Assessing Students' Engagement with Technology Tools in Biology Learning among General Science Undergraduate Students
Authors
School of Graduate Studies, Saint Mary’s University (Philippines)
Article Information
DOI: 10.47772/IJRISS.2026.100800223
Subject Category: Education
Volume/Issue: 10/8 | Page No: 3271-3285
Publication Timeline
Submitted: 2026-08-14
Accepted: 2026-08-19
Published: 2026-08-31
Abstract
This study examined the engagement of General Science undergraduate students with technology tools in learning Biology at Nueva Vizcaya State University Bambang Campus. It aimed to determine the students’ level of engagement and identify whether gender, preferred learning style, and monthly household income were associated with or could predict their engagement with technology tools. A quantitative descriptive-correlational research design was used, involving 63 General Science undergraduate students. Data were collected through a structured questionnaire and analyzed using appropriate statistical tools. The findings showed that the students had a high level of engagement with technology tools in learning Biology, with an overall mean score of 2.93 (SD = 0.54). The results also showed no significant difference in engagement according to gender or preferred learning style. Similarly, monthly household income was not significantly related to students’ engagement. Furthermore, multiple regression analysis showed that gender, preferred learning style, and monthly household income did not significantly predict overall engagement. Although these variables collectively explained 12.1% of the variation in students’ engagement, the overall regression model was not statistically significant. Overall, the findings indicate that students generally demonstrated high engagement with technology tools in Biology learning regardless of their gender, preferred learning style, or monthly household income. The findings also suggest that other factors not included in the present study may contribute to students’ engagement with technology tools in Biology learning.
Keywords
Technology Tools; Student Engagement; Biology Learning; Learning Styles; Household Income
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References
1. Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., Oxley, E., & others. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, 63. https://doi.org/10.1186/s41239-024-00493-y [Google Scholar] [Crossref]
2. Bilici, S., & Yilmaz, R. M. (2024). The effects of using collaborative digital storytelling on academic achievement and skill development in biology education. Education and Information Technologies, 29, 20243–20266. https://doi.org/10.1007/s10639-024-12638-7 [Google Scholar] [Crossref]
3. Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17, 2. https://doi.org/10.1186/s41239-019-0176-8 [Google Scholar] [Crossref]
4. Byukusenge, C., Nsanganwimana, F., & Tarmo, A. P. (2022). Effectiveness of virtual laboratories in teaching and learning biology: A review of literature. International Journal of Learning, Teaching and Educational Research, 21(6), 1–17. https://doi.org/10.26803/ijlter.21.6.1 [Google Scholar] [Crossref]
5. Chen, O., Paas, F., & Sweller, J. (2023). A cognitive load theory approach to defining and measuring task complexity through element interactivity. Educational Psychology Review, 35, 63. https://doi.org/10.1007/s10648-023-09782-w [Google Scholar] [Crossref]
6. Choi-Lundberg, D., et al. (2023). A systematic review of digital innovations in technology-enhanced learning designs in higher education. Australasian Journal of Educational Technology, 39(3), 133–162. https://doi.org/10.14742/AJET.7615 [Google Scholar] [Crossref]
7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]
8. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer. https://doi.org/10.1007/978-1-4899-2271-7 [Google Scholar] [Crossref]
9. Forsström, S., Njå, M., Munthe, E., Álvarez-Galván, J.-L., & Houldsworth, L. (2025). The impact of digital technologies on students’ learning: Results from a literature review (OECD Education Working Papers No. 335). OECD Publishing. https://doi.org/10.1787/9997e7b3-en [Google Scholar] [Crossref]
10. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059 [Google Scholar] [Crossref]
11. Godsk, M., & Møller, K. L. (2025). Engaging students in higher education with educational technology. Education and Information Technologies, 30, 2941–2976. https://doi.org/10.1007/s10639-024-12901-x [Google Scholar] [Crossref]
12. Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864 [Google Scholar] [Crossref]
13. Heilporn, G., Raynault, A., & Frenette, É. (2024). Student engagement in a higher education course: A multidimensional scale for different course modalities. Social Sciences & Humanities Open, 9, 100794. https://doi.org/10.1016/j.ssaho.2023.100794 [Google Scholar] [Crossref]
14. Kearsley, G., & Shneiderman, B. (1998). Engagement theory: A framework for technology-based teaching and learning. Educational Technology, 38(5), 20–23. [Google Scholar] [Crossref]
15. Mayer, R. E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36, 8. https://doi.org/10.1007/s10648-023-09842-1 [Google Scholar] [Crossref]
16. Mutlu-Bayraktar, D., Cosgun, V., & Altan, T. (2019). Cognitive load in multimedia learning environments: A systematic review. Computers & Education, 141, 103618. https://doi.org/10.1016/j.compedu.2019.103618 [Google Scholar] [Crossref]
17. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x [Google Scholar] [Crossref]
18. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860 [Google Scholar] [Crossref]
19. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 [Google Scholar] [Crossref]
20. UNESCO. (2023). Global education monitoring report 2023: Technology in education: A tool on whose terms? UNESCO. [Google Scholar] [Crossref]
21. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]
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