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Lala, Andi
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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.
Does Online Education Make Students Happy? Insights from Exploratory Data Analysis Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Emran, Talha Bin; Zahriah, Zahriah; Rahimah, Souvia; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.124

Abstract

This study investigates the impact of online education on student happiness. Utilizing a dataset of 5715 students sourced from Bangladesh, we employed an exploratory data analysis to analyze the quantitative data. The key finding is that there is a prevalent trend of dissatisfaction with online education among Bangladeshi students, regardless of demographic factors like age, gender, education level, preferred device for access, or type of academic institution. The dissatisfaction trend highlights the need of continuous improvements and targeted interventions are essential to ensure online education not only enables academic success, but also supports the overall wellbeing and happiness of students in the context of a developing country.
Academic Supervision by School Principals: Enhancing Junior High School Teachers' Professional Competence Julianda, Eka; Ismail, Ismail; Khairuddin, Khairuddin; Lala, Andi
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.v2i1.170

Abstract

This study investigates the role of academic supervision by school principals in enhancing teacher professional competence at SMP Negeri 1 Babahrot and SMP Negeri 2 Susoh in Southwest Aceh, Indonesia. Employing a qualitative approach, the research examined the planning, implementation, and follow-up of academic supervision activities conducted by the school principals. The findings indicate that the principals' systematic approach to academic supervision includes comprehensive planning, active implementation, and targeted follow-up actions, all aimed at supporting the continuous professional development of teachers. The study demonstrates that these practices significantly improve teaching quality and student learning outcomes. Challenges such as limited time and the need for experienced supervisors and strategies to address these issues were identified. Overall, the research underscores the vital role of effective academic supervision in fostering teacher professionalism and enhancing educational quality, suggesting that these practices could be adopted in other educational settings to achieve similar benefits.
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 Abdiansyah, Zeldi Abdul Fatakh, Abdul Abdullah Abdullah Abdullah Abdullah Akbar, Zulhiddin Aprianto . Arrahman Arrahman Azhar, Fauzul Dharma, Dian Budi Diah, Muhammad Emran, Talha Bin Erkata Yandri Fajri.S, Muhammad Farida Nugrahani Fatimah, Cut Frisca Madhona, Yenny Ghazi Mauer Idroes Gunawan Gunawan Hanan, Dafif Handayani, Sri Harianti, Evi Harsya, Rabith Madah Khulaili Hewindati, Yuni Tri Idroes, Ghalieb Mutig Idroes, Ghifari Maulana Irsan Hardi Irvanizam, Irvanizam Ismail Ismail Juansen, Monsya Julianda, Eka Junaedi Junaedi Junia, Wiyana Khairan Khairan Khairuddin - Khairullah, Khairullah Khalijah Awang Khudori, Abdullah Kosim Kosim Kusumo, Fitranto M. Yogi Riyantama Isjoni Manalu, Paris Marlina Marlina Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Mayureza, Guntur Ma’ruf, Rahmat Arif Mifthahul Jannah Misbullah, Alim Mohamed Yusof, Nur Intan Saidaah Muhammad Gunawan Muhammad Yusuf Mursal Mursal Muslem Muslem, Muslem Musvira, Intan Nizamuddin Nizamuddin Nurul Azmi, Nurul Piski Razief Perucha Fauzie Afidh Rinaldi Idroes Rivansyah Suhendra Rohyana, Hadi Salsabila, Athaya Samud Samud, Samud Saputra, Tri Dian Souvia Rahimah Sugianto Sugianto Suhendra , Rivansyah Suhendra, Rivansyah Surya Ade Saputera Taufiq Karma Teuku Rizky Noviandy TRINA EKAWATI TALLEI Ulhaq, Muhammad Zia Zahriah, Zahriah Zuchra Helwani, Zuchra Zulkarnain Jalil