Technology-Based Learning, Academic Motivation, and Performance of Students in Literature: Basis for Intervention

This study examined the relationship between technology-based learning, academic motivation, and academic performance in Literature among Grade 12 Academic Track learners enrolled in a public senior high school in a division component city in Northern Negros Occidental during School Year 2026-2027. Anchored on the Technology Acceptance Model, Self-Determination Theory, and Social Cognitive Theory, the study employed a quantitative descriptive-correlational research design. Using proportionate stratified sampling and Slovin’s formula, 198 learners were selected from a population of 393. Data were collected through a researcher-made and validated four-point Likert-scale questionnaire and analyzed using frequency, percentage, weighted mean, Spearman Rank-Order Correlation, Kruskal-Wallis H test, and effect size measures. Results revealed high levels of technology-based learning (WM = 2.85) and academic motivation (WM = 2.78), but low academic performance in Literature (WM = 1.81). Technology-based learning showed a weak and insignificant relationship with academic performance (rs = 0.103, p = 0.150), while academic motivation demonstrated a significant but very weak positive relationship (rs = 0.142, p = 0.045). Although differences in performance were observed based on sex, the effect size indicated limited practical significance. Overall, the findings suggest that high levels of technology use and academic motivation do not necessarily translate into improved academic performance in Literature.

technology-based learning, academic motivation, academic performance in literature, descriptive-correlational research, Self-Determination Theory, Technology Acceptance Model, senior high school

1. A. Books [Google Scholar] [Crossref]

2. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. [Google Scholar] [Crossref]

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

4. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. [Google Scholar] [Crossref]

5. Lohr, S. L. (2021). Sampling: Design and analysis (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429298899 [Google Scholar] [Crossref]

6. Wong, Z. Y., & Liem, G. A. D. (2023). Cross-cultural differences in motivation: Endorsement and influences of achievement goals on academic achievement. In W. O. Lee, P. Brown, A. L. Goodwin, & A. Green (Eds.), International handbook on education development in Asia-Pacific (pp. 1-27). Springer. https://doi.org/10.1007/978-981-16-2327-1_46-1 [Google Scholar] [Crossref]

7. B. Journals / Magazine / Articles [Google Scholar] [Crossref]

8. Bi, J., Izadpanah, S., Mohammadi, Z., & Mohammad Rezaei, Y. (2024). Investigating the impact of technology-based education on academic motivation, academic perseverance, and academic self-efficacy in English language learning skills. Education and Information Technologies, 29, 20523-20545. https://doi.org/10.1007/s10639-024-12712-0 [Google Scholar] [Crossref]

9. Chazan, D. J., Pelletier, G. N., & Daniels, L. M. (2022). Achievement Goal Theory review: An application to school psychology. Canadian Journal of School Psychology, 37(1), 40-56. https://doi.org/10.1177/08295735211058319 [Google Scholar] [Crossref]

10. Chen, C.-C., & Tu, H.-Y. (2021). The effect of digital game-based learning on learning motivation and performance under Social Cognitive Theory and entrepreneurial thinking. Frontiers in Psychology, 12, 750711. https://doi.org/10.3389/fpsyg.2021.750711 [Google Scholar] [Crossref]

11. Cheung, S. K. S., Kwok, L. F., Phusavat, K., & Yang, H. H. (2021). Shaping the future learning environments with smart elements: Challenges and opportunities. International Journal of Educational Technology in Higher Education, 18(1), 16. https://doi.org/10.1186/s41239-021-00254-1 [Google Scholar] [Crossref]

12. Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14-S30. https://doi.org/10.1080/15391523.2021.1891998 [Google Scholar] [Crossref]

13. Corbett, F., & Spinello, E. (2020). Connectivism and leadership: Harnessing a learning theory for the digital age to redefine leadership in the twenty-first century. Heliyon, 6(1), e03250. https://doi.org/10.1016/j.heliyon.2020.e03250 [Google Scholar] [Crossref]

14. 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]

15. Engeness, I. (2021). Developing teachers' digital identity: Towards the pedagogic design principles of digital environments to enhance students' learning in the 21st century. European Journal of Teacher Education, 44(1), 96-114. https://doi.org/10.1080/02619768.2020.1849129 [Google Scholar] [Crossref]

16. Fokkens-Bruinsma, M., Van Rooij, E. C. M., & Canrinus, E. T. (2020). Perceived classroom goal structures as predictors of students' personal goals. Teachers and Teaching, 26(1), 88-102. https://doi.org/10.1080/13540602.2020.1740195 [Google Scholar] [Crossref]

17. Freeman, K. E., Winston-Proctor, C. E., Gangloff-Bailey, F., & Jones, J. M. (2021). Racial identity-rooted academic motivation of first-year African American students majoring in STEM at an HBCU. Frontiers in Psychology, 12, 669407. https://doi.org/10.3389/fpsyg.2021.669407 [Google Scholar] [Crossref]

18. Fuchs, K. (2022). Using an extended technology acceptance model to determine students' behavioral intentions toward smartphone technology in the classroom. Frontiers in Education, 7, 972338. https://doi.org/10.3389/feduc.2022.972338 [Google Scholar] [Crossref]

19. Gopinathan, S., Kaur, A. H., Veeraya, S., & Raman, M. (2022). The role of digital collaboration in student engagement towards enhancing student participation during COVID-19. Sustainability, 14(11), 6844. https://doi.org/10.3390/su14116844 [Google Scholar] [Crossref]

20. Habok, A., Oo, T. Z., & Magyar, A. (2024). The effect of reading strategy use on online reading comprehension. Heliyon, 10(2), e24281. https://doi.org/10.1016/j.heliyon.2024.e24281 [Google Scholar] [Crossref]

21. Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004 [Google Scholar] [Crossref]

22. Honicke, T., Broadbent, J., & Fuller-Tyszkiewicz, M. (2023). The self-efficacy and academic performance reciprocal relationship: The influence of task difficulty and baseline achievement on learner trajectory. Higher Education Research & Development, 42(8), 1936-1953. https://doi.org/10.1080/07294360.2023.2197194 [Google Scholar] [Crossref]

23. Howard, J. L., Bureau, J., Guay, F., Chong, J. X. Y., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta-analysis from self-determination theory. Perspectives on Psychological Science, 16(6), 1300-1323. https://doi.org/10.1177/1745691620966789 [Google Scholar] [Crossref]

24. James, J. L. (2022). Students as stakeholders: Understanding expectations can increase student retention. Journal of College Student Retention: Research, Theory & Practice, 24(1), 20-42. https://doi.org/10.1177/1521025119898844 [Google Scholar] [Crossref]

25. Kus, M. (2025). A meta-analysis of the impact of technology related factors on students' academic performance. Frontiers in Psychology, 16, 1524645. https://doi.org/10.3389/fpsyg.2025.1524645 [Google Scholar] [Crossref]

26. Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267 [Google Scholar] [Crossref]

27. Li, C., Jiang, X., He, H., & Zhang, X. (2022). The influence of performance-contingent rewards on proactive and responsive creativity: Dual-path mediating effects of work motivation. Frontiers in Psychology, 13, 812298. https://doi.org/10.3389/fpsyg.2022.812298 [Google Scholar] [Crossref]

28. Li, X., Bergin, C., & Olsen, A. A. (2022). Positive teacher-student relationships may lead to better teaching. Learning and Instruction, 80, 101581. https://doi.org/10.1016/j.learninstruc.2022.101581 [Google Scholar] [Crossref]

29. Liu, J., Peng, P., Zhao, B., & Luo, L. (2022). Socioeconomic status and academic achievement in primary and secondary education: A meta-analytic review. Educational Psychology Review, 34(4), 2867-2896. https://doi.org/10.1007/s10648-022-09689-y [Google Scholar] [Crossref]

30. Martin, F., Wu, T., Wan, L., & Xie, K. (2022). A meta-analysis on the Community of Inquiry presences and learning outcomes in online and blended learning environments. Online Learning, 26(1), 325-359. https://doi.org/10.24059/olj.v26i1.2604 [Google Scholar] [Crossref]

31. Mendoza, N. B., Yan, Z., & King, R. B. (2023). Supporting students' intrinsic motivation for online learning tasks: The effect of need-supportive task instructions on motivation, self-assessment, and task performance. Computers & Education, 193, 104663. https://doi.org/10.1016/j.compedu.2022.104663 [Google Scholar] [Crossref]

32. Murphy, A., & Arciuli, J. (2024). Digital reading comprehension instruction in English for children with English as an additional language: A systematic review. Journal of Research in Reading, 47(3), 348-394. https://doi.org/10.1111/1467-9817.12448 [Google Scholar] [Crossref]

33. Nguyen, L. T., Kanjug, I., Lowatcharin, G., Manakul, T., Poonpon, K., Sarakorn, W., Somabut, A., Srisawasdi, N., Traiyarach, S., & Tuamsuk, K. (2022). How teachers manage their classroom in the digital learning environment: Experiences from the University Smart Learning Project. Heliyon, 8(10), e10817. https://doi.org/10.1016/j.heliyon.2022.e10817 [Google Scholar] [Crossref]

34. Ramos, E., & Magallanes, C. I. (2021). Social support and academic motivation of students at risk of dropping out. Philippine Social Science Journal, 4(2), 43-52. https://doi.org/10.52006/main.v4i2.357 [Google Scholar] [Crossref]

35. Ramos, M., De Sixte, R., Janez, A., & Rosales, J. (2022). Academic motivation at early ages: Spanish validation of the Elementary School Motivation Scale (ESMS-E). Frontiers in Psychology, 13, 980434. https://doi.org/10.3389/fpsyg.2022.980434 [Google Scholar] [Crossref]

36. Rudas, M. L. (2024). Measuring reading proficiency level of Senior High School students: Towards the development of a reading comprehension skills test. UNIVERSITAS: The Official Journal of University of Makati, 12(2). [Google Scholar] [Crossref]

37. Sezgin, S., & Firat, M. (2024). Exploring the digital divide in open education: A comparative analysis of undergraduate students. The International Review of Research in Open and Distributed Learning, 25(1), 109-126. https://doi.org/10.19173/irrodl.v25i1.7236 [Google Scholar] [Crossref]

38. Taherdoost, H. (2022). Designing a questionnaire for a research paper: A comprehensive guide to design and develop an effective questionnaire. Asian Journal of Managerial Science, 11(1), 8-16. https://doi.org/10.51983/ajms-2022.11.1.3087 [Google Scholar] [Crossref]

39. Thornberg, R., Forsberg, C., Hammar Chiriac, E., & Bjereld, Y. (2022). Teacher-student relationship quality and student engagement: A sequential explanatory mixed-methods study. Research Papers in Education, 37(6), 840-859. https://doi.org/10.1080/02671522.2020.1864772 [Google Scholar] [Crossref]

40. Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Mones, A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695-6726. https://doi.org/10.1007/s10639-022-11431-8 [Google Scholar] [Crossref]

41. Uslu, F. (2022). Number of response options, reliability, validity, and potential bias in the use of the Likert scale in education and social science research: A literature review. International Journal of Educational Methodology, 8(4), 625-637. https://doi.org/10.12973/ijem.8.4.625 [Google Scholar] [Crossref]

42. Yusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. Education in Medicine Journal, 11(2), 49-54. https://doi.org/10.21315/eimj2019.11.2.6 [Google Scholar] [Crossref]

43. Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students' intention to use an online education platform at a university in China. SAGE Open, 12(1). https://doi.org/10.1177/21582440221085259 [Google Scholar] [Crossref]

44. C. Online Sources [Google Scholar] [Crossref]

45. Kanda Data. (2024, December 21). How to determine sample size using proportionate stratified random sampling. https://kandadata.com/how-to-determine-samples-size-using-proportionate-stratified-random-sampling/ [Google Scholar] [Crossref]

46. Organisation for Economic Co-operation and Development. (2023). PISA 2022 results (Volume I and II): Country notes, Philippines. OECD Publishing. https://www.oecd.org/en/publications/2023/11/pisa-2022-results-volume-i-and-ii-country-notes_2fca04b9/philippines_dbf92b65.html [Google Scholar] [Crossref]

47. Philippine Statistics Authority. (2023). Family Income and Expenditure Survey: Income classification of Philippine families. https://psa.gov.ph [Google Scholar] [Crossref]

48. Sciencing. (2022, March 24). Slovin's formula sampling techniques. https://www.sciencing.com/slovins-formula-sampling-techniques-5475547/ [Google Scholar] [Crossref]

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