Development of a Web-Based Prediction System for Students’ Academic Performance

Dabiah Alboaneen, Modhe Almelihi, Rawan Alsubaie, Raneem Alghamdi, Lama Alshehri and Renad Alharthi
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Dabiah Alboaneen: Computer Science Department, College of Sciences and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia
Modhe Almelihi: Computer Science Department, College of Sciences and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia
Rawan Alsubaie: Computer Science Department, College of Sciences and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia
Raneem Alghamdi: Computer Science Department, College of Sciences and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia
Lama Alshehri: Computer Science Department, College of Sciences and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia
Renad Alharthi: Computer Science Department, College of Sciences and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia

Data, 2022, vol. 7, issue 2, 1-19

Abstract: Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. The ML model is developed to predict the total score of a course at the early stages. Several ML algorithms are applied, namely: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). This model applies to the data of female students of the Computer Science Department at Imam Abdulrahman bin Faisal University (IAU). The dataset contains 842 instances for 168 students. Moreover, the results showed that the prediction’s Mean Absolute Percentage Error (MAPE) reached 6.34%, and the academic factors had a higher impact on students’ academic performance than the demographic factors, the midterm exam score in the top. The developed web-based prediction system is available on an online server and can be used by tutors.

Keywords: academic performance; machine learning; students’ performance (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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