Differentiated Instruction through Adaptive Learning Platform in Science Education: A Systematic Literature Review
Abstract
Abstract: Recent technological advancements have led to various educational innovations, including adaptive Learning, which customizes content and instructional methods to meet the diverse needs and abilities of individual students. Several empirical studies have utilized adaptive learning platforms to support differentiated Learning in science. However, to date, there has yet to be a comprehensive review of the findings in this area. This study aims to explore research trends related to differentiated Instruction through adaptive learning platforms in science education, as documented in Scopus-indexed journal articles published between 2019 and 2024. The research follows PRISMA guidelines, employing the Publish or Perish application for the search system, with data sourced from SCOPUS. The search yielded 368 articles, and screening based on specific inclusion and exclusion criteria resulted in 23 papers that were subsequently analyzed. This study highlights various adaptive technology methods used in science education, emphasizing Learning Management Systems (LMS) and Artificial Intelligence (AI). LMS emerges as the most frequently utilized, followed by AI and assessment platforms. Crucial factors for successful implementation include real-time feedback and accessibility to technology. Although these platforms improve learning outcomes, issues regarding student engagement and satisfaction persist. Educational institutions should assess their technological infrastructure and provide training for educators to leverage new features effectively. Additionally, developers should focus on enhancing personalization options, while further research is necessary to address students' emotional needs better and enhance their motivation.
Keywords: adaptive learning, differentiated instruction, science education, teaching, systematic literature review.
DOI: http://dx.doi.org/10.23960/jpmipa/v25i2.pp914-931
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Aeiad, E., & Meziane, F. (2019). An adaptable and personalised E-learning system applied to computer science Programmes design. Education and Information Technologies, 24(2), 1485-1509.
Aleksandrovich, S. I., Ramazan, T., Utegaliyeva, R., Sarimbayeva, B., Keubassova, G., Bissalyyeva, R., ... & Abdikarimova, G. (2024). Transformative applications in biology education: A case study on the efficacy of adaptive learning with numerical insights. Caspian Journal of Environmental Sciences, 22(2), 395-408.
Al-Rayes, S., Al Yaqoub, F. A., Alfayez, A., Alsalman, D., Alanezi, F., Alyousef, S., ... & Alanzi, T. M. (2022). Gaming elements, applications, and challenges of gamification in healthcare. Informatics in Medicine Unlocked, 31, 100974.
Amado, C. M., & Roleda, L. S. (2020, January). Game element preferences and engagement of different hexad player types in a gamified physics course. In Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning (pp. 261-267).
Cardenas, L. S. H., Castano, L., Guzman, C. C., & Alvarez, J. P. N. (2022). Personalised learning model for academic leveling and improvement in higher education. Australasian Journal of Educational Technology, 38(2), 70-82.
Cinque, A., Miller, J., Legron-Rodriguez, T., Paradiso, J. R., & Lapeyrouse, N. (2024, June). Student perceptions of adaptive learning modules for general chemistry. In International Conference on Human-Computer Interaction (pp. 105-115). Cham: Springer Nature Switzerland.
Cota-Rivera, E. I., Correa, M. E. G., Marín, L. A. B., Montenegro, M. Y. M., Herrera, A. M., & Martinez, M. A. A. M. (2024). Transforming education with the power of artificial intelligence: case studies. In Enhancing Higher Education and Research With OpenAI Models (pp. 113-140). IGI Global.
Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023). Enhancing academic outcomes through an adaptive learning framework utilizing a novel machine learning-based performance prediction method. Data and Metadata, 2, 164-164.
González, J. D., Escobar, J. H., Sánchez, H., De la Hoz, J., Beltrán, J. R., Arciniegas, S. M., & Martínez, L. S. (2019, June). Impact of the use of virtual laboratories of electromagnetism in the development of competences in engineering students. In Journal of Physics: Conference Series (Vol. 1247, No. 1, p. 012018). IOP Publishing.
Heeg, D. M., & Avraamidou, L. (2023). The use of Artificial intelligence in school science: a systematic literature review. Educational Media International, 60(2), 125-150.
Hwang, G. J., & Fu, Q. K. (2020). Advancement and research trends of smart learning environments in the mobile era. International Journal of Mobile Learning and Organisation, 14(1), 114-129.
Istiyono, E., Dwandaru, W. S. B., Setiawan, R., & Megawati, I. (2020). Developing of computerized adaptive testing to measure physics higher order thinking skills of senior high school students and its feasibility of use. European Journal of Educational Research, 9(1), 91-101.
Jose, B. C., Mussanah, A. L., Kumar, O. D. M. A., & Nagalakshmi, M. Assessing the effectiveness of adaptive learning systems in K-12 Education.
Katz, S., Albacete, P., Chounta, I. A., Jordan, P., McLaren, B. M., & Zapata-Rivera, D. (2021). Linking dialogue with student modelling to create an adaptive tutoring system for conceptual physics. International journal of artificial intelligence in education, 31(3), 397-445.
Kinner, T., & Whitaker, E. T. (2022, June). A framework for the design and development of adaptive agent-based simulations to explore student thinking and performance in K-20 science. In International Conference on Human-Computer Interaction (pp. 190-206). Cham: Springer International Publishing.
Koć-Januchta, M. M., Schönborn, K. J., Roehrig, C., Chaudhri, V. K., Tibell, L. A., & Heller, H. C. (2022). “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook. International Journal of Educational Technology in Higher Education, 19(1), 11.
Lahiassi, J., Elwarraki, O., Aammou, S., & Jdidou, Y. (2024). Pedagogical innovations in personalized learning. In Fostering Pedagogical Innovation Through Effective Instructional Design (pp. 329-341). IGI Global.
Levin, S. M., & Isakova, A. I. (2024). Adaptive education as a key element for enhancing the effectiveness of learning processes. Digital Transformation, 30(2).
Li, Z., Lou, X., Chen, M., Li, S., Lv, C., Song, S., & Li, L. (2023). Students’ online learning adaptability and their continuous usage intention across different disciplines. Humanities and Social Sciences Communications, 10(1), 1-10.
Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903-1929.
Marzuki, M., Zakaria, N., & Masruri, M. (2024). Investigating the capabilities of adaptive learning as a cutting-edge approach to model development in an educational setting. Jurnal Kajian Pendidikan Dan Psikologi, 1(3 April), 195-206.
Min, W., Mott, B., Park, K., Taylor, S., Akram, B., Wiebe, E., ... & Lester, J. (2020, August). Promoting computer science learning with block-based programming and narrative-centered gameplay. In 2020 IEEE Conference on Games (CoG) (pp. 654-657). IEEE.
Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050.
Morze, N., & Buinytska, O. (2019). Digital competencies of university teachers. Universities in the Networked Society: Cultural Diversity and Digital Competences in Learning Communities, 19-37.
Mudrák, M., Turcáni, M., & Reichel, J. (2020). Impact of using personalized e-course in computer science education. Journal on Efficiency and Responsibility in Education and Science, 13(4), 174-188.
Müller, U., Huelmann, T., Haustermann, M., Hamann, F., Bender, E., & Sitzmann, D. (2022). First results of computerized adaptive testing for an online physics test. In Towards a new future in engineering education, new scenarios that european alliances of tech universities open up (pp. 1377-1387). Universitat Politècnica de Catalunya.
Nor, M., & Halim, L. (2021, November). Analysis of physics learning media needs based on mobile augmented reality (AR) on global warming for high school students. In Journal of Physics: Conference Series (Vol. 2126, No. 1, p. 012009). IOP Publishing.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Ramadhani, D. G., Yamtinah, S., Saputro, S., Sarwanto, S., & Masykuri, M. (2024). From screen to bench: unpacking the shifts in chemistry learning experiences during the COVID-19 transition. Chemistry Teacher International, 6(1), 19-33.
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic reviews, 10, 1-19.
Rezapour, M. M., Fatemi, A., & Nematbakhsh, M. A. (2024). A methodology for using players’ chat content for dynamic difficulty adjustment in metaverse multiplayer games. Applied Soft Computing, 156, 111497.
Rijal, A., Aswarliansyah, A., & Waluyo, B. (2025). Effectiveness of differentiated learning in mathematics: insights from elementary school students. Journal of Education and Learning (EduLearn), 19(1), 241-248.
Rincon-Flores, E. G., Castano, L., Guerrero Solis, S. L., Olmos Lopez, O., Rodríguez Hernández, C. F., Castillo Lara, L. A., & Aldape Valdés, L. P. (2024). Improving the learning-teaching process through adaptive learning strategy. Smart Learning Environments, 11(1), 27.
Rizki, S. N., & Ningsih, E. P. (2024). Penerapan pembelajaran berdiferensiasi dalam memenuhi gaya belajar siswa peserta didik di sekolah dasar. Ludi Litterarri, 1(1), 38-48.
Rıos, L., Lutz, B., Rossman, E., Yee, C., Trageser, D., Nevrly, M., ... & Self, B. (2020, October). Creating coupled-multiple response test items in physics and engineering for use in adaptive formative assessments. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.
Sahal Fawaiz, S. K. H., & Wisodo, H. (2024). The Development of Adaptive E-Scaffolding to Support the Academic Diversity of High School Students on Mechanical Waves Material.
Santos, S. M. A. V., dos Santos Rodrigues, B., Graciotto, C. D. M., de Almeida, C. S., Soeiro, J. T. P., Amorim, L. A. S., ... & das Neves Meroto, M. B. (2024). Personalizing education: the role of adaptive technologies in individualized education. Contribuciones a Las Ciencias Sociales, 17(2), e5190-e5190.
Soraya, S. (2022, November). Implementation of augmented reality (AR) using Assembler in high school applied physics education with the ADDIE model approach. In Journal of Physics: Conference Series (Vol. 2377, No. 1, p. 012072). IOP Publishing.
Tomlinson, C. A. (2017). How to differentiate instruction in academically diverse classrooms. Ascd.
Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education, 144, 103698.
Vincent-Ruz, P., & Boase, N. R. (2022). Activating discipline specific thinking with adaptive learning: A digital tool to enhance learning in chemistry. Plos one, 17(11), e0276086.
Vyas, V. S., Kemp, B., & Reid, S. A. (2021). Zeroing in on the best early-course metrics to identify at-risk students in general chemistry: an adaptive learning pre-assessment vs. traditional diagnostic exam. International Journal of Science Education, 43(4), 552-569.
Wu, J. Y., & Tsai, C. C. (2022). Harnessing the power of promising technologies to transform science education: prospects and challenges to promote adaptive epistemic beliefs in science learning. International Journal of Science Education, 44(2), 346-353.
Zourmpakis, A. I., Kalogiannakis, M., & Papadakis, S. (2023). Adaptive gamification in science education: An analysis of the impact of implementation and adapted game elements on students’ motivation. Computers, 12(7), 143.
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