Research Publications

Now showing 1 - 3 of 3

Open Access
The Impact of Digital Learning Readiness on Academic Performance and Student Engagement in Sri Lanka
(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Nuwanthika, W. A. N.; Thathsarani, U.S.
The 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.
Open Access
Systematic Review: The Role of Data Analytics in Enhancing Academic Performance Classroom interaction, Learning Analytics in Higher Education
(ICSDB 2024 and SLIIT Business School, 2024-12-10) Sithumini, J.H.C.; Sanjuka, A.N.E.; Ranawaka, P. S.; Hasaranga, H.G. D.; Samarakkody, T.; Pathirana, G
The field of data analytics has seen substantial growth, particularly within the education sector. With the recent expansion of e-learning due to the COVID-19 pandemic, the ability to make data-driven decisions in education has become more important than ever. This review synthesizes existing research on the role of data analytics in enhancing academic performance and decisionmaking in higher education. The key objectives are to examine the influence of data analytics on student performance, explore learning analytics’ role in institutional decision-making, and assess the effect of data analytics on e-learning systems, particularly during the COVID-19 pandemic.
Embargo
E-Pod: E-learning System for Improving Student Engagement in Asynchronous Mode
(IEEE, 2021-10-27) Tennakoon, S; Wickramaarachchi, T; Weerakotuwa, R; Sulochana, P; Karunasena, A; Piyawardana, V
Over the last decade, e-learning has grown significantly as the internet and education have merged to give individuals the possibility to learn new skills. With the COVID-19 pandemic, the use of e-learning has increased in an exponential manner. The asynchronous e-learning mode is found to be appealing to students due to its adoption at any time and in any location. Yet, this mode of learning suffers from lack of interactivity. Under such circumstances, this research proposes E-Pod, an asynchronous e-learning system, which promotes student engagement. Through attention monitoring, when the students are found to be inattentive they are provided with opportunities to engage in a wide range of activities such as summarization activities, puzzles and answering questions to improve the interactivity. The accuracy achieved for the gaze estimation model is 89.5 % and the accuracy achieved for the facial emotion recognition model is 83%. In order to generate FBQ and MCQ questions for students, a SVM model was trained to an accuracy of 95.56%. E-Pod includes a MaLSTM model with 83.98% accuracy for short answer evaluation and a DistilBERT model with 86.8% accuracy for essay answer evaluation. The system is developed using a blend of cutting-edge technologies including image processing, Natural Language Processing, machine learning algorithms and language models. With these features, E-Pod is proposed as an all-inclusive system which stands out from existing e-learning systems and will be helpful for educational institutions to deliver flexible and self-paced learning to their students in asynchronous mode.

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