Research Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4194

This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.

Browse

Filters

2018 - 201912020 - 20222

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.Online Learning

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationEmbargo
    Hastha: Online Learning Platform for Hearing Impaired
    (IEEE, 2022-11-30) Wanasinghe, D; Maddugoda, C; Ramawickrama, H; Munasinghe, T; Abeywardhana, L; Mallawarachchi, Y
    Sign language is the primary means of communication for the hearing-impaired community. Introducing a learning platform can result in many ways to make learning more accessible for the hearing-impaired community of Sri Lanka. Although many approaches are being made to build such systems, the learning platform “Hastha” aims to provide a more interactive outcome with a component that converts Youlhbe videos to sign language and a Chatbot component that acts as an intermediary between a hearing-impaired user and a Google Search Engine. Furthermore, it includes a game-based learning platform and a gesture translation component from Sri Lankan to American Sign Language while the results are displayed to the users in the form of an animation. The proposed methodology is achieved by using Natural Language Processing, speech recognition, and machine learning techniques. This web-based application enables increased interaction between the student and the system making it an effective learning environment for the hearing impaired.
  • Thumbnail Image
    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.
  • Thumbnail Image
    PublicationEmbargo
    Online learning resources finder based on computer programming domain
    (IEEE, 2018-12-21) Somadasa, K; Karunadhipathi, M; Wickramasinghe, N; Subasingha, S; Kodagoda, N; Suriyawansa, K
    With the huge growth of the internet, the amount of content on the internet also grown. Within that context, there are many irrelevant contents spread within the internet for a given topic. Therefore, it is very hard to find accurate, informative learning resources. Even though there are some search engines available, the job they do is very generic and provide millions of search results. Finding the most important learning content within a large set of search results is an extremely difficult task. The solution proposed in this paper addresses this issue. The learner can search for what is required and the system would filter both text and video content across the internet to provide the most relevant content. This paper describes how a textual resource finder was implemented based on ontologies, Euclidean distance, and the TF-IDF algorithm. The video content analyzer used a deep learning algorithm. The solution was developed for learners in the Computer Programming domain.