Exploring students’ computational thinking in mathematics: A self-efficacy perspective

Julia Noviani; Ega Gradini; Mila Gusdiana

Exploring students’ computational thinking in mathematics: A self-efficacy perspective

Abstract

Computational thinking is an essential component of mathematical problem solving; however, students may enact computational thinking processes differently depending on their self-efficacy. This study explored how students with high and low self-efficacy enact computational thinking during mathematical problem solving. A qualitative approach was employed to capture in-depth differences in students’ reasoning processes. The participants consisted of six students, including three students with high self-efficacy and three students with low self-efficacy. Data were collected through written problem-solving tasks and semi-structured interviews, and the analysis focused on four core components of computational thinking: decomposition, abstraction, algorithmic thinking, and generalization. The findings show that differences between high and low self-efficacy students were reflected not in answer correctness, but in how computational thinking and problem-solving processes were enacted. Students with high self-efficacy demonstrated integrated use of decomposition, abstraction, algorithmic thinking, and generalization, characterized by organized problem representation, recognition of relational patterns, coherent progression across solution steps, and the ability to extend reasoning beyond the given task. In contrast, students with low self-efficacy exhibited fragmented and outcome-oriented approaches, relying primarily on direct computation for the final case with limited engagement in problem structure, justification, or transfer. Overall, these findings suggest that examining computational thinking through the lens of self-efficacy provides valuable process-level insight into how students reason during mathematical problem solving. Rather than conceptualizing computational thinking solely in terms of indicator attainment, this study highlights qualitative variation in the coherence and integration of computational thinking processes across differing self-efficacy levels, offering implications for mathematics instruction and assessment.

Keywords

Supporting Institution

Institut Agama Islam Negeri Takengon

Ethical Statement

Ethical approval for the study was obtained from the relevant institutional ethics committee prior to data collection. Permission to conduct the study was also granted by the participating school. Written informed consent was obtained from students and their parents or guardians. Participants were informed of the study’s purpose, procedures, and their right to withdraw at any time without penalty. To ensure confidentiality, all participants were assigned anonymized subject codes (e.g., SE-Hi01, SE-Lo01), and no identifying information appears in the manuscript. All data were securely stored and used solely for research purposes.

References

Ali, I., & Das, S. (2024). Direct and Indirect Effect of Self-Efficacy, Anxiety and Interest on Algebraic Problem-Solving Achievement. Mathematics Teaching-Research Journal, 16(2), 102–118.
Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. In Computers in human behavior (Vol. 105, p. 106185). Elsevier. https://doi.org/https://doi.org/10.1016/j.chb.2019.106185
Atman Uslu, N. (2023). How do computational thinking self-efficacy and performance differ according to secondary school students’ profiles? The role of computational identity, academic resilience, and gender. Education and Information Technologies, 28(5), 6115–6139. https://doi.org/10.1007/s10639-022-11425-6
Azizia, A. J., Kusmaryono, I., Maharani, H. R., & Arifuddin, A. (2023). Students’ Computational Thinking Process in Solving PISA Problems of Change and Relationship Content Reviewed from Students’ Self Efficacy. Eduma : Mathematics Education Learning and Teaching, 12(1), 112. https://doi.org/10.24235/eduma.v12i1.13132
Bandura, A. (2006). Guide for constructing self-efficacy scales. Self-Efficacy Beliefs of Adolescents, 5(1), 307– 337.
Bandura, A. (2013). Self-Efficacy: The foundation of agency1. In Control of human behavior, mental processes, and consciousness (pp. 16–30). Psychology Press.
Bandura, A. (2014). Self-efficacy mechanism in psychobiologic functioning. In Self-Efficacy (pp. 355–394). Taylor & Francis.
Barcelos, T. S., Muñoz-Soto, R., Villarroel, R., Merino, E., & Silveira, I. F. (2018). Mathematics Learning through Computational Thinking Activities: A Systematic Literature Review. J. Univers. Comput. Sci., 24(7), 815–845.

Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M., Jasutė, E., & Malagoli, C. (2022). Reviewing computational thinking in compulsory education: State of play and practices from computing education. Publications Office of the European Union. https://doi.org/10.2760/126955
Chiang, F.-K., Zhang, Y., Zhu, D., Shang, X., & Jiang, Z. (2022). The influence of online STEM education camps on students’ self-efficacy, computational thinking, and task value. Journal of Science Education and Technology, 31(4), 461–472. https://doi.org/10.1007/s10956-022-09967-y
Cleary, T. J., & Zimmerman, B. J. (2004). Self‐regulation empowerment program: A school‐based program to enhance self‐regulated and self‐motivated cycles of student learning. Psychology in the Schools, 41(5), 537– 550. https://doi.org/10.1002/pits.10177
Cordero, E. D., Porter, S. H., Israel, T., & Brown, M. T. (2010). Math and science pursuits: A self-efficacy intervention comparison study. Journal of Career Assessment, 18(4), 362–375. https://doi.org/10.1177/1069072710374572
Creswell, J. W., & Creswell, J. D. (2018). Mixed Methods Procedures. In Research Defign: Qualitative, Quantitative, and Mixed M ethods Approaches (5th ed.). Sage Publications.
Danindra, L. S., Masriyah, M., & Hanifah, U. (2022). Computational Thinking Processes of Junior High School Students in Solving Problems of Number Patterns in Terms of Gender Differences. SHS Web of Conferences, 149, 1012. https://doi.org/10.1051/shsconf/202214901012
Fast, L. A. (2015). Self-Efficacy and Standardized Test Performance. Physiological Research, 64(6), 897–905.
Gandhi, H., & Varma, M. (2010). Strategic content learning approach to promote self-regulated learning in mathematics. Poceedings of EpiSTME, 3, 119–124. http://web.gnowledge.org/episteme3/pro_pdfs/19-haneet- verma.pdf
Gao, X., & Hew, K. F. (2022). Toward a 5E-based flipped classroom model for teaching computational thinking in elementary school: Effects on student computational thinking and problem-solving performance. Journal of Educational Computing Research, 60(2), 512–543. https://doi.org/10.1177/07356331211037757
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Gunawan, Ferdianto, F., Ulia, N., Akhsani, L., Untarti, R., & Istiqomah. (2025). The Profile of Students’ Mathematical Computational Thinking Process in Terms of Self-Efficacy. Mathematics Teaching-Research Journal, 17(1), 213– 231.
Hoffman, B. (2010). “I think I can, but I’m afraid to try”: The role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency. Learning and Individual Differences, 20(3), 276–283. https://doi.org/10.1016/j.lindif.2010.02.001
Hoffman, B., & Spatariu, A. (2008). The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary Educational Psychology, 33(4), 875–893.
Jacob, S. R., & Warschauer, M. (2018). Computational thinking and literacy. Journal of Computer Science Integration, 1(1). https://doi.org/10.26716/jcsi.2018.01.1.1
Kalelioglu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583. https://www.bjmc.lu.lv/fileadmin/user_upload/lu_portal/projekti/bjmc/Contents/4_3_15_Kalelioglu.pdf
Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3, 154–171. https://doi.org/10.1007/s40751-017-0031-2
Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014). Computational thinking in K-9 education. Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, 1–29. https://doi.org/10.1145/2713609.2713610
Margolis, H., & McCabe, P. P. (2003). Self-efficacy: A key to improving the motivation of struggling learners. Preventing School Failure: Alternative Education for Children and Youth, 47(4), 162–169. https://doi.org/10.1080/10459880309603362
Margolis, H., & McCabe, P. P. (2006). Improving self-efficacy and motivation: What to do, what to say. Intervention in School and Clinic, 41(4), 218–227.
McLeod, D. B., & Adams, V. M. (2012). Affect and mathematical problem solving: A new perspective. Springer Science & Business Media.
Mukhibin, A., Herman, T., A, E. C. M., & Utomo, D. A. S. (2024). Kemampuan computational thinking siswa pada materi garis dan sudut ditinjau dari self-efficacy. JPMI (Jurnal Pembelajaran Matematika Inovatif), 7(1), 143–152. https://doi.org/10.22460/jpmi.v7i1.21239
Öztürk, M., Akkan, Y., & Kaplan, A. (2020). Reading comprehension, Mathematics self-efficacy perception, and Mathematics attitude as correlates of students’ non-routine Mathematics problem-solving skills in Turkey. International Journal of Mathematical Education in Science and Technology, 51(7), 1042–1058. https://doi.org/10.1080/0020739X.2019.1648893
Polya, G. (1945). How to solve it: A new aspect of mathematical method. Princeton university press.
Rahmadhani, E. (2025). Mapping the development of computational thinking in mathematics education : A bibliometric analysis. Journal for the Mathematics Education and Teaching Practices, 6(2), 105–123. https://doi.org/10.5281/zenodo.17995443
Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691. https://doi.org/https://doi.org/10.1016/j.chb.2016.08.047
Schunk, D. H., & Pajares, F. (2002). The development of academic self-efficacy. In Development of achievement motivation (pp. 15–31). Elsevier. https://doi.org/10.1016/B978-012750053-9/50003-6
Seehorn, D., & Clayborn, L. (2017). CSTA K-12 CS standards for all. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, 730. https://doi.org/https://doi.org/10.1145/3017680.3022341
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
Skaalvik, E. M., & Skaalvik, S. (2006). Self-concept and self-efficacy in mathematics: Relation with mathematics motivation and achievement. The Concept of Self in Education, Family and Sports, 51–74.
Stevens, T., Olivarez, A., Lan, W. Y., & Tallent-Runnels, M. K. (2004). Role of mathematics self-efficacy and motivation in mathematics performance across ethnicity. The Journal of Educational Research, 97(4), 208–222. https://doi.org/10.3200/JOER.97.4.208-222
Susanti, R. D., Lukito, A., & Ekawati, R. (2024). Pattern Recognition in Computational Thinking: Semiotic Perspective. Mathematics Teaching-Research Journal, 16(5), 218–239.
Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275–314. https://doi.org/10.3102/0002831208324517
Van Dinther, M., Dochy, F., & Segers, M. (2011). Factors affecting students’ self-efficacy in higher education. Educational Research Review, 6(2), 95–108. https://doi.org/10.1016/j.edurev.2010.10.003
Voica, C., Singer, F. M., & Stan, E. (2020). How are motivation and self-efficacy interacting in problem-solving and problem-posing? Educational Studies in Mathematics, 105(3), 487–517. https://doi.org/10.1007/s10649-020- 10005-0
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60, 565–568. https://doi.org/10.1007/s11528-016-0087-7
Yin, R. K. (2018). Case study research and applications (Vol. 6). Sage Thousand Oaks, CA.
Yin, S. X., Hoe-Lian Goh, D., & Quek, C. L. (2024). Collaborative learning in K-12 computational thinking education: A systematic review. Journal of Educational Computing Research, 62(6), 1220–1254. https://doi.org/https://doi.org/10.1177/07356331241249956
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016
Zimmerman, B., & Kitsantas, A. (2007). Reliability and validity of self-efficacy for learning form (SELF) scores of college students. Zeitschrift Für Psychologie/Journal of Psychology, 215(3), 157–163. https://doi.org/10.1027/0044-3409.215.3.157

Details

Primary Language

English

Subjects

Mathematics Education

Journal Section

Research Article

Authors

Julia Noviani ^*
0000-0003-4853-0425
Indonesia

Ega Gradini
Indonesia

Mila Gusdiana
0009-0006-4368-9129
Indonesia

Early Pub Date

June 11, 2026

Publication Date

-

Submission Date

November 8, 2025

Acceptance Date

January 19, 2026

Published in Issue

Year 2026 Number: Advanced Online Publication

IZ

https://izlik.org/JA97AY75BG

Cite

RIS / Bibtex

APA

Noviani, J., Gradini, E., & Gusdiana, M. (2026). Exploring students’ computational thinking in mathematics: A self-efficacy perspective. Journal for the Mathematics Education and Teaching Practices, Advanced Online Publication, 55-77. https://izlik.org/JA97AY75BG

AMA

1.Noviani J, Gradini E, Gusdiana M. Exploring students’ computational thinking in mathematics: A self-efficacy perspective. JMETP. 2026;(Advanced Online Publication):55-77. https://izlik.org/JA97AY75BG

Chicago

Noviani, Julia, Ega Gradini, and Mila Gusdiana. 2026. “Exploring Students’ Computational Thinking in Mathematics: A Self-Efficacy Perspective”. Journal for the Mathematics Education and Teaching Practices, no. Advanced Online Publication: 55-77. https://izlik.org/JA97AY75BG.

EndNote

Noviani J, Gradini E, Gusdiana M (June 1, 2026) Exploring students’ computational thinking in mathematics: A self-efficacy perspective. Journal for the Mathematics Education and Teaching Practices Advanced Online Publication 55–77.

IEEE

[1]J. Noviani, E. Gradini, and M. Gusdiana, “Exploring students’ computational thinking in mathematics: A self-efficacy perspective”, JMETP, no. Advanced Online Publication, pp. 55–77, June 2026, [Online]. Available: https://izlik.org/JA97AY75BG

ISNAD

Noviani, Julia - Gradini, Ega - Gusdiana, Mila. “Exploring Students’ Computational Thinking in Mathematics: A Self-Efficacy Perspective”. Journal for the Mathematics Education and Teaching Practices. Advanced Online Publication (June 1, 2026): 55-77. https://izlik.org/JA97AY75BG.

JAMA

1.Noviani J, Gradini E, Gusdiana M. Exploring students’ computational thinking in mathematics: A self-efficacy perspective. JMETP. 2026;:55–77.

MLA

Noviani, Julia, et al. “Exploring Students’ Computational Thinking in Mathematics: A Self-Efficacy Perspective”. Journal for the Mathematics Education and Teaching Practices, no. Advanced Online Publication, June 2026, pp. 55-77, https://izlik.org/JA97AY75BG.

Vancouver

1.Julia Noviani, Ega Gradini, Mila Gusdiana. Exploring students’ computational thinking in mathematics: A self-efficacy perspective. JMETP [Internet]. 2026 Jun. 1;(Advanced Online Publication):55-77. Available from: https://izlik.org/JA97AY75BG