The Role of Real-Time Learning Analytics and Pedagogical Chatbots in Improving Digital Literacy and Self-Regulated Learning among Islamic Senior High School Students

Suardi Suardi

doi:10.55927/ajae.v4i4.15369

Authors

Suardi Suardi MAN 2 Pasaman Barat

DOI:

https://doi.org/10.55927/ajae.v4i4.15369

Keywords:

Real-Time Learning Analytics, Pedagogical Chatbots, Digital Literacy, Self-Regulated Learning, Secondary Education

Abstract

This study examines the impact of integrating real-time learning analytics and pedagogical chatbots on enhancing digital literacy and self-regulated learning among Madrasah Aliyah students. Using a mixed-methods approach with a pre-test–post-test quasi-experimental design involving 120 students, quantitative data were collected through digital literacy tests, self-regulated learning scales, and activity logs, while qualitative data came from semi-structured interviews. Results showed a significant improvement in both digital literacy and self-regulated learning in the experimental group compared to the control group, with a positive correlation between chatbot interaction intensity and learning outcomes. Qualitative findings revealed that real-time feedback and adaptive chatbot guidance enhanced students’ intrinsic motivation and learning management skills. Overall, the integration of these technologies effectively strengthens 21st-century competencies and offers strategic implications for technology-based learning design in Islamic senior high schools.

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References

Bai, B., Wang, X., & Lee, A. (2023). Artificial intelligence in education: Applications and implications. Educational Technology Research and Development, 71(2), 345–362.

Bond, M., Zawacki-Richter, O., & Nichols, M. (2024). Systematic review of learning analytics in higher education: The state of the field. Computers & Education, 200, 104760.

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. Sage.

Cao, Y., Liu, J., & Chen, L. (2023). Pedagogical chatbots as multi-role learning agents: A design framework. Computers in Human Behavior, 137, 107420.

Chang, C. C., & Sasa, G. (2022). Conversational agents for supporting self-regulated learning in digital environments. Interactive Learning Environments, 30(4), 742–757.

Chang, C. C., Sasa, G., & Wong, B. (2022). The effects of AI chatbots on students’ engagement and learning performance. Journal of Educational Computing Research, 60(5), 1123–1145.

Fryer, L., Nakao, K., & Thompson, A. (2022). Chatbots for language learning: A study of learner perceptions. Language Learning & Technology, 26(3), 45–63.

International Computer and Information Literacy Study. (2024). Preparing for life in a digital world: The IEA International Computer and Information Literacy Study. IEA.

Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Research and Practice in Technology Enhanced Learning, 15(4), 329–343.

Lai, C. (2024). The role of AI in supporting self-regulated learning: Opportunities and challenges. British Journal of Educational Technology, 55(1), 12–29.

Luo, L., & Zhan, Z. (2022). The role of learning analytics dashboards in self-regulated learning: A systematic review. Educational Technology & Society, 25(2), 143–158.

Malterud, K., Siersma, V. D., & Guassora, A. D. (2021). Information power in qualitative studies and its implications for sample size. Qualitative Health Research, 31(2), 189–196.

Markauskaite, L., & Goodyear, P. (2023). Epistemic fluency and learning analytics: New directions for research and practice. Learning, Media and Technology, 48(2), 133–150.

Moura, A., & Carvalho, A. (2023). Artificial intelligence chatbots in education: Adaptive learning and teaching support. Computers and Education: Artificial Intelligence, 4, 100122.

Palinkas, L. A., Horwitz, S. M., & Green, C. A. (2023). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health, 50(2), 318–330.

Rahayu, S., & Sari, D. (2021). Digital literacy in Indonesian schools: Challenges and opportunities. Journal of Educational Research and Practice, 11(3), 455–470.

Rehman, A. (2024). Autonomy, competence, and relatedness in digital learning environments: The role of chatbots. Computers & Education, 205, 104876.

Taber, K. S. (2021). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 51(3), 853–879.

Vuorikari, R., Punie, Y., Carretero, S., & Van den Brande, L. (2022). The Digital Competence Framework for Citizens (DigComp 2.2): Updates and policy implications. Publications Office of the European Union.

Wong, B. T. M., Li, K. C., & Lam, R. (2022). The impacts of AI-based learning analytics dashboards on student learning outcomes. Computers & Education, 182, 104463.

Yang, S., Wang, Y., & Zhang, M. (2023). Enhancing reflective thinking through AI chatbots in online learning. Educational Technology Research and Development, 71(3), 789–807.

Yin, H., Lee, J. C. K., & Wang, W. (2023). Motivational effects of AI chatbots on student learning: Evidence from secondary schools. British Journal of Educational Psychology, 93(2), 567–584.

Zimmerman, B. J., & Moylan, A. R. (2022). Self-regulated learning: Theories, measures, and outcomes. Routledge.