Development of microlearning media science concepts in senior high school: Emphasis on technology literacy
Science learning in secondary schools often struggles to explain abstract concepts such as biological mechanisms and cellular processes, which are difficult for students to visualise using conventional teaching methods. This condition contributes to low conceptual understanding and limited technological literacy among students. To address this issue, this study developed science microlearning media designed in short, interactive, and focused learning units. This study aimed to identify the characteristics of science learning in secondary schools, determine the validity of the developed microlearning media, and examine their effectiveness in improving students’ conceptual understanding and technological literacy. The study employed a research-and-development method that included needs analysis, product development, expert validation, and feasibility testing. The validation results showed that the developed media achieved a very high validity score, with a content score of 4.52. Feasibility assessments also demonstrated high acceptance with average scores of 4.47 and 4.51, respectively. These findings indicate that the developed microlearning media are feasible and have strong potential to support science learning, particularly in facilitating students’ understanding of abstract concepts. Future studies are recommended to conduct broader experimental investigations and integrate advanced technologies to enhance the scalability and effectiveness of microlearning media.
Akram, H., Aslam, S., Saleem, A., & Parveen, K. (2021). The Challenges of Online Teaching in COVID-19 Pandemic: A Case Study of Public Universities in Karachi, Pakistan. Journal of Information Technology Education: Research, 20, 263–282. https://www.informingscience.org/Publications/4784
Ayres, P., & Paas, F. (2012). Cognitive Load Theory: New Directions and Challenges. Applied Cognitive Psychology, 26(6), 827–832. https://doi.org/10.1002/acp.2882
Backfisch, I., Scherer, R., Siddiq, F., Lachner, A., & Scheiter, K. (2021). Teachers’ technology use for teaching: Comparing two explanatory mechanisms. Teaching and Teacher Education, 104, 103390. https://doi.org/10.1016/j.tate.2021.103390
Betancur Chicué, V., & García-Valcárcel, A. (2023). Microlearning for the Development of Teachers’ Digital Competence Related to Feedback and Decision Making. Education Sciences, 13. https://doi.org/10.3390/educsci13070722
Cheng, S.-L., & Xie, K. (2018). The relations among teacher value beliefs, personal characteristics, and TPACK in intervention and non-intervention settings. Teaching and Teacher Education, 74, 98–113. https://doi.org/10.1016/j.tate.2018.04.014
Chi, M. T. H., & Wylie, R. (2014). The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823
Farahiba, A. (2022). Pengembangan Instrumen Tes Literasi Peserta Didik Pada Materi Teks Anekdot. Jurnal Dimensi Pendidikan Dan Pembelajaran, 10, 146–154. https://doi.org/10.24269/dpp.v10i2.4554
Garshasbi, S., Yecies, B., & Shen, J. (2021). Microlearning and computer-supported collaborative learning: An agenda towards a comprehensive online learning system. STEM Education, 1, 225. https://doi.org/10.3934/steme.2021016
Ghomi, M., & Redecker, C. (2019). Digital Competence of Educators (DigCompEdu): Development and Evaluation of a Self-assessment Instrument for Teachers’ Digital Competence (p. 548). https://doi.org/10.5220/0007679005410548
Gog, T., Paas, F., & Sweller, J. (2010). Cognitive Load Theory: Advances in Research on Worked Examples, Animations, and Cognitive Load Measurement. Educational Psychology Review, 22, 375–378. https://doi.org/10.1007/s10648-010-9145-4
Guo, P., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos (p. 50). https://doi.org/10.1145/2556325.2566239
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004
Jainuri, M., Kamid, K., Syaiful, S., & Huda, N. (2025). Microlearning Effectiveness in Higher Education: A Systematic Review and Meta-Analysis of Student Retention and Learning Outcomes. MATHEMA: JURNAL PENDIDIKAN MATEMATIKA, 7, 630–642. https://doi.org/10.33365/jm.v7i2.517
Kemdikbud. (2021). Bantuan Fasilitas Bidang Pendidikan dan Keudayaan. Kementerian Pendidikan dan Kebudayaan, 1(1), 1–91.
Kikas, E., Puusepp, I., & Aus, K. (2024). Expectancy-value-cost motivational profiles in biology and physics: Their relations with gender, self-reported satisfaction of needs, and learning behavior. Learning and Individual Differences, 115, 102520. https://doi.org/10.1016/j.lindif.2024.102520
Kuciapski, M. (2016). Students Acceptance of m-Learning for Higher Education—UTAUT Model Validation (Vol. 264, p. 166). https://doi.org/10.1007/978-3-319-46642-2_11
Law, N., Woo, D., & Wong, G. (2018). A Global Framework of Reference on Digital Literacy Skills for Indicator 4.4.2.
Marlina, R., & Hamdani, H. (2023). Trend Topic in School-based Lesson Study for Learning Community in Transformational Program. Jurnal Penelitian Pendidikan IPA, 9(7), 4956–4962. https://doi.org/10.29303/jppipa.v9i7.2864
Marlina, R., Suwono, H., Ibrohim, I., Yuenyong, C., Hamdani, H., & Pamungkas, R. (2025). CRTP: Learning model for integrating STEM competencies in pre-service biology teachers. Journal of Education and Learning (EduLearn), 19(3), 1466–1473. http://edulearn.intelektual.org/index.php/EduLearn/article/view/21818
Marlina, R., Suwono, H., Ibrohim, I., Yuenyong, C., Husamah, H., & Hamdani, H. (2024). Theoretical frameworks of self-efficacy in collaborative science learning practices: A systematic literature review. JPBI (Jurnal Pendidikan Biologi Indonesia), 10(2), Article 2. https://doi.org/10.22219/jpbi.v10i2.33628
Marlina, R., Suwono, H., Yuenyong, C., Ibrohim, I., & Hamdani, H. (2024). Teacher role and domain of expertise in the 21st century: Evidence from preservice biology teacher. Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education), 12(2), 279–293. https://jurnal.usk.ac.id/JPSI/article/view/35985
Marlina, R., Suwono, H., Yuenyong, C., Ibrohim, I., Mahanal, S., Saefi̇, M., & Hamdani̇, H. (2023). Technological Pedagogical Content Knowledge (TPACK) for Preservice Biology Teachers: Two Insights More Promising. Participatory Educational Research, 10(6), 245–265. https://doi.org/10.17275/per.23.99.10.6
Mat, H., Mustakim, S., & Razali, F. (2024). The Integration of Digital Learning to Enhance Higher Order Thinking Skills (HOTS) among Elementary Students in Science Education.
Mayer, R., & Fiorella, L. (2022). Introduction to Multimedia Learning (pp. 3–16). https://doi.org/10.1017/9781108894333.003
Ministry of National Education, Bayar, M. F., Mınıstry of Natıonal Educatıon, & Ağgül, Ö. (2023). Needs Analysis of Teachers Providing Science Education to Visually Impaired Students and Their Students. Educational Policy Analysis and Strategic Research, 18(4), 142–164. https://doi.org/10.29329/epasr.2023.631.7
Mostrady, A., Sanchez-Lopez, E., & Gonzalez-Sanchez, A. (2024). Microlearning and its Effectiveness in Modern Education: A Mini Review. Acta Pedagogia Asiana, 4, 33–42. https://doi.org/10.53623/apga.v4i1.496
Rathi, T., Ronald, D. B., & Shelke, D. A. (2022). Utility of Observation As A Tool of Data Collection in Empirical Research. Journal of Positive School Psychology, 7700–7704. https://journalppw.com/index.php/jpsp/article/view/8841
Saputri, D., Mellisa, Hidayati, N., & Fauziah, N. (2023). Lembar Validasi: Instrumen yang Digunakan Untuk Menilai Produk yang Dikembangkan Pada Penelitian Pengembangan Bidang Pendidikan. Biology and Education Journal, 3(2), 133–151. https://doi.org/10.25299/baej.2023.15347
Senadheera, V., Muthukumarana, C., Ediriweera, D., & Rupasinghe, T. (2024). Impact of microlearning on academic performance of students in higher education: A systematic review and meta-analysis. Journal of Multidisciplinary & Translational Research, 9, 10–25. https://doi.org/10.4038/jmtr.v9i1.2
Sinha, R. & Rinki. (2025). CONCEPT DEVELOPMENT TEST IN SCIENCE: A RESEARCH TOOL. mLAC Journal for Arts, Commerce and Sciences (m-JACS) ISSN: 2584-1920, 3, 1–9. https://doi.org/10.59415/mjacs.v3i4.287
Son, M., & Ha, M. (2024). Development of a digital literacy measurement tool for middle and high school students in the context of scientific practice. Education and Information Technologies, 30, 4583–4606. https://doi.org/10.1007/s10639-024-12999-z
Sweller, J. (2008). Cognitive load theory and the use of educational technology. Educational Technology, 48, 32–35. https://doi.org/10.1007/s11423-019-09701-3
Vildósola, M., Salcedo-Lagos, P., Kotz, G., & Sanhueza-Campos, C. (2025). Contributions of AI Tools to Critical Thinking Development in EFL Learning: A Systematic Review and Meta-Analysis. Computer-Assisted Language Learning Electronic Journal, 26(7), 90–116. https://callej.org/index.php/journal/article/view/807
Zhu, M., Bonk, C., & Doo, M. Y. (2020). Self-directed learning in MOOCs: Exploring the relationships among motivation, self-monitoring, and self-management. Educational Technology Research and Development, 68. https://doi.org/10.1007/s11423-020-09747-8
Zuccarini, G., & Malgieri, M. (2024). Modeling and Representing Conceptual Change in the Learning of Successive Theories. Science & Education, 33(3), 717–761. https://doi.org/10.1007/s11191-022-00397-1
Copyright (c) 2026 Reni Marlina, Hamdani Hamdani, Rahmania Pamungkas

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The journal allows the author(s) to hold the copyright without restrictions. Finally, the journal allows the author(s) to retain publishing rights without restrictions
![]() | Jurnal Inovasi Teknologi Pendidikan by http://journal.uny.ac.id/index.php/jitp is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |








