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Predicting Student Academic Success Using Machine Learning Models: A Learning Analytics Approach in Higher Education Arief Hidayat; Swasti Maharani; Dendi Pratama; Ramadiani Ramadiani; S Sujito; Addy Septyawan; Dian Wardiana Sjuchro
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4881

Abstract

Rapid deployment of digital learning technologies in the higher education sector has created immense amounts of educational data that could be leveraged to enhance student success and institutional effectiveness. Nevertheless, student dropout, poor academic performance, and lack of retention continue to plague universities across the world. In most cases, identification of academically struggling students is often late since existing models are largely reactive. Therefore, there is need for development of advanced learning analytics models that are able to forecast student performance in higher education institutions. The current study seeks to create an artificial neural network (ANN)-based learning analytics framework to predict student success in higher education institutions. A predictive analytical approach based on quantitatively evaluating a sample of 1,000 undergraduate students was used in the current study. Various attributes used to evaluate the students included demographic information, academic performance, LMS activity, and learning behaviors. Learning analytics indicators used in the model included previous GPA, attendance rate, assignment completion rate, quiz scores, logins per week, learning hours per week, discussion engagement, engagement index, interaction scores, and learning consistency. In the analysis, the model was validated and tested against accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, and cross validation tests. Results showed that accuracy, precision, recall, F1-Score, and ROC-AUC of the ANN model were 92.8%, 91.4%, 93.7%, 92.5%, and 0.96, respectively. Based on these outcomes, previous GPA, attendance rate, assignment completion rate, and various engagement indicators were found to be the strongest predictors of student success in college. On the theoretical front, contributions of this study include AI-assisted student performance and behavior prediction. Practically, a sophisticated warning system was developed in this study to assist in effective academic advisement and planning for student retention and academic improvement strategies.
Co-Authors A., Amelia Achmad Fawaid Achmad Nizar Hidayanto Addy Septyawan Agus, Fahrul Aini, Nur Amelia A. Anita Anita Apriyanti Nurliyah Arief Hidayat Ariffin, Zainal AW., Sawung Awang Harsa Kridalaksana Azainil Azainil Bayu Ramadhani Damayanti, Elok Dendi Pratama Desy Sitti Kahdijah Dharma Widada Dian Wardiana Sjuchro Dyna Marisa Khairina Dyna Marissa Khairina Eka Maya S.S. Ciptaningsih Eko Junirianto Eko Wiji Setio Budianto Elfrida Simanjuntak Fahrul Agus Fahrul Agus Fahrul Agus Fazari, Alawiyah Nur Gore, Krisantus Gore Guellica Agnesia Claudia Thanos Halimatusya'diyah Hamdani Hamdani Hariyanti, Titik Harman Harman Hatta, Heliza Rahmania Heliza Rahmania Hatta, Heliza Rahmania Idrus, S. Saleh Al Ilma, Okta Usrifatin Imanda, Galih Dapa Indah Fitri Astuti, Indah Fitri Ira, Taqdiraa Jundillah, Muhammad Labib Kenya Permata Kusumadewi kusnandar kusnandar Kusnandar Kusnandar Labib Jundillah Labib Jundillah, Muhammad Luthfi Fahrozi, Muhammad Masayu Widiastuti Mohd. Hasan Selamat Mohd. Hasan Selamat Muhamad Azhari Muhamad Azhari Muhammad Fadli Muhammad Labib Jundillah Nadia Christin Borneo S Nina Queena Hadi Noraini Che Pa Noraini Che Pa Nur Aini Nur Aini Nurbasar Nurbasar Prastyo, Teguh Priantono, Ahmad Agung Puspitasari, Ni Luh Gede Dian Putut Pamilih Widagdo Putut Pamilih Widagdo, Putut Pamilih Rahmah, Auliana Rahmah, Auliana Reza Andrea Rodziah binti Atan Rodziah binti Atan Rusli Abdullah S Sujito S., Wardana Sawung AW. Septya Maharani, Septya Setyadi, Hario Jati Siti Lailiyah Soraya, Nova Intan Sri Rahayu Sudarsono, Bambang Surya Adithama Swasti Maharani Toong Hai Sam Tunggal, Anggunan Vina Zahrotun Kamila Wardana S. Winci Firdaus Yuli Wulandari, Yuli Yulianto Yulianto Yunia Hardiani Yusak Hudiyono, Yusak Zainal Ariffin Zainal Arifin