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Exploring the Impact of Technology Integration on Student Engagement and Achievement in Science Education Suyono; Sayida Khoiratun Nisak; Riyanto; Muhammad Arsyad; Rusliana
International Journal of Educational Research Excellence (IJERE) Vol. 3 No. 2 (2024): July-December
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijere.v3i2.1048

Exploring the Impact of Technology Integration on Student Engagement and Achievement in Science Education

The use Problem Based Learning Methods in Science Education Muhammad Arsyad; Razia Khan; Chak Sothy
Journal Neosantara Hybrid Learning Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v2i3.2072

Science education plays a crucial role in developing students' critical thinking and problem-solving skills. However, traditional teaching methods often fail to engage students in meaningful learning experiences. To address this issue, Problem-Based Learning (PBL) has been introduced as an innovative instructional approach that encourages active learning and critical inquiry. This study aims to examine the effectiveness of the PBL method in science education by analyzing its impact on students' learning outcomes and engagement. This research employs a quasi-experimental design with two groups: an experimental group using the PBL method and a control group using conventional methods. Data were collected through pre-test and post-test assessments, student questionnaires, and classroom observations. The study involved secondary school students in a science subject. The findings reveal that students in the PBL group demonstrated higher academic achievement and improved problem-solving abilities compared to those in the control group. Additionally, PBL fosters greater student engagement, motivation, and collaboration during the learning process. In conclusion, the Problem-Based Learning method proves to be an effective strategy in science education, enhancing students' understanding and critical thinking skills. Educators are encouraged to implement PBL in science classrooms to create a more dynamic and interactive learning environment.

Generative AI for Learning: A Bibliometric Mapping of Global Scientific Publications Loso Judijanto; Muhammad Arsyad; Istiarsyah Istiarsyah
West Science Interdisciplinary Studies Vol. 4 No. 07 (2026): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v4i07.3018

The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed educational practices by introducing new possibilities for personalized learning, intelligent tutoring systems, automated feedback, and AI-supported knowledge creation. This study aims to explore the global research landscape of Generative AI for Learning through a bibliometric analysis of scientific publications indexed in the Scopus database. A comprehensive literature search was conducted to identify relevant publications, followed by performance analysis and science mapping using VOSviewer. The analysis examined publication trends, highly cited literature, keyword co-occurrence, citation networks, author collaboration, institutional contributions, and international research patterns. The findings reveal that research on generative AI in learning has experienced substantial growth, particularly following the emergence of ChatGPT and large language models. The intellectual structure of the field is dominated by three interconnected themes: technological advancement of artificial intelligence, educational integration of AI-based learning systems, and human-centered considerations including AI literacy, critical thinking, ethics, and responsible adoption. Influential publications highlight both the opportunities and challenges of generative AI, including improvements in learning effectiveness, academic transformation, assessment challenges, and potential cognitive impacts. Furthermore, collaboration analysis indicates that the United States plays a central role in global research networks, while contributions from countries across Asia, Europe, and other regions continue to expand. This study provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.

Generative AI for Learning: A Bibliometric Mapping of Global Scientific Publications Loso Judijanto; Muhammad Arsyad; Istiarsyah Istiarsyah
West Science Interdisciplinary Studies Vol. 4 No. 07 (2026): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v4i07.3018

The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed educational practices by introducing new possibilities for personalized learning, intelligent tutoring systems, automated feedback, and AI-supported knowledge creation. This study aims to explore the global research landscape of Generative AI for Learning through a bibliometric analysis of scientific publications indexed in the Scopus database. A comprehensive literature search was conducted to identify relevant publications, followed by performance analysis and science mapping using VOSviewer. The analysis examined publication trends, highly cited literature, keyword co-occurrence, citation networks, author collaboration, institutional contributions, and international research patterns. The findings reveal that research on generative AI in learning has experienced substantial growth, particularly following the emergence of ChatGPT and large language models. The intellectual structure of the field is dominated by three interconnected themes: technological advancement of artificial intelligence, educational integration of AI-based learning systems, and human-centered considerations including AI literacy, critical thinking, ethics, and responsible adoption. Influential publications highlight both the opportunities and challenges of generative AI, including improvements in learning effectiveness, academic transformation, assessment challenges, and potential cognitive impacts. Furthermore, collaboration analysis indicates that the United States plays a central role in global research networks, while contributions from countries across Asia, Europe, and other regions continue to expand. This study provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.

Partnerships and the Economic Transformation of Cocoa Farmers in Konawe, Southeast Sulawesi Hannin Pradita Nur Soulthoni; Eka Suaib; Muhammad Arsyad; Muammar Akbar AQ; Dian Puspita Rizki; Novytha Sary
Komunitas Vol. 18 No. 1 (2026): March 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/komunitas.v18i1.37794

Agricultural development programs for cocoa in Indonesia often fail to create sustainable economic transformation despite intensive interventions. Previous studies tend to evaluate program effectiveness in aggregate without exploring why identical programs produce divergent outcomes at the farmer level. This study aims to analyze the dynamics of strategic partnerships among the private sector, farmer groups, and local governments in creating economic transformation for cocoa farmers, and to identify the inclusion and exclusion mechanisms that mediate program benefits. This qualitative case study was conducted in Mataiwoi Village, Konawe Regency, Southeast Sulawesi, involving 32 informants through in-depth interviews, participant observation, and focus group discussions during 45 days of fieldwork (August–October 2025). Thematic analysis reveals an effective asymmetric tripartite partnership model, in which role differentiation based on comparative advantage, rather than power balance, is key to success. Participating farmers experienced multidimensional transformation: productivity tripled from 400 kg to 1,000–1,200 kg/ha/year, income quadrupled from Rp8–12 million to Rp35–45 million/year, and their mindset shifted from fatalism to economic agency. However, only 63 of 240 farmers accessed the program because pre-existing inequalities in assets and social capital functioned as mediating structures. This study concludes that the asymmetric tripartite partnership model transforms farmers who can access the program, but inclusivity remains a crucial challenge requiring affirmative mechanisms. These findings imply that future studies and agricultural partnership programs should move beyond aggregate success indicators and examine how inequalities in capital, networks, and institutional access shape inclusive rural economic transformation.

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