admission is mainly based on matriculation cumulative grade point assessment (CGPA) results. Successful graduation from the Bachelor of Dental Surgery programme is assessed through four Professional Exams. This study aims to predict the academic performance of dental students based on their admission results using Artificial Intelligence. Machine Learning Algorithm is applied using academic result samples of graduates of the Kulliyyah of Dentistry, IIUM from 2016-2021. The dataset input variables will include student’s gender, age during admission, scholarship, parents’ level of education, pre-university result, Professional Exams result, and final CGPA. Dataset output variables include the number of repeat papers, repeat years, distinctions, and graduation on time. Exploratory Data Analysis will be performed with training and testing data. For modeling, several prediction models will be trained using neural networks. For evaluation, accuracy and prediction error will be calculated. Data will be analyzed statistically for each variable, visualized into graphical format, and the correlation coefficients computed. The expected result is accuracy in prediction of academic performance of students from Kulliyyah of Dentistry IIUM students based on admission results.
Keywords: dental admission, students’ performance, artificial intelligence |
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