Academic Grit, Interest, and Technological Attitudes as Predictors of Learners' Performance

Authors

  • John Michael F. Verson Graduate School, Cagayan de Oro College-PHINMA, Cagayan de Oro City, Philippines
  • Joel D. Potane Graduate School, Cagayan de Oro College-PHINMA, Cagayan de Oro City, Philippines/City College of Cagayan de Oro, Cagayan de Oro City, Philippines

DOI:

https://doi.org/10.69569/jip.2025.699

Keywords:

Academic grit, Interest, Technological attitudes, Academic performance, Senior high school

Abstract

As learning environments continue to evolve, it is essential to understand the psychosocial and technological factors that influence learners’ academic success. This study examined the predictive roles of academic grit, academic interest, and technological attitudes on learners’ performance in General Mathematics among 104 randomly selected Grade 11 STEM and ABM learners in a public school in Misamis Oriental. Using a descriptive-correlational design and both descriptive and inferential analyses, results revealed that learners demonstrated high levels of academic grit (M = 4.11, SD = 0.46) and positive technological attitudes (M = 3.98, SD = 0.53). In contrast, their academic interest was moderate (M = 3.42, SD = 0.51). Regression analysis revealed that academic grit (β = 0.247, p = .005) and technological attitude (β = 0.220, p = .020) were significant predictors of learners' mathematics performance, collectively explaining 9.8% of the variance (adjusted R² = 0.098). Conversely, academic interest did not significantly predict performance (p > .05). The findings suggest that perseverance and adaptability to digital tools contribute more strongly to academic achievement than interest alone. Hence, interventions that foster grit and promote purposeful technology integration may enhance learners’ mathematical proficiency and overall academic resilience.

Downloads

Download data is not yet available.

References

Acock, A. C. (2020). Data collection and analysis in sociology (5th ed.). Sage Publications. https://tinyurl.com/2amvzwmp

Aremu, B., & Adeoluwa, O. (2022). Assessing learners’ learning attitude and academic performance through M-Learning during the COVID-19 pandemic. Canadian Journal of Learning and Technology, 47(3). https://doi.org/10.21432/cjlt28085

Bernardo, A., Cordel ΙΙ, M., Lapinid, M. R., Teves, J. M., Yap, S., & Chua, U. (2022). Contrasting profiles of low-performing Mathematics students in public and private schools in the Philippines: Insights from machine learning. Journal of Intelligence, 10(3), 61. https://doi.org/10.3390/jintelligence10030061

Cai, J., Hwang, S., & Robison, V. (2019). Journal for research in Mathematics education: Practical guides for promoting and disseminating significant research in Mathematics education. ICME-13 Monographs, 425–442. https://doi.org/10.1007/978-3-030-15636-7_21

Clark, K. N., & Malecki, C. K. (2019). Academic grit scale: Psychometric properties and associations with achievement and life satisfaction. Journal of School Psychology, 72, 49–66. https://doi.org/10.1016/j.jsp.2018.12.001

Coristine, S., Russo, S., Fitzmorris, R., Beninato, P., & Rivolta, G. (2021). The importance of learner–teacher relationships. Classroom Practice in 2021. https://tinyurl.com/ysb76c63

Datu, J. A. D., Yuen, M., & Chen, G. (2017). Grit and determination: A review of literature with implications for theory and research. Journal of Psychologists and Counsellors in Schools, 27(2), 168-176. https://doi.org/10.1017/jgc.2016.2

de Vries, N., Meeter, M., & Huizinga, M. (2024). Does interest fit between learner and study program lead to better outcomes? A meta-analysis of vocational interest congruence as predictor for academic success. Educational Research Review, 44, 100619. https://doi.org/10.1016/j.edurev.2024.100619

Frenzel, A. C., Goetz, T., Lüdtke, O., Pekrun, R., & Sutton, R. E. (2009). Emotional transmission in the classroom: Exploring the relationship between teacher and learner enjoyment. Journal of Educational Psychology, 101(3), 705–716. https://doi.org/10.1037/a0014695

Karlen, Y., Suter, F., Hirt, C., & Merki, K. (2019). The role of implicit theories in learners’ grit, achievement goals, intrinsic and extrinsic motivation, and achievement in the context of a long-term challenging task. Learning and Individual Differences, 74, 101757. https://doi.org/10.1016/j.lindif.2019.101757

Liu, R. D., Zhen, R., Ding, Y., Liu, Y., Wang, J., Jiang, R., & Xu, L. (2018). Teacher support and Math engagement: Roles of academic self-efficacy and positive emotions. Educational Psychology, 38(1), 3–16. https://doi.org/10.1080/01443410.2017.1359238

Luo, Z., Dang, Y., & Xu, W. (2019). Academic interest scale for adolescents: Development, validation, and measurement invariance with Chinese learners. Frontiers in Psychology, 10, 2301. https://doi.org/10.3389/fpsyg.2019.02301

Masigan, A. J. (2021, October 6). Our education crisis. Why and what next? (Part 1). philstar.com. https://tinyurl.com/34u6p2p9

Muenks, K., Wigfield, A., & Eccles, J. S. (2020). I can do this! The development and calibration of children’s expectations for success and competence beliefs. Developmental Review, 48(2), 24–39. https://doi.org/10.1016/j.dr.2018.04.001

OECD. (2019). PISA 2018 results: What learners know and can do (Vol. 1). OECD Publishing. https://doi.org/10.1787/5f07c754-en

Park, D., Tsukayama, E., Yu, A., & Duckworth, A. L. (2020). The development of grit and growth mindset during adolescence. Journal of Experimental Child Psychology, 198, 104889. https://doi.org/10.1016/j.jecp.2020.104889

Rahman, A., Zaid, N., Aris, B., Abdullah, Z., Mohamed, H., Van, H., & Meijden, D. (2016, October). Implementation strategy of project-based learning through flipped classroom method. In 2016 IEEE Conference on E-Learning, E-Management and E-Services (IC3e) (pp. 1–5). IEEE. https://tinyurl.com/ms46c9dc

Reinhold, F., Strohmaier, A., Finger-Collazos, Z., & Reiss, K. (2021). Considering teachers’ beliefs, motivation, and emotions regarding teaching Mathematics with digital tools: The effect of an in-service teacher training. Front. Educ. 6:723869. https://doi.org/10.3389/feduc.2021.723869

Rosli, M. S., Saleh, N. S., & Omar, M. F. (2020, April). Technology-assisted cognitive augmentation: An OLE prototype to nurture cognitive skills in Chemistry. In AIP Conference Proceedings (Vol. 2215, No. 1, p. 020021). AIP Publishing LLC. https://doi.org/10.1063/5.0000590

Shaffer, P., & Stern, J. (2021). Making Mathematics relevant for learners: The role of real-world applications. International Journal of STEM Education, 8(1), 1–12. https://doi.org/10.1186/s40594-021-00296-1

Schiefele, U., Streblow, L., & Retelsdorf, J. (2013). Dimensions of teacher interest and their relations to occupational well-being and instructional practices. Journal for Educational Research Online, 5(1), 7–37. https://tinyurl.com/yuvf6pxp

Shin, M., & Bolkan, S. (2021). Intellectually stimulating learners’ intrinsic motivation: The mediating influence of learner engagement, self-efficacy, and learner academic support. Communication Education, 70(2), 146-164. https://doi.org/10.1080/03634523.2020.1828959

Tang, X., Wang, M. T., Guo, J., & Salmela-Aro, K. (2021). Building grit: The longitudinal pathways between mindset, self-regulation, and achievement in high school learners. Journal of Youth and Adolescence, 50(5), 973–985. https://doi.org/10.1007/s10964-019-00998-0

Taimalu, M., & Luik, P. (2019). The impact of beliefs and knowledge on the integration of information and communication technology into classroom practice. Computers & Education, 135, 20–32. https://doi.org/10.1016/j.tate.2018.12.012

Tariq, A., Mohyuddin, R., & Ismaeel, G. (2025). Building bridges to success: The role of teacher-learner relationships in academic achievement. Review of Education, Administration & Law, 8(1), 125–135. https://doi.org/10.47067/real.v8i1.411

Triplett, W. J. (2023). Impact of technology integration in STEM education. Cybersecurity and Innovative Technology Journal, 1(1), 16–22. https://doi.org/10.53889/citj.v1i1.295

Wu, X.-Y. (2023). Exploring the effects of digital technology on deep learning: A meta-analysis. Education and Information Technologies, 29(3). https://doi.org/10.1007/s10639-023-12307-1

Yildiz, B. (2021). The effects of technology-mediated flipped classroom on learners’ Mathematics achievement and engagement. Educational Technology Research and Development, 69(6), 3219–3238. https://doi.org/10.1007/s11423-021-10008-4

Downloads

Published

2025-11-25

How to Cite

Verson, J. M., & Potane, J. (2025). Academic Grit, Interest, and Technological Attitudes as Predictors of Learners’ Performance. Journal of Interdisciplinary Perspectives, 3(12), 245–253. https://doi.org/10.69569/jip.2025.699