Enhancing Deep Learning Through Computational Thinking-Based Assessment

Authors

  • Reza Ruhbani Amarulloh Universitas Islam Negeri Syarif Hidayatullah, Jakarta
  • Nur Habib Muhammad Iqbal Universitas Pendidikan Indonesia
  • Viqhi Aswie MAN 1 Kota Sukabumi

DOI:

https://doi.org/10.38075/jen.v6i1.539

Keywords:

Assessment, Computational Thinking, Deep Learning, 21st Education

Abstract

The transformation of 21st century education demands assessments that not only measure basic cognitive learning outcomes but also encourage deep engagement of higher-order thinking. Computational Thinking (CT), as a systematic and logical thinking approach to problem solving, offers great potential to be integrated in learning assessment design. Thus, this study aims to explore the development of CT-based assessments in promoting deep learning through analyzing relevant scientific literature. The method used is a descriptive qualitative literature review, by analyzing articles from various scientific sources that discuss CT theory, components, and implementation in the context of education and assessment. Data were collected from articles indexed in Google Scholar, ScienceDirect, and other academic sources, then analyzed thematically and comparatively. The results of the discussion show that CT-based assessments, which involve the components of decomposition, abstraction, pattern recognition, algorithms, and generalization, are able to reveal students' thinking processes in depth. This assessment not only measures knowledge, but also encourages reflective, creative, and transdisciplinary skills. In the context of project-based learning, such as the design of an AI-based automatic air purifier system, CT assessments can serve as a tool that encourages students to contextually understand, design, and evaluate solutions. In conclusion, CT-based assessments contribute significantly to deep learning and need to be systematically developed in educational practice. This is in line with the need to form a generation of adaptive, critical, and solutive learners in facing the challenges of a complex and technology-based modern world.

 

Downloads

Download data is not yet available.

References

Alonso-García, S., Rodríguez Fuentes, A. V., Ramos Navas-Parejo, M., & Victoria-Maldonado, J. J. (2024). Enhancing computational thinking in early childhood education with educational robotics: A meta-analysis. Heliyon, 10(13). https://doi.org/10.1016/j.heliyon.2024.e33249

Basu, S., Kinnebrew, J. S., & Biswas, G. (2014). Assessing student performance in a computational-thinking based science learning environment. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8474 LNCS. https://doi.org/10.1007/978-3-319-07221-0_59

Basu, S., McElhaney, K. W., Grover, S., Harris, C. J., & Biswas, G. (2018). A principled approach to designing assessments that integrate science and computational thinking. Proceedings of International Conference of the Learning Sciences, ICLS, 1(2018-June).

Cansu, F. K., & Cansu, S. K. (2019). An Overview of Computational Thinking. International Journal of Computer Science Education in Schools, 3(1), 17–30. https://doi.org/10.21585/ijcses.v3i1.53

Falloon, G. (2024). Advancing young students’ computational thinking: An investigation of structured curriculum in early years primary schooling. Computers and Education, 216. https://doi.org/10.1016/j.compedu.2024.105045

Gane, B. D., Israel, M., Elagha, N., Yan, W., Luo, F., & Pellegrino, J. W. (2021). Design and validation of learning trajectory-based assessments for computational thinking in upper elementary grades. Computer Science Education, 31(2). https://doi.org/10.1080/08993408.2021.1874221

Gane, B., Elagha, N., Luo, F., Liu, R., Yan, W., Strickland, C., Franklin, D., Rich, K. M., Pellegrino, J. W., & Israel, M. (2020). Developing computational thinking assessments for elementary students: Connecting cognition, observation, and interpretation. Computer-Supported Collaborative Learning Conference, CSCL, 3.

Gar Chi, P., Zaffwan Idris, M., & Nugrahani, R. (2021). Virtual Reality (VR) in 21 st. Century Education: The Opportunities and Challenges of Digital Learning in Classroom. 1(2), 105–110. https://doi.org/10.53797/aspen.v1i2.15.2021

Grover, S., Cooper, S., & Pea, R. (2014). Assessing computational learning in K-12. ITICSE 2014 - Proceedings of the 2014 Innovation and Technology in Computer Science Education Conference. https://doi.org/10.1145/2591708.2591713

Grover, S., & Pea, R. (2013). Computational Thinking in K-12: A Review of the State of the Field. In Educational Researcher (Vol. 42, Issue 1, pp. 38–43). https://doi.org/10.3102/0013189X12463051

Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2). https://doi.org/10.1080/08993408.2015.1033142

Hoffer, M. S., Baroni, S., Fronza, I., & Pahl, C. (2019). About computational thinking assessment: A proposal for primary school first year from a pedagogical perspective. CEUR Workshop Proceedings. https://ceur-ws.org/Vol-2434/invited1.pdf

Hou, H., Lai, J. H. K., & Wu, H. (2023). Project-based learning and pedagogies for virtual reality-aided green building education: case study on a university course. … Journal of Sustainability in Higher Education. https://doi.org/10.1108/IJSHE-06-2022-0197

Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers and Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004

Hsu, W.-Y., Goverover, Y., & Bove, R. M. (2023). Capturing cognitive changes in multiple sclerosis by performance-based functional and virtual reality assessments. Annals of Physical and Rehabilitation Medicine, 66(3), 101677. https://doi.org/https://doi.org/10.1016/j.rehab.2022.101677

Inayati, U. (2022). Konsep dan Implementasi Kurikulum Merdeka pada Pembelajaran Abad-21 di SD/MI. International Conference on Islamic Education Volume, 2(2), 293–304.

Kaur, D. P., Mantri, A., & Horan, B. (2020). Enhancing Student Motivation with use of Augmented Reality for Interactive Learning in Engineering Education. Procedia Computer Science, 172, 881–885. https://doi.org/https://doi.org/10.1016/j.procs.2020.05.127

Library of Congress Cataloging-in-Publication Data. (2022). Computational Thinking in Education: A Pedagogical Perspective (A. Yadav & U. D. Berthelsen, Eds.; 1st ed.). Taylor & Francis Group. https://doi.org/10.4324/9781003102991

Luo, F., Yan, W., Liu, R., & Israel, M. (2022). Elementary Students’ Understanding of Variables in Computational Thinking-Integrated Instruction: A Mixed Methods Study. SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, 1. https://doi.org/10.1145/3478431.3499323

Mansur, Candra, & Ramadhan, A. (2024). ANALISIS PEMBELAJARAN SMK BERDASARKAN HASIL ASESMEN NASIONAL DAN PRAKTIK BAIK SEKOLAH. Buletin Asesmen, 1, 11–18.

Min, W., Frankosky, M. H., Mott, B. W., Rowe, J. P., Wiebe, E., Boyer, K. E., & Lester, J. C. (2015). DeepStealth: Leveraging deep learning models for stealth assessment in game-based learning environments. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9112. https://doi.org/10.1007/978-3-319-19773-9_28

Morales, T. M., Bang, E. J., & Andre, T. (2013). A one-year case study: Understanding the rich potential of project-based learning in a virtual reality class for high school students. Journal of Science Education and …. https://doi.org/10.1007/s10956-012-9431-7

Mustaghfirin, U. A., & Zaman, B. (2025). Tinjauan Pendekatan Pembelajaran Mendalam Kemdikdasmen Perspektif Pendidikan Islam. Journal of Instructional and Development Researches, 5(1), 75–85. https://doi.org/10.53621/jider.v5i1.476

Tim Pengembang Pembelajaran Mendalam. (2024). Pembelajaran Mendalam Menuju Pendidikan Bermutu untuk Semua.

Permana, M. P., & Hardyanto, R. H. (2023). Assessing lecturer’s question products as part of maintaining the quality of learning. AIP Conference Proceedings, 2491(1), 020012. https://doi.org/10.1063/5.0105491

Langer, E. J. (2016). The Power of Mindful Learning. Da Capo Press.

Relkin, E., de Ruiter, L. E., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young children. Computers and Education, 169. https://doi.org/10.1016/j.compedu.2021.104222

Riley, D. D., & Hunt, K. A. (n.d.). Chapman & Hall/CRC TEXTBOOKS IN COMPUTING Computational thinking for the modern problem Solver.

Schlauch, M., Sylla, C., & Gil, M. (2025). More than words: Conceptualizing narrative computational thinking based on a multicase study. International Journal of Child-Computer Interaction, 43. https://doi.org/10.1016/j.ijcci.2024.100704

Selby, C. C., & Woollard, J. (2010). Computational Thinking: The Developing Definition.

Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers and Education, 148. https://doi.org/10.1016/j.compedu.2019.103798

Touretzky, D., Gardner-Mccune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What Should Every Child Know about AI? https://playground.tensorflow.org

Tsarava, K., Moeller, K., Román-González, M., Golle, J., Leifheit, L., Butz, M. V., & Ninaus, M. (2022). A cognitive definition of computational thinking in primary education. Computers and Education, 179. https://doi.org/10.1016/j.compedu.2021.104425

Wing, J. M. (2006). Computational Thinking - Viewpoint. Communications of The ACM, 49(3), 33–35.

Zohar, A., & Barzilai, S. (2013). A review of research on metacognition in science education: current and future directions. Studies in Science Education, 49(2), 121–169. https://doi.org/10.1080/03057267.2013.847261

Downloads

Published

2025-07-03

How to Cite

Amarulloh, R. R., Iqbal, N. H. M., & Aswie, V. (2025). Enhancing Deep Learning Through Computational Thinking-Based Assessment . JENTRE, 6(1), 54 - 62. https://doi.org/10.38075/jen.v6i1.539