Research Article

Understanding student motivation and self-efficacy in differential calculus: Evidence from a technology-based learning approach

Jaime Segarra 1 * , Andres Galarza 1 , Leopoldo Pauta 1 , Abel Cabrera-Martínez 2
More Detail
1 Universidad Católica de Cuenca, Cuenca, ECUADOR2 Universidad de Córdoba, Córdoba, SPAIN* Corresponding Author
European Journal of Science and Mathematics Education, 14(2), April 2026, 295-305, https://doi.org/10.30935/scimath/18253
Published: 26 March 2026
OPEN ACCESS   186 Views   75 Downloads
Download Full Text (PDF)

ABSTRACT

The present study analyzed student perceptions of an alternative educational modality for the teaching of differential calculus at an Ecuadorian university. A 20-item questionnaire was applied to 70 engineering students, evaluated by exploratory and confirmatory factor analysis, and complemented with multiple regression models and student’s t-tests. The results confirmed a four-factor structure: teaching modality and experience, practices and resources, theoretical classes and demand, and self-efficacy and academic performance. The model showed adequate fit and validity indices, consolidating the relevance of the instrument. Likewise, self-efficacy emerged as the central factor, both for its predictive role in the other components and for being the aspect most highly valued by the students. These findings show the relevance of considering organizational, methodological and motivational dimensions in the design of innovative pedagogical proposals to strengthen learning in highly demanding subjects such as differential calculus.
Keywords: alternative educational modality, academic motivation, differential calculus, factor analysis, self-efficacy, engineering

CITATION (APA)

Segarra, J., Galarza, A., Pauta, L., & Cabrera-Martínez, A. (2026). Understanding student motivation and self-efficacy in differential calculus: Evidence from a technology-based learning approach. European Journal of Science and Mathematics Education, 14(2), 295-305. https://doi.org/10.30935/scimath/18253

REFERENCES

  1. Alabdulaziz, M. S., & Higgins, S. (2025). Faculty members’ perspectives on effective instructional methods for teaching calculus in the UK and Saudi universities. Education and Practice International Journal, 16(223), Article e2025223. https://doi.org/10.22521/edupij.2025.16.223
  2. Alé, J., & Arancibia, M. L. (2025). Emerging technology-based motivational strategies: A systematic review with meta-analysis. Education Sciences, 15(2), Article 197. https://doi.org/10.3390/educsci15020197
  3. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
  4. Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15, 1-40. https://doi.org/10.1023/A:1021302408382
  5. Bonilla, S. M., Jácome-Domínguez, E. Á., Moreno-Novillo, Á. C., & Albuja-Jácome, J. E. (2024). Design and validation of a questionnaire on the perception of learning differential equations using software in university students. Salud, Ciencia y Tecnología, 4, Article 912. https://doi.org/10.56294/saludcyt2024.1300
  6. Brashi, A. (2022). Self-efficacy in the prediction of GPA and academic computer use in undergraduate translation students at a Saudi University. Frontiers in Psychology, 13, Article 865581. https://doi.org/10.3389/fpsyg.2022.865581
  7. Bressoud, D. (2015). Insights from the MAA national study of college calculus. Mathematics Teacher, 109(9), 700-706. https://doi.org/10.5951/mathteacher.109.3.0178
  8. Bressoud, D., Mesa, V., & Rasmussen, C. (2015). Insights and recommendations from the MAA national study of college calculus. Mathematical Association of America.
  9. Chechan, B., Ampadu, E., & Pears, A. (2023). Effect of using Desmos on high school students’ understanding and learning of functions. Eurasia Journal of Mathematics, Science and Technology Education, 19(10), Article em2331. https://doi.org/10.29333/ejmste/13540
  10. Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer.
  11. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
  12. Elmi, C. (2020). Integrating social emotional learning strategies in higher education. European Journal of Investigation in Health, Psychology and Education, 10(3), 850-860. https://doi.org/10.3390/ejihpe10030061
  13. El-Thalji, I. (2025). Boosting active learning through a gamified flipped classroom: A retrospective case study in higher engineering education. Education Sciences, 15(4), Article 430. https://doi.org/10.3390/educsci15040430
  14. Emiru, E. K., & Gedifew, M. T. (2024). The effect of teacher self-efficacy on learning engagement of secondary school students. Cogent Education, 11(1), Article 2308432. https://doi.org/10.1080/2331186X.2024.2308432
  15. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. PNAS, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
  16. Geng, X., & Su, Y.-S. (2025). The effects of different metacognitive patterns on students’ self-regulated learning in blended learning. Computers & Education, 227, Article 105211. https://doi.org/10.1016/j.compedu.2024.105211
  17. González, J. I. (2025). Dificultades en la enseñanza-aprendizaje del cálculo diferencial en carreras de ingeniería de UNAN-Managua, CUR-Estelí Nicaragua [Difficulties in the teaching-learning of differential calculus in engineering programs at UNAN-Managua, CUR-Estelí Nicaragua]. Revista Científica Estelí, 14(54), 131-151. https://doi.org/10.5377/esteli.v14i54.20781
  18. Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
  19. Hammoudi, M. M., & Grira, S. (2022). Improving students’ motivation in calculus courses at institutions of higher education: Evidence from graph-based visualization of two models. Eurasia Journal of Mathematics, Science and Technology Education, 19(1), Article em2209. https://doi.org/10.29333/ejmste/12771
  20. Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
  21. Huang, T., & Lin, Y. (2017). Development and validation of a motivation scale for university students in calculus courses. International Journal of Science and Mathematics Education, 13(5), 109-123.
  22. Husin, M., Usmeldi, Usmeldi, Masdi, H., Simatupang, W., Fadhilah, F., & Hendriyani, Y. (2025). Project-based problem learning: Improving problem-solving skills in higher education engineering students. International Journal of Sociology of Education, 14(1), 62-84. https://doi.org/10.17583/rise.15125
  23. Klassen, R. M., & Klassen, J. R. L. (2018). Self-efficacy beliefs of medical students: A critical review. Perspectives on Medical Education, 7(2), 76-82. https://doi.org/10.1007/s40037-018-0411-3
  24. Lazar, I. M., Panisoara, G., & Panisoara, I. O. (2020). Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool. PLoS ONE, 15(7), Article e0235957. https://doi.org/10.1371/journal.pone.0235957
  25. Meng, Q., & Zhang, Q. (2023). The influence of academic self-efficacy on university students’ academic performance: The mediating effect of academic engagement. Sustainability, 15(7), Article 5767. https://doi.org/10.3390/su15075767
  26. Mohammed, A. M., Huneiti, Z. A., Balachandran, W., & Al-Naafa, M. A. (2013). A study of the effects of using MATLAB as a pedagogical tool for engineering mathematics. International Journal of Online and Biomedical Engineering, 9(2), 27-35. https://doi.org/10.3991/ijoe.v9i2.2511
  27. Pedersen, M. K., Svenningsen, A., Dohn, N. B., Lieberoth, A., & Sherson, J. (2016). DiffGame: Game-based mathematics learning for physics. Procedia-Social and Behavioral Sciences, 228, 316-322. https://doi.org/10.1016/j.sbspro.2016.07.047
  28. Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385-407. https://doi.org/10.1007/s10648-004-0006-x
  29. Schel, J., & Drechsel, B. (2025). A latent profile analysis for teacher education students’ learning: An overview of competencies in self-regulated learning. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1527438
  30. Segarra, J., & Cabrera-Martínez, A. (2024). Does the use of the calculator reduce anxiety in the study of differential and integral calculus? European Journal of Science and Mathematics Education, 12(1), 97-111. https://doi.org/10.30935/scimath/13867
  31. Singun, A. (2025). Unveiling the barriers to digital transformation in higher education institutions: A systematic literature review. Discover Education, 4, Article 37. https://doi.org/10.1007/s44217-025-00430-9
  32. Song, S. H., Antonelli, M., Fung, T. W., Armstrong, B. D., Chong, A., Lo, A., & Shi, B. E. (2018). Developing and assessing MATLAB exercises for active concept learning. IEEE Transactions on Education, 62(1), 2-10. https://doi.org/10.1109/TE.2018.2811406
  33. Tsamir, P., Rasslan, S., & Dreyfus, T. (2006). Prospective teachers’ reactions to Right-or-Wrong tasks: The case of derivatives of absolute value functions. The Journal of Mathematical Behavior, 25(3), 240-251. https://doi.org/10.1016/j.jmathb.2006.09.001
  34. Usher, E. L., & Pajares, F. (2009). Sources of self-efficacy in mathematics: A validation study. Contemporary Educational Psychology, 34(1), 89-101. https://doi.org/10.1016/j.cedpsych.2008.09.002
  35. Wang, C., Cho, H. J., Wiles, B., Moss, J. D., Bonem, E. M., Li, Q., Lu, Y., & Levesque-Bristol, C. (2022). Competence and autonomous motivation as motivational predictors of college students’ mathematics achievement: From the perspective of self-determination theory. International Journal of STEM Education, 9, Article 41. https://doi.org/10.1186/s40594-022-00359-7
  36. Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219-246. https://doi.org/10.1177/0095798418771807
  37. Xu, B. (2024). Mediating role of academic self-efficacy and academic emotions in the relationship between teacher support and academic achievement. Scientific Reports, 14, Article 24705. https://doi.org/10.1038/s41598-024-75768-5
  38. Zakariya, Y. F., & Adegoke, N. A. (2024). Teacher instructional practices: Untangling their complex relations with self-efficacy, job satisfaction, stress, and cooperation among mathematics teachers. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1367076
  39. Zee, M., & Koomen, H. M. Y. (2016). Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational Research, 86(4), 981-1015. https://doi.org/10.3102/0034654315626801
  40. Zimmerman, B. J. (2010). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2