Systematic review of adaptive learning research in physics education in Indonesia

Muhammad Minan Chusni(1*)

(1) Scopus ID 57205023959 UIN Sunan Gunung Djati Bandung
(*) Corresponding Author


This study aims to map publication topics and research interests based on the author's keywords in an analysis based on co-occurrence analysis from the Scopus database on adaptive learning research in physics education. This study uses a systematic review method with the main data sources, namely articles from scientific journals and proceedings indexed by Scopus from 2013 to 2022. Keyword restrictions are focused on adaptive learning with physics topics in Indonesia. The results of the study show that there are five main clusters related to adaptive learning, namely machine learning, deep learning, algorithms, calculations, and students. Based on the results of the novelty analysis, areas that are becoming research trends in the realm of educational research are independent learning, instructional design, and curriculum to optimize adaptive learning. The results of this study can be used as a reference for further research that focuses on developing and optimizing the potential of adaptive learning in Indonesia.


Adaptive learning sistematic review Physics education Indonesia


Almeida, F., & Simoes, J. (2019). The role of serious games, gamification and industry 4.0 tools in the education 4.0 paradigm. Contemporary Educational Technology, 10(2), 120–136.

Angeli, F., & Montefusco, A. (2020). Sensemaking and learning during the Covid-19 pandemic: A complex adaptive systems perspective on policy decision-making. World Development, 136, 105106.

Barcelona, K. (2014). 21<SUP>st</SUP> Century Curriculum Change Initiative: A Focus on STEM Education as an Integrated Approach to Teaching and Learning. American Journal of Educational Research, 2(10), 862–875.

Bohmrah, M. K., & Kaur, H. (2021). Classification of Covid-19 patients using efficient fine-tuned deep learning DenseNet model. Global Transitions Proceedings, 2(2), 476–483.

Bylkova, S., & Shalkov, D. (2020). TV and Internet interviews in the structure of media education: transformation of the ontological paradigm. E3S Web of Conferences, 210, 18010.

Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. R. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. International Review of Research in Open and Distributed Learning, 21(1), 173–197.

Clark, R. M., & Kaw, A. (2020). Adaptive learning in a numerical methods course for engineers: Evaluation in blended and flipped classrooms. Computer Applications in Engineering Education, 28(1), 62–79.

Contreras, G. S., González, A. H., Fernández, I. S., Cepa, C. B. M., & Escobar, J. C. Z. (2022). The Challenge of Technology in the Classroom, An Uncertain but Necessary Paradigm in a Digital Reality.

Daim, T. U., Chan, L., & Estep, J. (2018). Infrastructure and Technology Management. In Innovation, Technology, and Knowledge Management. Springer.

Darmaji, D., Kurniawan, D., Astalini, A., Lumbantoruan, A., & Samosir, S. (2019). Mobile learning in higher education for the industrial revolution 4.0: Perception and response of physics practicum.

Farooq, J., & Bazaz, M. A. (2020). A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies. Chaos, Solitons & Fractals, 138, 110148.

Forbes, C. T., & Davis, E. A. (2010). Curriculum design for inquiry: Preservice elementary teachers’ mobilization and adaptation of science curriculum materials. Journal of Research in Science Teaching, 47(7), 820–839.

Gaur, U., Majumder, M. A. A., Sa, B., Sarkar, S., Williams, A., & Singh, K. (2020). Challenges and opportunities of preclinical medical education: COVID-19 crisis and beyond. SN Comprehensive Clinical Medicine, 2(11), 1992–1997.

Goh, P. S.-C., & Abdul-Wahab, N. (2020). Paradigms to drive higher education 4.0. International Journal of Learning, Teaching and Educational Research, 19(1), 159–171.

Guo, S. (2020). Synchronous versus asynchronous online teaching of physics during the COVID-19 pandemic. Physics Education, 55(6), 65007.

Harrell, S., & Bynum, Y. (2018). Factors affecting technology integration in the classroom. Alabama Journal of Educational Leadership, 5, 12–18.

Kamil, M., Sunarya, P. A., Muhtadi, Y., Adianita, I. R., & Anggraeni, M. (2021). BlockCert Higher Education with Public Key Infrastructure in Indonesia. 2021 9th International Conference on Cyber and IT Service Management (CITSM), 1–6.

Kanwar, A., Balasubramanian, K., & Carr, A. (2019). Changing the TVET paradigm: new models for lifelong learning. International Journal of Training Research, 17(sup1), 54–68.

Krasnova, L., & Shurygin, V. (2019). Blended learning of physics in the context of the professional development of teachers. International Journal of Emerging Technologies in Learning (IJET), 14(23), 17–32.

Kuswanto, H. (2018). Android-Assisted Mobile Physics Learning through Indonesian Batik Culture: Improving Students’ Creative Thinking and Problem Solving. International Journal of Instruction, 11(4), 287–302.

Leask, B., & Bridge, C. (2013). Compare: A Journal of Comparative and International Education Comparing internationalisation of the curriculum in action across disciplines: theoretical and practical perspectives. A Journal of Comparative and International Education, 43(1), 79–101.

Liu, J.-H., Ruan, L.-X., & Zhou, Y.-Y. (2018). Application of Big Data on Self-adaptive Learning System for Foreign Language Writing. International Symposium on Computational Science and Computing, 86–93.

Marzano, R. J. (1988). Dimensions of Thinking: A Framework for Curriculum and Instruction. The Association for Supervision and Curriculum Development.

Maulidah, S. S., & Prima, E. C. (2018). Using Physics Education Technology as Virtual Laboratory in Learning Waves and Sounds. Journal of Science Learning, 1(3), 116–121.

Mead, C., Buxner, S., Bruce, G., Taylor, W., Semken, S., & Anbar, A. D. (2019). Immersive, interactive virtual field trips promote science learning. Journal of Geoscience Education, 67(2), 131–142.

Meeter, M. (2021). Primary school mathematics during the COVID-19 pandemic: No evidence of learning gaps in adaptive practicing results. Trends in Neuroscience and Education, 25, 100163.

Millar, R. (2008). Taking scientific literacy seriously as a curriculum aim. Asia-Pacific Forum on Science Learning and Teaching, 9(2), 1–18.

Miranda, J., Navarrete, C., Noguez, J., Molina-Espinosa, J.-M., Ramírez-Montoya, M.-S., Navarro-Tuch, S. A., Bustamante-Bello, M.-R., Rosas-Fernández, J.-B., & Molina, A. (2021). The core components of education 4.0 in higher education: Three case studies in engineering education. Computers & Electrical Engineering, 93, 107278.

Mou, C., Tian, Y., Zhang, F., & Zhu, C. (2021). Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education. Frontiers in Psychology, 12.

Nimavat, N., Singh, S., Fichadiya, N., Sharma, P., Patel, N., Kumar, M., Chauhan, G., & Pandit, N. (2021). Online medical education in India–different challenges and probable solutions in the age of COVID-19. Advances in Medical Education and Practice, 12, 237.

Pols, F. (2020). A Physics Lab Course in Times of COVID-19. Electronic Journal for Research in Science & Mathematics Education, 24(2), 172–178.

Romlah, O. Y., Bodho, S., Latief, S., & Akbar, H. (2021). Empowering the Quality of School Resources in Improving the Quality of Education. Bulletin of Science Education, 1(1), 27–44.

Safar, N. Z. M., Kamaludin, H., Ahmad, M., Jofri, M. H., Wahid, N., & Gusman, T. (2022). Intervention Strategies through Interactive Gamification E-Learning Web-Based Application to Increase Computing Course Achievement. JOIV: International Journal on Informatics Visualization, 6(2), 376–381.

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.

Vesin, B., Mangaroska, K., & Giannakos, M. (2018). Learning in smart environments: user-centered design and analytics of an adaptive learning system. Smart Learning Environments, 5(1), 1–21.

Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2020). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 1–11.

Westbury, M. (2020). Infrastructure and technology-enhanced learning: Context, agency, multiplicity. Studies in Technology Enhanced Learning, 1(1), 47–64.

Williamson, B. (2019). Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 50(6), 2794–2809.

Xie, Z., Wang, X., Zhang, H., Sato, I., & Sugiyama, M. (2022). Adaptive inertia: Disentangling the effects of adaptive learning rate and momentum. International Conference on Machine Learning, 24430–24459.

Yakin, M., & Linden, K. (2021). Adaptive e‐learning platforms can improve student performance and engagement in dental education. Journal of Dental Education, 85(7), 1309–1315.

Zulfiani, Z., Suwarna, I. P., & Miranto, S. (2018). Science education adaptive learning system as a computer-based science learning with learning style variations. Journal of Baltic Science Education, 17(4), 711.

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Published by: Chemistry Education, Muhammadiyah Semarang University