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Journals / Journal for the Mathematics Education and Teaching Practices / 2026 / Volume: 7 Issue: 1

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

Julia Noviani(Takengon State Islamic Institute)0000-0003-4853-0425
Ega Gradini(Institut Agama Islam Negeri Takengon)
Mila Gusdiana(Institut Agama Islam Negeri Takengon)0009-0006-4368-9129
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Computational thinking is an essential component of mathematical problem solving; however, students may enact computational thinking processes differently depending on their self-efficacy.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:computational thinking · Mathematical problem solving · Qualitative case study

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.

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Cite This Article

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, Volume: 7 Issue: 1. https://doi.org/10.5281/zenodo.21054704

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