Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Item Embargo 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 DSPublication Embargo 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.