The Relationship Between Teachers’ Machine Learning Competence and Improvements in Islamic Education Achievement Among Students at Al Uswah Islamic Junior High School
DOI:
https://doi.org/10.59890/ijgsr.v4i6.258Keywords:
Teacher Competence, Machine Learning, Learning Achievment, Islamic Religious Education, Integrated Islamic SchoolAbstract
Digital transformation encourages the integration of machine learning (ML) as a strategic instrument for improving Islamic Religious Education (PAI) learning quality. This study aims to analyze the relationship between teachers' ML competence and PAI learning achievement of students at SMP IT Al Uswah. A mixed methods approach with sequential explanatory design was employed. Respondents consisted of 8 PAI teachers and 124 students from grades VII–IX. Quantitative data were collected through a 40-item Likert scale questionnaire, report card grades, and digital formative assessment scores; analyzed using SEM-PLS (SmartPLS 4.0). Qualitative data were obtained through in-depth interviews with 6 informants, classroom observation over 12 sessions, and documentation study. Results indicate that teachers' ML competence is at a moderate level (mean = 3.41; SD = 0.54). The data literacy dimension obtained the highest score (mean = 3.78), while predictive pedagogical intervention was the lowest (mean = 3.02). Students' average PAI learning achievement was 81.6 (SD = 6.4). SEM-PLS analysis shows a positive and significant relationship between teachers' ML competence and PAI learning achievement (β = 0.612; t = 7.84; p < 0.001; R² = 0.462). AI-based pedagogical mechanisms identified include the use of Quizizz Analytics, Google Classroom insights, and the Edmodo platform. These findings confirm the need for structured ML training programs for PAI teachers in integrated Islamic schools
References
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Ca Alam, B. P., Arifin, Z., Asmoro, E. T., Sutrisno, D., Syuhardi, Y. I., Sonny, M., Trista, R. T., & Nasution, S. (2024). Enhancing elementary school teachers’ competencies with Artificial Intelligence (AI) technology for future education. Tridharmadimas: Jayakarta Journal of Community Service, 4.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist. https://doi.org/10.1037/0003-066X.55.1.34
Haerani, S., Parmitasari, R. D. A., Aponno, E. H., & Aunalal, Z. I. (2019). Moderating effects of age on personality, driving behavior towards driving outcomes. International Journal of Human Rights in Healthcare. https://doi.org/10.1108/IJHRH-08-2017-0040
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE.
Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. Globethics Publications.
Holmes, W., Porayska-Pomsta, K., & Rajendran, G. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526.
Juliyanti, N., & Angraini, R. S. (2026). Mathematics education students’ readiness in content, pedagogy, and technology mastery to become AI-based educators. Journal of Mathematics and Science Education, 14(1), 294–306.
Kim, H., & Reeves, T. (2023). Teachers’ perceptions and readiness for AI-enhanced education. Computers & Education: Artificial Intelligence, 5(1), 100128.
Kumar, A., & Parveen, N. (2022). Teacher readiness for technology integration: Conceptual model and implications. International Journal of Educational Technology in Higher Education, 19(24), 1–15.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16.
Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. B. F. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. B. F. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Lusardi, A., Mitchell, O. S., & Curto, V. (2010). Financial literacy among the young: Evidence and implications. National Bureau of Economic Research, 358–380. Retrieved from https://www.nber.org/papers/w15352.pdf
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy-makers. UNESCO.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.
Murniyetti, M., Rahman, R., & Muliati, I. (2023). Teachers’ responses to the use of artificial intelligence in Islamic Religious Education and Character Education (a case study in Padang City). HAWARI: Journal of Islamic Religious Education and Studies, 4(2).
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041.
Nurmiati. (2021). The pedagogical competence of teachers of Al-Qur'an Hadith studies in the implementation of the 2013 curriculum. Idaarah: Journal of Educational Management, 5(1), 128.
Puslapdik Kemendikdasmen. (2026). AI as a learning support tool in the digital age. Ministry of Primary and Secondary Education.
Rahayuningsih, Y. S., & Muhtar, T. (2022). Digital pedagogy as an effort to improve 21st-century teacher competencies. Jurnal Basicedu, 6(4), 6960–6966.
Redecker, C., & Punie, Y. (2017). Digital competence of educators: DigCompEdu. European Commission.
Sabri, M. F., & MacDonald, M. (2010). Savings Behavior and Financial Problems among College Students: The Role of Financial Literacy in Malaysia | Sabri | Cross-cultural Communication. Crosscultural Communication. https://doi.org/10.3968/j.ccc.1923670020100603.009
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.
Susilowati, E., Kristian, N., & Pawerti, R. C. (2025). Teachers’ readiness and pedagogical adaptation to learning integrated with artificial intelligence in the Merdeka Curriculum. Research and Development Journal of Education, 11(2).
Yuspita, Y. E., Okra, R., Musril, H. A., Derta, S., Jasmienti, J., Alamsyah, Y., Tanjung, R. N., & Putra, M. A. (2023). Utilization of AI applications to improve the competencies of KKG PAI teachers in Baso District. Dedikasia Journal: Journal of Community Service, 3(2), 136.prara, G. V., & Zimbardo, P. G. (2004). Personalizing politics: A congruency model of political preference. American Psychologist. https://doi.org/10.1037/0003-066X.59.7.581
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