Pembelajaran Adaptif Berbasis Teknologi: Upaya Meningkatkan Keterlibatan dan Hasil Belajar Siswa
Keywords:
adaptive learning, educational technology, student engagement, learning outcomes, personalized learningAbstract
This study aims to examine the effectiveness of technology-based adaptive learning in enhancing students’ engagement and learning outcomes. A quantitative approach was employed using a quasi-experimental design with a non-equivalent control group. The participants consisted of 60 fifth-grade elementary students divided into an experimental group and a control group. The experimental group was exposed to adaptive learning through a Learning Management System (LMS) equipped with personalization features, while the control group received conventional instruction. Data were collected through achievement tests, student engagement questionnaires, and classroom observations, and were analyzed using independent and paired sample t-tests, N-Gain, and effect size. The findings reveal that technology-based adaptive learning significantly improves students’ learning outcomes compared to conventional methods (p < 0.05), with a large effect size (Cohen’s d = 1.12). In addition, student engagement in the experimental group was consistently higher across cognitive, emotional, and behavioral dimensions. These results indicate that personalized learning supported by technology creates a more effective and meaningful learning experience. In conclusion, technology-based adaptive learning represents a promising and innovative approach to improving educational quality in the digital era. The study highlights the importance of integrating adaptive technologies into instructional practices and educational policies to foster student-centered learning environments.
References
Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2020). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 17(1), 1–24.
Bruner, J. S. (1966). Toward a Theory of Instruction. Harvard University Press.
Creswell, J. W. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept. Review of Educational Research, 74(1), 59–109.
Fullan, M., Quinn, J., & McEachen, J. (2018). Deep Learning: Engage the World Change the World. Corwin.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Pearson.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign.
Kementerian Pendidikan dan Kebudayaan. (2021). Transformasi Digital Pendidikan di Indonesia. Jakarta: Kemdikbud.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. London: Pearson.
OECD. (2018). The Future of Education and Skills: Education 2030. Paris: OECD Publishing.
OECD. (2021). Digital Education Outlook 2021. Paris: OECD Publishing.
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2017). Informing Progress: Insights on Personalized Learning Implementation and Effects. RAND Corporation.
Piaget, J. (1970). Science of Education and the Psychology of the Child. New York: Viking.
Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37–76.
Tomlinson, C. A. (2017). How to Differentiate Instruction in Academically Diverse Classrooms. Alexandria: ASCD.
Vygotsky, L. S. (1978). Mind in Society. Cambridge: Harvard University Press.
Walkington, C., & Bernacki, M. (2020). Personalization of instruction. Educational Psychologist, 55(3), 1–18.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27.
Zimmerman, B. J. (2002). Becoming a self-regulated learner. Theory Into Practice, 41(2), 64–70.
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