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All Journal Jurnal Matematika & Sains Acta Aquatica: Aquatic Sciences Journal Biosaintifika: Journal of Biology & Biology Education Jurnal Jejaring Matematika dan Sains Jurnal Geuthee Makara Journal of Technology Buletin Pengabdian Heca Journal of Applied Sciences Journal of Educational Management and Learning Bulletin of Chemical Reaction Engineering & Catalysis JURNAL REKAYASA KIMIA & LINGKUNGAN
Zulkarnain Jalil
Department of Aquaculture, Faculty of Marine and Fisheries, Syiah Kuala University Banda Aceh, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Journal of Educational Management and Learning

 1 
Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Maulana, Aga; Irvanizam, Irvanizam; Jalil, Zulkarnain; Lensoni, Lensoni; Lala, Andi; Abas, Abdul Hawil; Tallei, Trina Ekawati; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.58

Abstract

Artificial intelligence (AI) has emerged as a powerful technology that has the potential to transform education. This study aims to comprehensively understand students' perspectives on using AI within educational settings to gain insights about the role of AI in education and investigate their perceptions regarding the advantages, challenges, and expectations associated with integrating AI into the learning process. We analyzed the student responses from a survey that targeted students from diverse academic backgrounds and educational levels. The results show that, in general, students have a positive perception of AI and believe AI is beneficial for education. However, they are still concerned about some of the drawbacks of using AI. Therefore, it is necessary to take steps to minimize the negative impact while continuing to take advantage of the advantages of AI in education.
Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach Noviandy, Teuku Rizky; Zahriah, Zahriah; Yandri, Erkata; Jalil, Zulkarnain; Yusuf, Muhammad; Mohamed Yusof, Nur Intan Saidaah; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i1.191

Abstract

Education is important for societal advancement and individual empowerment, providing opportunities, developing essential skills, and breaking cycles of poverty. Nonetheless, the path to educational success is marred by challenges such as achieving academic excellence and preventing student dropouts. Early identification of students at risk of dropping out or those likely to excel academically can significantly enhance educational outcomes through tailored interventions. Traditional methods often fall short in precision and foresight for effective early detection. While previous studies have utilized machine learning to predict student performance, the potential for more sophisticated ensemble methods, such as stacked classifiers, remains largely untapped in educational contexts. This study develops a stacked classifier integrating the predictive strengths of LightGBM, Random Forest, and logistic regression. The model achieved an accuracy of 80.23%, with precision, recall, and F1-score of 79.09%, 80.23%, and 79.20%, respectively, surpassing the performance of the individual models tested. These results underscore the stacked classifier's enhanced predictive capability and transformative potential in educational settings. By accurately identifying students at risk and those likely to achieve academic excellence early, educational institutions can better allocate resources and design targeted interventions. This approach optimizes educational outcomes and supports informed policymaking, fostering environments conducive to student success.
Co-Authors Abas, Abdul Hawil Abdullah A. Muhammadar Adi Rahwanto Aidil, Jul Aiza, Nur Al-Qossam, Izzuddin Alfani, Fatha Yufari Almerian, Ivan Amri, Asbahrul Azizah Azizah Bakruddin, Bakruddin Barizki, Gebrina Zikra Chan, Syafruddin Cut Yulvizar Fardin Afrido, Fardin Farisi, Wilma Fauzi Fauzi Feriyanto, Reki Ghazi Mauer Idroes Gheitsa, Tjut Nyak Ghalda Harahap, Putri Dyana Harahap, Riqqa Rahmania Sallim Irhamni Irhamni Irvanizam, Irvanizam Ismail Ismail Isvalana, Nabila Juliana, Eka Lala, Andi M. Nizar Machmud Malahayati Malahayati Maulana, Aga Mifthahul Jannah Mirnawati, Windy Mochammad Afifuddin Mohamed Yusof, Nur Intan Saidaah Muhammad Nizar Machmud Muhammad Yusuf Muhammad Zaki Muhammad, M Munthe, Aris Darmika Mursal Mursal Nur Fadli Nurul Azmi, Nurul Pajriana, Elisa Putra, Dedi Fazriansyah Putra, Dedi Fazrianyah Rachmatillah, Faukas Rahayu, Silvana Rinaldi Idroes Syahrul Purnawan Tanzil Murda, Tanzil Teuku Rizky Noviandy TRINA EKAWATI TALLEI Wahab, Gunawan Abdul Yandri, Erkata Zahara, Silvia Zahriah, Zahriah ZAINAL ABIDIN MUCHLISIN Zuhra, Annisa Zulfalina, Zulfalina