Research Article
Analyzing the effect of ICT engagement on academic performance: A PLS-SEM approach mediated by intrinsic motivation
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1 Professional School of Business Administration, Universidad Autónoma del Perú, Lima, PERU2 Research, Innovation and Sustainability Department, Universidad Privada del Norte, Lima, Peru* Corresponding Author
Contemporary Educational Technology, 18(3), 2026, ep663, https://doi.org/10.30935/cedtech/18690
Published: 30 May 2026
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ABSTRACT
This study examines how engagement with information and communication technologies (ICT) relates to university students’ academic performance, considering intrinsic motivation as a mediator and platform usability as an enabling condition. A quantitative, cross-sectional, correlational, and descriptive design was implemented with 385 students from a university in Lima (Peru), selected through non-probability convenience sampling. Data were collected using a structured 5-point Likert questionnaire measuring platform usability, ICT engagement, intrinsic and extrinsic motivation. Academic performance was operationalized as self-reported semester GPA using the national 0-20 grading scale. The model was tested using PLS-SEM (SmartPLS 4) with a bootstrapping procedure of 5,000 subsamples. Results indicated a strong association between platform usability and ICT engagement (β = 0.78). ICT engagement showed positive effects on intrinsic motivation (β = 0.64) and extrinsic motivation (β = 0.42). Intrinsic motivation was positively related to academic performance (β = 0.55) and mediated the relationship between ICT engagement and performance (indirect effect = 0.35). Platform usability also exerted an indirect effect on performance through engagement and intrinsic motivation (0.43). Overall, the findings suggest that ICT engagement translates into better academic outcomes primarily when digital platforms are usable and instructional strategies foster intrinsic motivation and autonomous learning.
CITATION (APA)
Olórtegui-Alcalde, L. M., & Cordova-Buiza, F. (2026). Analyzing the effect of ICT engagement on academic performance: A PLS-SEM approach mediated by intrinsic motivation. Contemporary Educational Technology, 18(3), ep663. https://doi.org/10.30935/cedtech/18690
REFERENCES
- Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26, 7205-7224. https://doi.org/10.1007/s10639-021-10573-5
- Al-Fraihat, D., Alshahrani, A. M., Alzaidi, M., Shaikh, A. A., Al-Obeidallah, M., & Al-Okaily, M. (2025). Exploring students’ perceptions of the design and use of the Moodle learning management system. Computers in Human Behavior Reports, 18, Article 100685. https://doi.org/10.1016/j.chbr.2025.100685
- Almarghani, E. M., & Mijatovic, I. (2017). Factors affecting student engagement in HEIs—It is all about good teaching. Teaching in Higher Education, 22(8), 940-956. https://doi.org/10.1080/13562517.2017.1319808
- Anthonysamy, L., Koo, A. C., & Hew, S. H. (2020). Self-regulated learning strategies in higher education: Fostering digital literacy for sustainable lifelong learning. Education and Information Technologies, 25(4), 2393-2414. https://doi.org/10.1007/s10639-020-10201-8
- Banihashem, S. K., Noroozi, O., Van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, Article 100489. https://doi.org/10.1016/j.edurev.2022.100489
- Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, Article 63. https://doi.org/10.1186/s41239-024-00493-y
- Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13. https://doi.org/10.1016/j.iheduc.2015.04.007
- Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 2, Article 100027. https://doi.org/10.1016/j.caeai.2021.100027
- 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(1), S14-S30. https://doi.org/10.1080/15391523.2021.1891998
- 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
- de Vreugd, L., van Leeuwen, A., & van der Schaaf, M. (2025). Students’ use of a learning analytics dashboard and influence of reference frames: Goal setting, motivation, and performance. Journal of Computer Assisted Learning, 41, Article e70015. https://doi.org/10.1111/jcal.70015
- Deci, E. L., & Ryan, R. M. (2016). Optimizing students’ motivation in the era of testing and pressure: A self-determination theory perspective. In W. Liu, J. Wang, & R. M. Ryan (Eds.), Building autonomous learners (pp. 9-29). Springer. https://doi.org/10.1007/978-981-287-630-0_2
- Dobashi, K., Ho, C. P., Fulford, C. P., Lin, M.-F., & Higa, C. (2022). Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section. Computers and Education: Artificial Intelligence, 3, Article 100105. https://doi.org/10.1016/j.caeai.2022.100105
- Drugova, E., Zhuravleva, I., Zakharova, U., & Latipov, A. (2024). Learning analytics driven improvements in learning design in higher education: A systematic literature review. Journal of Computer Assisted Learning, 40(2), 510-524. https://doi.org/10.1111/jcal.12894
- Emerson, A., Cloude, E. B., Azevedo, R., & Lester, J. (2020). Multimodal learning analytics for game-based learning. British Journal of Educational Technology, 51(5), 1505-1526. https://doi.org/10.1111/bjet.12992
- Fang, X., & Chiu, T. K. F. (2025). Using self-determination theory to explain how mind mapping and real-time commenting enhance student engagement and learning outcomes in video creation. Computers and Education Open, 8, Article 100254. https://doi.org/10.1016/j.caeo.2025.100254
- Flanagan, B., Majumdar, R., & Ogata, H. (2022). Early-warning prediction of student performance and engagement in open book assessment by reading behavior analysis. International Journal of Educational Technology in Higher Education, 19, Article 41. https://doi.org/10.1186/s41239-022-00348-4
- Fuentes, R. P., & LaBad, R. B. (2025). Impact of digital learning tools on student engagement and academic achievement in higher education: A systematic review. Ennoia Advances in Social Science, Technology and Education, 1(2), 53-73. https://doi.org/10.5281/zenodo.16430142
- Gallagher, T., Slof, B., van der Schaaf, M., Toyoda, R., Tehreem, Y., Garcia Fracaro, S., & Kester, L. (2024). Reference frames for learning analytics dashboards: The progress and social reference frame and occupational self-efficacy. Journal of Computer Assisted Learning, 40(2), 742-760. https://doi.org/10.1111/jcal.12912
- Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105. https://doi.org/10.1016/j.iheduc.2004.02.001
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
- Hasan, N., & Bao, Y. (2020). Impact of “e-Learning crack-up” perception on psychological distress among college students during COVID-19 pandemic: A mediating role of “fear of academic year loss.” Children and Youth Services Review, 118, Article 105355. https://doi.org/10.1016/j.childyouth.2020.105355
- Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53. https://doi.org/10.1016/j.compedu.2015.09.005
- Herodotou, C., Maguire, C., McDowell, N., Hlosta, M., & Boroowa, A. (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, Article 104285. https://doi.org/10.1016/j.compedu.2021.104285
- Huang, Y., & Wang, S. (2023). How to motivate student engagement in emergency online learning? Evidence from the COVID-19 situation. Higher Education, 85, 1101-1123. https://doi.org/10.1007/s10734-022-00880-2
- Instituto Nacional de Estadística e Informática [INEI] (2022). Acceso y uso de tecnologías en educación superior en el Perú [Access to and use of technologies in higher education in Peru]. National Institute of Statistics and Informatics. https://www.inei.gob.pe/media/MenuRecursivo/boletines/boletin_tic.pdf
- Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students’ learning performance: A systematic review. Sustainability, 15(10), Article 7849. https://doi.org/10.3390/su15107849
- Jung, Y., & Wise, A. F. (2025). How students engage with learning analytics: Access, action-taking, and learning routines with message-based information to support collaborative annotation. Computers & Education, 232, Article 105280. https://doi.org/10.1016/j.compedu.2025.105280
- Kannan, V., Kuromiya, H., Gouripeddi, S., Majumdar, R., Warriem, J., & Ogata, H. (2020). Flip & pair–A strategy to augment a blended course with active-learning components: Effects on engagement and learning. Smart Learning Environments, 7, Article 34. https://doi.org/10.1186/s40561-020-00138-3
- Kebritchi, M., Lipschuetz, A., & Santiague, L. (2017). Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems, 46(1), 4-29. https://doi.org/10.1177/0047239516661713
- Korsah, D. P. (2024). Adoption and utilization of Moodle learning management system for emergency remote teaching: A UTAUT perspective. Educational Point, 1(2), Article e111. https://doi.org/10.71176/edup/15730
- Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594-2618. https://doi.org/10.1111/bjet.12720
- Lee, J., & Kim, D. (2025). From awareness to empowerment: Self-determination theory-informed learning analytics dashboards to enhance student engagement in asynchronous online courses. Journal of Computing in Higher Education, 37, 1078-1118. https://doi.org/10.1007/s12528-024-09416-2
- Lee, J.-E., & Recker, M. (2021). The effects of instructors’ use of online discussions strategies on student participation and performance in university online introductory mathematics courses. Computers & Education, 162, Article 104084. https://doi.org/10.1016/j.compedu.2020.104084
- Li, T., Fan, Y., Tan, Y., Wang, Y., Singh, S., Li, X., Rakovic, M., van der Graf, J., Lim, L., Yang, B., Molenaar, I., Bannert, M., Moore, J., Swiecki, Z., Tsai, Y.-S., Shaffer, D. W., & Gasevic, D. (2023). Analytics of self-regulated learning scaffolding: Effects on learning processes. Frontiers in Psychology, 14, Article 1206696. https://doi.org/10.3389/fpsyg.2023.1206696
- Li, Y., Hew, K. F., & Du, J. (2024). Gamification enhances student intrinsic motivation, perceptions of autonomy and relatedness, but minimal impact on competency: A meta-analysis and systematic review. Educational Technology Research and Development, 72, 765-796. https://doi.org/10.1007/s11423-023-10337-7
- Lin, L., King, R. B., Fu, L., & Leung, S. O. (2024). Information and communication technology engagement and digital reading: How meta-cognitive strategies impact their relationship. British Journal of Educational Technology, 55(1), 277-296. https://doi.org/10.1111/bjet.13355
- Linden, K., van der Ploeg, N., & Roman, N. (2023). Explainable learning analytics to identify disengaged students early in semester: An intervention supporting widening participation. Journal of Higher Education Policy and Management, 45(6), 626-640. https://doi.org/10.1080/1360080X.2023.2212418
- Machuca-Vílchez, J. A., Ramos-Cavero, M. J., & Cordova-Buiza, F. (2023). Knowledge management in financial education in Peruvian government programs focused on women: Progress and challenges. Knowledge and Performance Management, 7(1), 1-14. https://doi.org/10.21511/kpm.07(1).2023.01
- Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205-222. https://doi.org/10.24059/olj.v22i1.1092
- Navarro-Ibarra, L., Cuevas-Salazar, O., & Robles-Aguilar, A. (2023). Knowledge gaps in education and ICT: A literature review in open access publications. Contemporary Educational Technology, 15(4), Article ep480. https://doi.org/10.30935/cedtech/13770
- OECD. (2022). Education at a glance: OECD indicators. OECD Publishing. https://doi.org/10.1787/3197152b-en
- Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18, Article 46. https://doi.org/10.1186/s41239-021-00284-9
- Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
- Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32(1), 77-112. https://doi.org/10.1007/s10648-019-09498-w
- Sakhieva, R. G., Grishnova, E. E., Zhukova, M. A., Sokolova, E. G., Lapidus, N. I., & Khlusyanov, O. V. (2025). Use and adoption of ICTs oriented to university student learning. Contemporary Educational Technology, 17(4), Article ep604. https://doi.org/10.30935/cedtech/17429
- Salazar-Rebaza, C., Zegarra-Alva, M., & Cordova-Buiza, F. (2023). Academic management of university virtual education: An analysis from the perception of students, teachers, and managers. Problems and Perspectives in Management, 21(4), 532-544. https://doi.org/10.21511/ppm.21(4).2023.40
- Salazar-Rebaza, C., Zegarra-Alva, M., Cordova-Buiza, F. (2022). Management and leadership in university education: Approaches and perspectives. Problems and Perspectives in Management, 20(3), 130-141. https://doi.org/10.21511/ppm.20(3).2022.11
- Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26, 1632-1640. https://doi.org/10.1016/j.chb.2010.06.011
- Sanhueza, M., Sandoval, L., Ormazabal, M., & Zúñiga, M. (2025). Effect of a teacher training program with ICT on university students’ learning. Contemporary Educational Technology, 17(1), Article ep556. https://doi.org/10.30935/cedtech/15745
- Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: A critical review of the literature. International Journal of Educational Technology in Higher Education, 4(1), Article 25. https://doi.org/10.1186/s41239-017-0063-0
- Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
- Tepgec, M., Heil, J., & Ifenthaler, D. (2025). Feedback literacy matters: Unlocking the potential of learning analytics-based feedback. Assessment & Evaluation in Higher Education, 50(1), 50-66. https://doi.org/10.1080/02602938.2024.2367587
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
- Wang, F., Yin, H., & King, R. B. (2025). Profiling motivation and engagement in online learning: A multilevel latent profile analysis of students and institutions. Computers & Education, 227, Article 105209. https://doi.org/10.1016/j.compedu.2024.105209
- Zegarra-Alva, M., Castañeda-Gil, Y., & Cordova-Buiza, F. (2024). Evaluation of specific competencies of university students in hospitality and gastronomy programs. Knowledge and Performance Management, 8(1), 149-162. https://doi.org/10.21511/kpm.08(1).2024.11
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