E-Learning Readiness and Academic Adaptability among Higher Education Students
DOI:
https://doi.org/10.63345/Keywords:
E-learning readiness, academic adaptability, higher education, artificial intelligence, learning analytics, self-regulated learningAbstract
E-learning in higher education has evolved from an emergency instructional alternative into a persistent ecosystem integrating learning management systems, virtual assessment, adaptive platforms, analytics, and generative artificial intelligence. This transformation has shifted the meaning of student readiness from simple access to digital devices toward a multidimensional capability involving technological competence, self-regulation, communication, motivation, AI literacy, and responsible technology use. Existing research has extensively examined online learning readiness, yet comparatively limited attention has been given to whether students who report being digitally prepared can actually adapt their academic strategies when learning conditions, assessment formats, technological interfaces, and cognitive demands change. The present study addresses this gap by conceptualizing Readiness–Adaptability Alignment, defined as the degree to which students' e-learning preparedness translates into observable academic adjustment across changing digital learning conditions. The proposed framework distinguishes technological readiness, autonomous-learning readiness, interaction readiness, motivational readiness, and AI-assisted learning readiness while conceptualizing academic adaptability through strategic adjustment, workload regulation, help-seeking flexibility, assessment adaptation, and persistence.
References
OECD. OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. Paris, France: OECD Publishing, 2023. doi: 10.1787/c74f03de-en.
Miao, F., and Holmes, W. Guidance for Generative AI in Education and Research. Paris, France: UNESCO, 2023. doi: 10.54675/EWZM9535.
Wagiran, Suharjana, Nurtanto, M., Mutohhari, F., et al. “Determining the e-learning readiness of higher education students: A study during the COVID-19 pandemic.” Heliyon, vol. 8, no. 10, e11160, 2022. doi: 10.1016/j.heliyon.2022.e11160.
Hoang, D. T. N., and Hoang, T. “Ready or not? An exploration of university students’ online learning readiness and intention to use during COVID-19 pandemic.” International Journal of Educational Technology in Higher Education, 2023. doi: 10.1177/20427530221117330.
Lin, X., and Dai, Y. “An exploratory study of the effect of online learning readiness on self-regulated learning.” International Journal of Chinese Education, vol. 11, no. 2, 2022. doi: 10.1177/2212585X221111938.
Dai, Y., Luan, L., and Lin, X. “The effects of online learning readiness on self-regulated learning for the first-time online learning students.” Asian Journal of Distance Education, vol. 18, no. 2, pp. 42–62, 2023. Available: https://asianjde.com/ojs/index.php/AsianJDE/article/view/719
Li, Z., Li, S., Lou, X., Chen, M., Lv, C., Song, S., and Li, L. “Students’ online learning adaptability and their continuous usage intention across different disciplines.” Humanities and Social Sciences Communications, vol. 10, 2023. doi: 10.1057/s41599-023-02376-5.
Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., and Spirgi, L. “First-year students’ AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education.” International Journal of Educational Technology in Higher Education, vol. 21, article 18, 2024. doi: 10.1186/s41239-024-00452-7.
Strzelecki, A. “Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology.” Innovative Higher Education, vol. 49, pp. 223–245, 2024. doi: 10.1007/s10755-023-09686-1.
Rets, I., Herodotou, C., and Gillespie, A. “Six practical recommendations enabling ethical use of predictive learning analytics in distance education.” Journal of Learning Analytics, vol. 10, no. 1, pp. 149–167, 2023. doi: 10.18608/jla.2023.7743.
