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All Journal Jurnal Ilmu Komputer Techno.Com: Jurnal Teknologi Informasi Jurnal JSIKA Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Majalah Ekonomi Jurnal Komunika : Jurnal Komunikasi, Media dan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Pengabdian UNDIKMA Infotech: Jurnal Informatika & Teknologi Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat IJORER : International Journal of Recent Educational Research Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Visi Pengabdian Kepada Masyarakat Jurnal Abdimas Bina Bangsa Journal of Technology and Informatics (JoTI) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Abdi Masyarakat Indonesia Empiricism Journal Jurnal Sistem Informasi Universitas Dinamika KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi EKOBIS ABDIMAS : Jurnal Pengabdian Masyarakat Jurnal Ragam Pengabdian
Julianto Lemantara
Universitas Dinamika
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Exploring Online Evaluation Development : A Bibliometric Approach M.J. Dewiyani Sunarto; Bambang Hariadi; Edo Yonatan Koentjoro; Pradita Maulidya Effendi; Julianto Lemantara; Erwin Soetomo
Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Vol 9, No 3 (2023): September
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v9i3.8775

Abstract

This study aims to explore the growth of online evaluation to give a comprehensive overview and identify research in the trending field and recommendations for further research that can be developed. This study used a bibliometric approach with descriptive methods from documents in the last ten years of the Scopus database. Quantitative and qualitative analyses were performed using data collected from numerous scientific reference sources to determine development trends, the most popular subjects, and the contributions of eminent scholars. The analysis’s findings indicated that student evaluations of courses and teachers were the most commonly discussed subjects. However, in subsequent years, interest had changed to e-learning, higher education, assessment, and evaluation. Recent scientific articles further emphasized the significant impact Covid-19 had had on altering the evaluation process, placing particular attention on online learning. With the advancement of research on artificial intelligence in education, this study also outlined prospects and challenges in online evaluation research. We found thirteen publications covering Chat GPT for educational purposes by obtaining data from articles published in the most recent year in the Scopus database. This study offered a comprehensive view of the evolution of online evaluation through a bibliometric method, covered pertinent topics, and provided a basis for academics to carry out the most recent research in this area.
The Learning of Mathematics Based on Individual Differences : A Topic Trend Bibliometric Analysis Using VOSviewer M.J. Dewiyani Sunarto; Julianto Lemantara
Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Vol 10, No 1 (2024): March
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i1.10238

Abstract

This study aims to establish a foundation for the development of differentiated learning through an analysis of trends in articles related to personality type differences in the field of mathematics education. The research method used was descriptive, using Bibliometric Analysis. These 422 data from Scopus have been from Scopus-indexed journals and were obtained by defining search keywords, namely "individual differences." These articles were then filtered based on the highest citation, resulting in 100 articles that were analyzed using Vos Viewer and Microsoft Excel to display the necessary data for analysis. The results of this study indicated that from many articles that had been analyzed, individual differences have a wide meaning, it could be gender, characteristics, thinking process, fear, attitude, and others. Those differences should be taken into consideration by the teacher and cannot be neglected because they can influence the learners. From the data that has been analyzed, the topic above has been increasing after 2017 with the United States and the United Kingdom as the most recent country that published this topic. Cognition is the journal that has the most publications on this topic, with Engle, R. W. as the most productive writer. The most trending article was about Mathematics in general, mathematics concepts, and the phenomena in teaching and learning math. The next piece of advice is about the development of the individual learning model, its evaluation, and its utilization in creating a differentiated learning model.
Developing A Differentiated Learning Model Based on Artificial Intelligence : Implementation in The Mathematics Classroom M. J. Dewiyani Sunarto; Bambang Hariadi; Julianto Lemantara
Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Vol 10, No 2 (2024): June
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i2.11135

Abstract

This study aims to produce a model that can meet the needs of Differentiated Learning based on AI. This method used research and development (R&D) with the Generic Desain Research Model (GDRM). The subject of this study was mathematics education experts and mathematics learning model experts. Instruments used in this study include quality validity assessment and feasibility assessment analysis. The data analysis techniques used were quantitative methods with learning model validity assessment criteria that use A Linkert Scale of 1-4. The test results of the AI-based Mathematics learning model show that (1) the Content Validity test resulted in an average of 3.47 which means the model is very valid, and (2) The average feasibility test is 3.43, which means it is also very valid. The results showed that the AI-based Mathematics learning model can be implemented in differentiated mathematics classes and train students to learn independently, think critically, provide solutions to problems, and have the courage to convey solutions.
The Learning of Mathematics Based on Individual Differences : A Topic Trend Bibliometric Analysis Using VOSviewer Sunarto, M.J. Dewiyani; Lemantara, Julianto
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 10 No. 1 (2024): March
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i1.10238

Abstract

This study aims to establish a foundation for the development of differentiated learning through an analysis of trends in articles related to personality type differences in the field of mathematics education. The research method used was descriptive, using Bibliometric Analysis. These 422 data from Scopus have been from Scopus-indexed journals and were obtained by defining search keywords, namely "individual differences." These articles were then filtered based on the highest citation, resulting in 100 articles that were analyzed using Vos Viewer and Microsoft Excel to display the necessary data for analysis. The results of this study indicated that from many articles that had been analyzed, individual differences have a wide meaning, it could be gender, characteristics, thinking process, fear, attitude, and others. Those differences should be taken into consideration by the teacher and cannot be neglected because they can influence the learners. From the data that has been analyzed, the topic above has been increasing after 2017 with the United States and the United Kingdom as the most recent country that published this topic. Cognition is the journal that has the most publications on this topic, with Engle, R. W. as the most productive writer. The most trending article was about Mathematics in general, mathematics concepts, and the phenomena in teaching and learning math. The next piece of advice is about the development of the individual learning model, its evaluation, and its utilization in creating a differentiated learning model.
Developing A Differentiated Learning Model Based on Artificial Intelligence : Implementation in The Mathematics Classroom Sunarto, M. J. Dewiyani; Hariadi, Bambang; Lemantara, Julianto
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 10 No. 2 (2024): June
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i2.11135

Abstract

This study aims to produce a model that can meet the needs of Differentiated Learning based on AI. This method used research and development (R&D) with the Generic Desain Research Model (GDRM). The subject of this study was mathematics education experts and mathematics learning model experts. Instruments used in this study include quality validity assessment and feasibility assessment analysis. The data analysis techniques used were quantitative methods with learning model validity assessment criteria that use A Linkert Scale of 1-4. The test results of the AI-based Mathematics learning model show that (1) the Content Validity test resulted in an average of 3.47 which means the model is very valid, and (2) The average feasibility test is 3.43, which means it is also very valid. The results showed that the AI-based Mathematics learning model can be implemented in differentiated mathematics classes and train students to learn independently, think critically, provide solutions to problems, and have the courage to convey solutions.
Co-Authors A. B. Tjandrarini A.B. 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