Development of Deep Learning-Based Instructional Materials to Improve Cognitive Process Skills in Elementary Science Education

Authors

  • Ully Arta Miladia Universitas Sultan Ageng Tirtayasa, Indonesia
  • Abdul Fatah Universitas Sultan Ageng Tirtayasa, Indonesia
  • Yuvita Oktarisa Universitas Sultan Ageng Tirtayasa, Indonesia

DOI:

https://doi.org/10.33394/jk.v12i2.20373

Keywords:

deep learning, instructional materials, cognitive process skills, science education, Higher-Order Thinking Skills

Abstract

This study aims to develop deep learning-based instructional materials on the topic of states of matter and their changes, to determine the feasibility of the developed materials, and to examine their effectiveness in improving students’ cognitive process skills. The study employed a modified 4D Research and Development (R&D) model consisting of the define, design, develop, and limited dissemination stages. The participants were 35 fourth-grade students at SDN Serdang 1, Banten Province, Indonesia. The developed products included learning outcomes, learning objective sequences, teaching modules, student worksheets, Canva-based learning media, and assessment instruments. Data were collected through expert validation, student response questionnaires, and pretest–posttest assessments. The data were analyzed using descriptive percentage analysis and normalized gain (N-gain). The results showed that the developed instructional materials were categorized as highly feasible in terms of content, media, and language aspects. Furthermore, the effectiveness test indicated a moderate improvement in students’ cognitive process skills after the implementation of the instructional materials. The highest improvement was observed in applying skills (C3), followed by analyzing (C4) and evaluating (C5). These findings indicate that deep learning-based instructional materials can effectively support meaningful science learning and improve elementary school students’ cognitive process skills. Therefore, the developed instructional materials may serve as an alternative instructional model aligned with the demands of 21st-century education.

References

Belda-Medina, J., & Kokošková, V. (2023). Integrating AI tools in education: Impacts on student learning. International Journal of Educational Technology. https://doi.org/10.1186/s41239-023-00432-3

Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2020). Emergency remote teaching in higher education: Mapping the first global online semester. Educational Review, 73(5), 1–24. https://doi.org/10.1080/02671522.2020.1831455

Bond, M., et al. (2020). Digital transformation in education: A systematic review. Educational Technology & Society, 23(2), 1–15.

Chang, Y., Choi, J., & Şen-Akbulut, M. (2024). Student engagement in contextual learning environments. Education Sciences. https://doi.org/10.3390/educsci14020168

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

Clark, R. C., Nguyen, F., & Sweller, J. (2021). Efficiency in learning: Evidence-based guidelines. Wiley.

Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791

Erdem, C. (2024). Problem-based learning effects on student cognitive outcomes: A meta-analysis. Educational Studies. https://doi.org/10.1080/03055698.2022.2103650

Freeman, S., et al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111

Fullan, M., & Langworthy, M. (2014). A rich seam: How new pedagogies find deep learning. Pearson.

Hake, R. R. (1999). Analyzing change/gain scores. Indiana University.

Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S., & Reiss, K. (2020). The potential of digital tools to enhance mathematics and science learning: A meta-analysis. Educational Research Review, 30, 100341. https://doi.org/10.1016/j.edurev.2020.100341

Hwang, G. J., & Chien, S. Y. (2022). Definition, roles, and potential of deep learning in education. Educational Technology & Society, 25(3), 1–13.

Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi. (2022). Panduan pembelajaran dan asesmen pada Kurikulum Merdeka. Kemendikbudristek.

Kim, H., Park, E., & Baek, Y. (2021). The effects of digital problem-based learning on student engagement. Computers & Education. https://doi.org/10.1016/j.compedu.2021.104233

Kirschner, P. A., & Hendrick, C. (2020). How learning happens: Seminal works in educational psychology. Routledge.

Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press.

OECD. (2019). Future of education and skills 2030: OECD learning compass 2030. OECD Publishing.

OECD. (2023). PISA 2022 results: Learning during and beyond disruption. OECD Publishing.

Paas, F., & van Merriënboer, J. (2020). Cognitive load theory: Methods to optimize learning. Educational Psychologist, 55(4), 273–289. https://doi.org/10.1080/00461520.2020.1783692

Piaget, J. (1972). The psychology of the child. Basic Books.

Putra, A., & Rahmawati, N. (2021). Pengembangan perangkat pembelajaran IPA berbasis inkuiri untuk meningkatkan kemampuan proses kognitif siswa sekolah dasar. Jurnal Pendidikan Dasar, 12(2), 115–126.

Redecker, C. (2017). European framework for digital competence of educators. European Commission.

Sari, D., & Sutopo, H. (2020). Pengembangan perangkat pembelajaran IPA berbasis higher order thinking skills di sekolah dasar. Jurnal Pendidikan IPA, 9(1), 45–53.

Schindler, L. A., et al. (2017). Computers in human behavior and student engagement. Computers in Human Behavior, 69, 170–191.

Sweller, J., Ayres, P., & Kalyuga, S. (2019). Cognitive load theory. Springer.

Thiagarajan, S., Semmel, D. S., & Semmel, M. I. (1974). Instructional development for training teachers of exceptional children. University of Minnesota.

Utaminingsih, E. S., & Ellianawati, E. (2025). Development of digital learning modules in technology-based environments. Turkish Online Journal of Distance Education.

Voogt, J., et al. (2018). Computational thinking in education. Education and Information Technologies, 23, 1–20.

Wahab, A., et al. (2021). Analisis N-Gain untuk mengukur efektivitas pembelajaran. Jurnal Pendidikan, 5(2), 55–62.

Wahyudi, A. B. E., et al. (2025). Local wisdom-based learning to improve critical thinking skills. Journal of Education and Learning.

Yuliantoro, Y., & Setyowati, D. L. (2025). Digital media based on local wisdom to increase student engagement. Al-Ishlah Journal.

Zainuri, B. N. S., Gunawan, G., & Kosim, K. (2025). Validity of PBL-based e-modules integrated with local wisdom. Jurnal Pendidikan Fisika dan Teknologi.

Zheng, L., Li, X., Tian, L., & Cui, P. (2020). The effectiveness of digital learning tools on student engagement and learning outcomes. Computers & Education, 150, 103842. https://doi.org/10.1016/j.compedu.2020.103842

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Published

2026-06-09

How to Cite

Miladia, U. A., Fatah, A., & Oktarisa, Y. (2026). Development of Deep Learning-Based Instructional Materials to Improve Cognitive Process Skills in Elementary Science Education. Jurnal Kependidikan : Jurnal Hasil Penelitian Dan Kajian Kepustakaan Di Bidang Pendidikan, Pengajaran, Dan Pembelajaran, 12(2), 912–921. https://doi.org/10.33394/jk.v12i2.20373

Issue

Vol. 12 No. 2 (2026): June (IN PRESS)

Section

Articles

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