Faculty of Computing

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Subject: search.filters.subject.Online education

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    PublicationEmbargo
    AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance
    (Institute of Electrical and Electronics Engineers, 2022-10-29) Liyanage, M.L.A.P.; Hirimuthugoda, U.J; Liyanage, N.L.T.N.; Thammita, D.H.M.M.P; Koliya Harshanath Webadu Wedanage, D; Kugathasan, A; Thelijjagoda, S
    Higher productivity in online education can be attained by consistent student engagement and appropriate use of learning resources and methodologies in the form of audio, video, and text. Lower literacy rates, decreased popularity, and unsatisfactory end-user goals can result from unbalanced or inappropriate use of the aforementioned. Prior studies mainly focused on identifying and separating the elements affecting the quality of online education and pinpointing the students' preferred learning styles outside of in-person and online instruction. This has not been able to clearly show how to enhance and customize the online learning environment in order to benefit the aforementioned criteria. This case study will primarily concentrate on elements that can be personalized and optimized to improve the quality of online education. With the aid of various algorithms like logistic regression,Support Vector Machines (SVM), time series forecasting (ARIMA), deep neural networks, and Recurrent Neural Networks (RNN), which make use of machine learning and deep learning techniques, the ultimate result has been attained. To increase application and accuracy, the newly presented technique will then be presented as a web-based software application. Contrary to what is commonly believed, this applied research proposes a new all-in-one Learning Management System (LMS) for students and tutors that acts as a central hub of all the learning resources.
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    PublicationOpen Access
    Online education during Covid-19 lockdown-Student experience in the non-state higher education in Sri Lanka
    (National Science Foundation, 2020) Wijesundara, M; Peiris, T. S, G; Thanaraj, T; Peiris, C. N
    The objective of this paper is to analyze the effectiveness of online education in both teaching and learning, based on data captured from the Moodle LMS, Eduscope Lecture Video Management System and two students’ feedback surveys at the Sri Lanka Institute of Technology (SLIIT) from January to December 2020. Regression analysis and chi-square test were used as data analyses tools. The data were analyzed using simple linear regression and Analysis of LMS data showed that with each user logging into LMS 3 to 4 times a day with a minimum of 10 user actions per login. The study also found that the percentage of ‘satisfactory’ ratings by students for all aspects considered under four criteria, namely lecture delivery, technology, support services and overall satisfaction exceeded 80% irrespective of the faculty and time of the year. However the students’ responses for individual criteria within four aspects were significantly associated (p < 0.05) by the nature of the faculty. More than 75% of students claimed that the online delivery is working well and enabling them to continue with their studies. No significant difference was found with respect to overall satisfaction by the students between the two periods. The inferences of this study can be used effectively to provide better online education environment in higher education organizations in Sri Lanka. and The infrastructure upgrades, including overall bandwidth, new services including Zoom, Webex and MS Teams, staff training on online delivery enabled a quick transition to online delivery. The incorporation of Respondus lockdown browser and Respondus Monitor online proctoring system further enhanced the integrity of online assessments and examinations.