Calculator Reliance and Computational Self-Efficacy among Electrical Engineering Technology Students

Calculator use is common in engineering and engineering technology education, particularly in courses that involve quantitative problem solving. While calculators may support efficiency and reduce unnecessary computational burden, excessive dependence on calculator outputs may also shape students’ confidence, mental effort, and independent computational behaviour. This exploratory study used selected concepts from educational psychology to examine calculator reliance and computational self-efficacy among Electrical Engineering Technology students. The study used a quantitative descriptive-correlational design. Data were collected from 55 Electrical Engineering Technology students through a structured questionnaire using five-point Likert-scale items. Calculator Reliance was measured using six items, Computational Self-Efficacy using six items, and Perceived Cognitive Offloading using four supporting items. Descriptive statistics, Cronbach’s alpha, and Spearman’s correlation analysis were used.
The mean score was 3.75 (standard deviation [SD] = 0.73) for calculator reliance and 3.22 (SD = 0.57) for computational self-efficacy. Calculator reliance was not significantly associated with computational self-efficacy (Spearman’s ρ = 0.173, p = 0.206), but it was significantly associated with the exploratory measure of perceived cognitive offloading (Spearman’s ρ = 0.384, p = 0.004). Computational self-efficacy was significantly associated with self-perceived analytical accuracy (Spearman’s ρ = 0.372, p = 0.005). Calculator reliance should not be treated as direct evidence of weak computational ability because actual performance was not assessed. The results suggest that calculator reliance may involve confidence, external support, and perceived mental effort. Given the limited sample and use of self-reported measures, the findings should be interpreted as exploratory and specific to this engineering technology programme.

calculator reliance, computational self-efficacy, perceived cognitive offloading, educational psychology, engineering technology students

1. Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676-688. [Google Scholar] [Crossref]

2. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. [Google Scholar] [Crossref]

3. Hembree, R., & Dessart, D. J. (1986). Effects of hand-held calculators in precollege mathematics education: A meta-analysis. Journal for Research in Mathematics Education, 17(2), 83-99. [Google Scholar] [Crossref]

4. Ellington, A. J. (2003). A meta-analysis of the effects of calculators on students’ achievement and attitude levels in precollege mathematics classes. Journal for Research in Mathematics Education, 34(5), 433-463. [Google Scholar] [Crossref]

5. Grinschgl, S., & Neubauer, A. C. (2022). Supporting cognition with modern technology: Distributed cognition today and in an AI-enhanced future. Frontiers in Artificial Intelligence, 5, 908261. [Google Scholar] [Crossref]

6. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776-778. [Google Scholar] [Crossref]

7. Storm, B. C., Stone, S. M., & Benjamin, A. S. (2017). Using the Internet to access information inflates future use of the Internet to access other information. Memory, 25(6), 717-723. [Google Scholar] [Crossref]

8. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman. [Google Scholar] [Crossref]

9. Pajares, F., & Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology, 86(2), 193-203. [Google Scholar] [Crossref]

10. Betz, N. E., & Hackett, G. (1983). The relationship of mathematics self-efficacy expectations to the selection of science-based college majors. Journal of Vocational Behavior, 23(3), 329-345. [Google Scholar] [Crossref]

11. Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. [Google Scholar] [Crossref]

12. Zakariya, Y. F., Goodchild, S., Bjørkestøl, K., & Nilsen, H. K. (2019). Calculus self-efficacy inventory: Its development and relationship with approaches to learning. Education Sciences, 9(3), 170. [Google Scholar] [Crossref]

13. Mamaril, N. A., Usher, E. L., Li, C. R., Economy, D. R., & Kennedy, M. S. (2016). Measuring undergraduate students’ engineering self-efficacy: A validation study. Journal of Engineering Education, 105(2), 366-395. [Google Scholar] [Crossref]

14. Marra, R. M., Rodgers, K. A., Shen, D., & Bogue, B. (2009). Women engineering students and self-efficacy: A multi-year, multi-institution study of women engineering student self-efficacy. Journal of Engineering Education, 98(1), 27-38. [Google Scholar] [Crossref]

15. Hunsu, N. J., Olaogun, O. P., Oje, A. V., Carnell, P. H., & Morkos, B. (2023). Investigating students' motivational goals and self-efficacy and task beliefs in relationship to course attendance and prior knowledge in an undergraduate statics course. Journal of Engineering Education, 112(1), 108-124. [Google Scholar] [Crossref]

16. Power, J. R., Tanner, D., & Buckley, J. (2025). Self-efficacy development in undergraduate engineering education. European Journal of Engineering Education, 50(1), 1–25. [Google Scholar] [Crossref]

17. Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21, 34. [Google Scholar] [Crossref]

18. Zhang, L., & Xu, J. (2025). The paradox of self-efficacy and technological dependence: Unraveling generative AI's impact on university students' task completion. The Internet and Higher Education, 65, 100978. [Google Scholar] [Crossref]

19. Ellington, A. J. (2006). The effects of non-CAS graphing calculators on student achievement and attitude levels in mathematics: A meta-analysis. School Science and Mathematics, 106(1), 16-26. [Google Scholar] [Crossref]

20. Sullivan, G. M., & Artino, A. R. Jr. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541-542. [Google Scholar] [Crossref]

21. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. [Google Scholar] [Crossref]

22. Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach’s alpha test: A simple guide for researchers. Malaysian Journal of Medical Sciences, 25(6), 85-99. [Google Scholar] [Crossref]

23. Usher, E. L., & Pajares, F. (2009). Sources of self-efficacy in mathematics: A validation study. Contemporary Educational Psychology, 34(1), 89-101. [Google Scholar] [Crossref]

24. Lent, R. W., Brown, S. D., & Larkin, K. C. (1984). Relation of self-efficacy expectations to academic achievement and persistence. Journal of Counseling Psychology, 31(3), 356-362. [Google Scholar] [Crossref]

25. Williams, D. M., & Rhodes, R. E. (2016). The confounded self-efficacy construct: Conceptual analysis and recommendations for future research. Health Psychology Review, 10(2), 113-128. [Google Scholar] [Crossref]

26. Whitcomb, K. M., Kalender, Z. Y., Nokes-Malach, T. J., Schunn, C. D., & Singh, C. (2020). Comparison of self-efficacy and performance of engineering undergraduate women and men. International Journal of Engineering Education, 36(6), 1996-2014. [Google Scholar] [Crossref]

27. Wang, J. (2026). Cognitive offloading through digital tools and its relationship with critical thinking, task persistence, and learning depth. Frontiers in Psychology, 17, 1781101. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study