Research Publications

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    An Adaptive E-Learning Platform for Individuals with Down Syndrome
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sandaruwan U.V.S.; Dias A.H.J.S.S; Shamindi H.M.H; Priyawansha N.G.D.; Chandrasiri L.H.S.S; Attanayaka B.
    Children with Down Syndrome (DS) encounter varying degrees of learning disabilities within the traditional education framework, requiring personalized interventions. This paper presents Blooming Minds, an adaptive, Machine Learning (ML) driven e-learning platform designed to support the development of cognitive, linguistic, and motor skills in children with DS. Built on the VARK (Visual, Auditory, Reading/Writing, Kinematic) theory, the platform provides personalized activities using real-time feedback mechanisms. The system includes nine interactive modules that cover the above VARK theory. It uses ML algorithms, including Support Vector Machine (SVM) and Random Forest (RF) for screening, Convolutional Neural Networks (CNN) for handwriting and speech analysis, Long Short-Term Memory (LSTM) for sequence prediction, and Reinforcement Learning (RL) for adaptive difficulties. Handwritten letters and voice samples from children with DS, both domestic and international, were specifically considered as inputs for this research. Progress tracking dashboards provide visual insights for educators, parents, and caregivers, improving support and adaptability. The system achieved 91.26% accuracy in letter recognition and 88% in speech classification. This e-learning platform has been recognized as an effective solution in Sri Lanka, allowing for further correlations and investigations to assess the knowledge capacity and ability to express that knowledge in children with DS
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    PublicationOpen 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.
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    E-Learning Assistive System for Deaf and Mute Students
    (IEEE, 2022-12-09) Ranasinghe, P; Akash, K; Nanayakkara, L; Perera, H; Chandrasiri, S; Kumari, S
    E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.
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    PublicationOpen Access
    Learners’ Satisfaction and Commitment Towards Online Learning During COVID-19: A Concept Paper
    (SAGE Publications, 2021-11-08) Ranadewa, D. U. N; Gregory, T. Y; Boralugoda, D. N; Silva, J. A. H. T; Jayasuriya, N. A
    This study offers a comprehensive literature review on the gaps related to online learning efficiency and a structured conceptual model. The findings would be favourable for the learners, lecturers, future researchers, universities and other educational institutes. This study has presented the results of a systematic literature review on the factors affecting the efficiency of online learning and how they impact on satisfaction and commitment of learners. To conduct the literature review, approximately 40 empirical studies were reviewed and analysed. The results reveal that several factors, including academic issues, accessibility issues, technological skills, mental well-being and lecturer commitment, impact depreciating the online learning efficiency, which has made a significant impact on learner satisfaction and learner commitment during the COVID-19 pandemic. If the pandemic would continue, the institutes can use the deliverables to figure out the difficulties encountered by the learners during the pandemic, how to prevent those issues and to search for a solution: to re-open the universities following necessary health guidelines or to resume delivering education online. The literature evaluates the impact of online learning efficiency on learners’ satisfaction and commitment, and there are no adequate empirical studies available for testing the online learning efficiency with respect to learners’ satisfaction and commitment. Hence, in identifying several gaps related to online learning efficiency, this study offers a new structured conceptual model.
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    PublicationOpen Access
    DEVELOPING WEB BASED QUALITY INFORMATION SYSTEMS FOR QUALITY IMPROVEMENT AT THE HORIZON CAMPUS
    (Horizon Campus, National School of Business and Management , Sri Lanka, 2017) Peiris, S; Wickramasinghe, S; Peiris, C. N
    Quality Information Systems (QIS) provide quality related information to stakeholders. In elearning applications, QIS should provide e-learning materials to the lecturers and the learners who use them. In Student Management Systems (SMS), QIS provide all the facilities to run the administrative functions smoothly. Electronic Research Repository (such as DSpace) enhances the quality of the institute as it reflects the research competencies of the academic staff of the institute. Horizon Campus QIS include eLearning, SMS, DSpace, Library Management System, all based on a single platform. QIS provide better service to their stakeholders anywhere, anytime effectively and efficiently. Students‘ pass rate has been increased after introducing the Learning Management System (LMS). eLearning platforms immensely facilitate the teaching and learning process as they offer an environment-friendly and efficient mechanism for ensuring learner centered teaching and learning. As QIS implemented in a Cloud based System, it is easy to maintain and run with minimal cost.
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    E-Learn Detector: Smart Behaviour MonitoringSystem to Analyze Student Behaviours DuringOnline Educational Activities
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Bamunuge, H.K.T.; Perera, H.M.; Kumarage, S.; Savindri, P.A.P.; Kasthurirathna, D.; Kugathasan, A.
    With the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. “E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. “E-Learn Detector” is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.
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    Computer Vision and NLP based Multimodal Ensemble Attentiveness Detection API for E-Learning
    (IEEE, 2021-04-21) Wijeratne, M. D; Lakmal, R. H. G. A; Geethadhari, W. K. S; Athalage, M. A; Gamage, A; Kasthurirathna, D
    Attention is the fundamental element of effective learning, memory, and interaction. Learning however, with the evolvement of technologies in the modern digital age, has surpassed traditional learning systems to more convenient online or e-learning systems. Nevertheless, unlike in the traditional learning systems, attention detection of a student in an e-learning environment remains one of the barely explored areas in Human Computer Interaction. This study proposes a multimodal ensemble solution to detect the level of attentiveness of a student in an e-learning environment, with the use of computer vision, natural language processing, and deep learning to overcome the barriers in identifying user attention in e-learning. The proposed multimodal captures, processes, and predicts user attentiveness levels of individual students, which are subsequently aggregated through an ensemble model to derive an overall outcome of better accuracy than individual model outcomes. The final outcome of the ensemble model produces a range of percentages, within which the attentiveness level of the student lies during a single online lesson. This range is consequently delivered to the users through an Application Programming Interface.