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Author: search.filters.author.Weerasinghe, M

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    Gamifying Coding Education for Beginners: Empowering Learners with HTML, CSS and JavaScript
    (Institute of Electrical and Electronics Engineers Inc., 2025) Chandrasekara, S; Hewavitharana, D; Weerasinghe, M; Gayasri, B; Wijendra, D; De Silva, D
    Traditional coding education often fails to engage and motivate beginners due to its lack of interactivity and personalized learning experiences. This paper presents a gamified learning platform designed to teach Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript (JS) to beginners. The platform incorporates interactive lessons, AI (Artificial Intelligence)-powered coding assistance, and advanced gamification mechanics to enhance learner motivation, engagement, and success. Furthermore, key features include performance-based recommendation engines, virtual coding environments with real-time feedback, and a collaborative platform for peer interactions. The integration of AI provides personalized feedback and adaptive learning paths, while gamified elements such as badges, points, and leaderboards foster competitive and enjoyable experiences. Preliminary findings demonstrate a 40% increase in student engagement metrics and a 35% improvement in coding competency compared to traditional methods. This research lays the groundwork for future expansion to additional programming languages and broader educational applications, with potential implications for transforming computer science education on a scale.
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    Deep Learning Based Sinhala Sign Language Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2025) Samarakoon, S.C; Weerasinghe, M
    Deaf individuals in Sri Lanka rely primarily on Sinhala Sign Language (SSL) for communication due to hearing impairments. However, effective communication between the Deaf and hearing populations remains challenging due to the limited knowledge of SSL among hearing individuals. This research aims to address this gap by developing an SSL gesture recognition system using computer vision and deep learning techniques. Specifically, the study compares the performance of 3D Convolutional Neural Networks (3D-CNNs) and a hybrid 2D Convolutional Neural Network with Long Short-Term Memory (2D-CNN+LSTM) for classifying short-duration spatiotemporal SSL gestures. Additionally, the research emphasizes reducing computational complexity to ensure efficient operation of the system on low-end devices. These contributions advance the accessibility and practical usability of gesture recognition systems for the Sinhala Sign Language.