Learning Analytics-Based Sociology Learning Orchestration for Self-Regulated Learning, Engagement, And Concept Mastery Among Public Senior High School Students
DOI:
https://doi.org/10.61255/jupiter.v4i2.1084Keywords:
Learning Analytics, Sociology Learning, Self-Regulated Learning, Learning Engagement, Concept MasteryAbstract
Purpose: This study examined the effectiveness of learning analytics-based sociology learning orchestration in improving students’ self-regulated learning, engagement, and sociological concept mastery, addressing limited empirical integration of learning analytics, teacher orchestration, and sociology outcomes in secondary education. Methods: A quantitative multi-site quasi-experimental design with a pretest-posttest control group structure was implemented in three public senior high schools in Jakarta. The participants were 209 Grade XI students, comprising 105 students in the experimental group and 104 students in the control group. Data were collected using self-regulated learning and engagement questionnaires, a sociological concept mastery test, a treatment implementation observation sheet, and learning analytics logs. Data were analyzed using descriptive statistics, N-Gain, assumption testing, MANCOVA, and effect size interpretation. Findings: The experimental group showed greater improvement than the control group across all measured outcomes. The intervention produced moderate gains, while learning analytics logs indicated active participation in material access, formative quizzes, assignment submission, discussion, and feedback response. MANCOVA confirmed a significant multivariate intervention effect after controlling for pretest scores. Research Implications: Learning analytics can strengthen sociology learning when teachers use student data to provide timely feedback, identify learning difficulties, and design adaptive pedagogical interventions. Originality: This study contributes to data-informed pedagogy by positioning teachers as learning orchestrators who transform analytics data into adaptive instructional decisions in secondary sociology classrooms.
Abstract views: 16
,
PDF downloads: 9
Downloads
References
Aguilar, S. J., Karabenick, S. A., Teasley, S. D., & Baek, C. (2021). Associations between learning analytics dashboard exposure and motivation and self-regulated learning. Computers & Education, 162, Article 104085. https://doi.org/10.1016/j.compedu.2020.104085
Alhazbi, S., Al-Ali, A., Tabassum, A., Al-Ali, A., Al-Emadi, A., Khattab, T., & Hasan, M. A. (2024). Using learning analytics to measure self-regulated learning: A systematic review of empirical studies in higher education. Journal of Computer Assisted Learning, 40(4), 1658-1674. https://doi.org/10.1111/jcal.12982
Amarasinghe, I., Michos, K., Crespi, F., & Hernández-Leo, D. (2024). Learning analytics support to teachers' design and orchestrating tasks. Journal of Computer Assisted Learning, 40(6), 2416-2431. https://doi.org/10.1111/jcal.12711
Banihashem, S. K., Farrokhnia, M., Badali, M., & Noroozi, O. (2022). The impacts of constructivist learning design and learning analytics on students' engagement and self-regulation. Innovations in Education and Teaching International, 59(4), 442-452. https://doi.org/10.1080/14703297.2021.1890634
Blumenstein, M. (2020). Synergies of learning analytics and learning design: A systematic review of student outcomes. Journal of Learning Analytics, 7(3), 13-32. https://doi.org/10.18608/jla.2020.73.3
Broadbent, J., Sharman, S., Panadero, E., & Fuller-Tyszkiewicz, M. (2021). How does self-regulated learning influence formative assessment and summative grade? Comparing online and blended learners. The Internet and Higher Education, 50, Article 100805. https://doi.org/10.1016/j.iheduc.2021.100805
Du, J., Hew, K. F., & Liu, L. (2023). What can online traces tell us about students' self-regulated learning? A systematic review of online trace data analysis. Computers & Education, 201, Article 104828. https://doi.org/10.1016/j.compedu.2023.104828
Fan, Y., Matcha, W., Uzir, N. A., Wang, Q., & Gašević, D. (2021). Learning analytics to reveal links between learning design and self-regulated learning. International Journal of Artificial Intelligence in Education, 31(4), 980-1021. https://doi.org/10.1007/s40593-021-00249-z
Feng, S., Zhang, L., Wang, S., & Cai, Z. (2023). Effectiveness of the functions of classroom orchestration systems: A systematic review and meta-analysis. Computers & Education, 203, Article 104864. https://doi.org/10.1016/j.compedu.2023.104864
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2023). Supporting self-regulated learning with learning analytics interventions: A systematic literature review. Education and Information Technologies, 28(3), 3059-3088. https://doi.org/10.1007/s10639-022-11281-4
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53. https://doi.org/10.1016/j.compedu.2015.09.005
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27-52. https://doi.org/10.18608/jla.2019.62.3
Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners' self-regulated learning in massive open online courses. Computers & Education, 146, Article 103771. https://doi.org/10.1016/j.compedu.2019.103771
Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. The Internet and Higher Education, 47, Article 100758. https://doi.org/10.1016/j.iheduc.2020.100758
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18-33. https://doi.org/10.1016/j.compedu.2016.10.001
Mangaroska, K., & Giannakos, M. (2019). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516-534. https://doi.org/10.1109/TLT.2018.2868673
Matcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226-245. https://doi.org/10.1109/TLT.2019.2916802
Prieto, L. P., Rodríguez-Triana, M. J., Martínez-Maldonado, R., Dimitriadis, Y., & Gašević, D. (2019). Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level. Australasian Journal of Educational Technology, 35(4), 14-33. https://doi.org/10.14742/ajet.4314
Ramadhan, I., Thoharudin, M., Wiyono, H., Sabirin, S., & Suriyanisa, S. (2024). Enhancing students' learning interest and conceptual understanding in sociology: Using the analogy method and Canva infographic media. AL-ISHLAH: Jurnal Pendidikan, 16(4), 5731-5743. https://doi.org/10.35445/alishlah.v16i4.6385
Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18, Article 46. https://doi.org/10.1186/s41239-021-00284-9
Rodríguez-Triana, M. J., Prieto, L. P., Dimitriadis, Y., de Jong, T., & Gillet, D. (2021). ADA for IBL: Lessons learned in aligning learning design and analytics for inquiry-based learning orchestration. Journal of Learning Analytics, 8(2), 22-50. https://doi.org/10.18608/jla.2021.7357
Saint, J., Fan, Y., Gašević, D., & Pardo, A. (2022). Temporally-focused analytics of self-regulated learning: A systematic review of literature. Computers and Education: Artificial Intelligence, 3, Article 100060. https://doi.org/10.1016/j.caeai.2022.100060
Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19, Article 12. https://doi.org/10.1186/s41239-021-00313-7
Tzimas, D. E., & Demetriadis, S. N. (2024). Impact of learning analytics guidance on student self-regulated learning skills, performance, and satisfaction: A mixed methods study. Education Sciences, 14(1), Article 92. https://doi.org/10.3390/educsci14010092
Yang, C. C. Y., Wu, J.-Y., & Ogata, H. (2025). Learning analytics dashboard-based self-regulated learning approach for enhancing students' e-book-based blended learning. Education and Information Technologies, 30(1), 35-56. https://doi.org/10.1007/s10639-024-12913-7
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Suyuti, Devi Septiandini, Ike Arriany, Muhammad Ilman Naafi’a

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.






