Nuwanthika, W. A. N.Thathsarani, U.S.2026-01-112025-10-10978-624-6010-14-02783 – 8862https://rda.sliit.lk/handle/123456789/4512The rapid shift to virtual learning settings has unveiled disparities in preparedness, involvement, and scholarly performance among Sri Lankan government university undergraduate students. This study investigates the impact of Digital Learning Readiness (DLR), Teacher Support (TS), Perceived Usefulness (PU), and Motivation (MDL) on Student Engagement (ENG) and Academic Performance (AP). The general aim is to develop and validate a structural model that explains the mechanisms by which psychological and environmental factors lead to academic performance in online learning contexts. Quantitative research design was employed. A standardized questionnaire was completed by 301 undergraduate students sampled through simple random sampling across ten government universities. Data were analyzed using Structural Equation Modeling (SEM) supplemented by Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Reliability and validity were tested using Cronbach's alpha, Average Variance Extracted (AVE), Composite Reliability (CR),and fit indices for the model through SPSS and SmartPLS. Greater digital learning readiness strongly facilitates student motivation, engagement, and academic achievement. Perceived digital tool usefulness mediates the influence of readiness on academic performance to some extent. Motivation and engagement also have central mediating roles. Support from teachers has a positive impact on motivation, which reinforces student engagement. The study confirms that digital readiness, motivational factors, perceived technology usefulness, and supportive pedagogy are integrated to influence academic performance in digital learning settings. The results have theoretical and practical suggestions for increasing the efficiency of digital learning in the higher education system of Sri Lanka.enAcademic PerformanceDigital Learning ReadinessMotivationStudent EngagementTeacher SupportArticlehttps://doi.org/10.54389/CCGN6841