Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Evaluation and Research in Education (IJERE)
Kamsin, Amirrudin
Unknown Affiliation
Computer Science & IT Education

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Bibliometric analysis of mobile learning user experience industrial revolution 5.0 Ariffin, Shamsul Arrieya; Kamsin, Amirrudin; Mustapha, Ramlan
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i5.28958

Abstract

User experience or usability is under research, particularly in mobile learning in the era of industrial revolution (IR) 5.0. This article discusses incorporating sophisticated mobile technologies such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) into the user experience in educational settings. Therefore, this paper investigates the relatively new revolutionary potential of mobile learning user experience in the context of the IR 5.0, where the digital and technology spheres meet for better user experiences, particularly for students in learning. The research explores novel meta-mobile technology approaches by examining concrete cases from 2012, analyzing their impact, and improving the user experience. Likewise, this article elucidates the need for mobile learning user experience research based on bibliometric analysis.
Personalized learning model based on machine learning algorithms Jin, Zhang; Kamsin, Amirrudin
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp470-475

Abstract

Machine learning algorithms have been widely applied in the field of personalized learning within educational information technology. By leveraging big data analysis and data mining techniques, machine learning can help identify patterns and trends in students' learning behaviors, preferences, and performance. This information can then be used to tailor educational resources and experiences to meet the individual needs and unique characteristics of each learner. Machine learning has made great progress and achievements in the teaching process of universities, but there are also some shortcomings. Such as data dependence, over-fitting and under-fitting, explanatory problems, need a lot of computing resources, data bias, sensitive to outliers, cannot solve all problems, and the challenge of data privacy, through the analysis of machine learning algorithm model, efforts to find ways to expand the dimension of personalized learning classroom, meet the students in learning objectives, learning content, learning methods of the special characteristics and unique needs, to guide students to actively explore and research, obtain innovation and appropriate learning results.
Generative AI in teacher education: a systematic review Yuan, Longfa; Razak, Rafiza Abdul; Kamsin, Amirrudin; Abdul-Rahman, Siti-Soraya
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i2.37225

Abstract

This study addresses a critical gap in the literature by conducting one of the earliest systematic reviews (2021-2025) on generative artificial intelligence (GenAI) in teacher education. Using a structured screening and coding process, 35 peer-reviewed articles from Scopus and Web of Science (WoS) were analyzed to examine methodological trends, geographical disparities, and cross-cultural adaptability. The review identifies four major application areas, including stakeholder perception analysis, instructional resource generation, curriculum design, and student-AI collaborative learning, and synthesizes their underlying pedagogical mechanisms. Key findings reveal pronounced geographical imbalance (with no studies from Africa or Latin America), heavy reliance on short-term qualitative designs, and limited empirical or longitudinal validation. Based on these insights, the study proposes a conceptual framework linking GenAI applications, challenges, and future research pathways. This work contributes a structured evidence base and offers guidance for advancing GenAI-integrated teacher education through more rigorous, inclusive, and context-sensitive research.
Co-Authors Abdul-Rahman, Siti-Soraya Jin, Zhang Mustapha, Ramlan Razak, Rafiza Abdul Shamsul Arrieya Ariffin Yuan, Longfa