Browsing by Author "Jayalal, Shantha"

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Factors Influencing the Secondary Level Students’ Satisfaction in E-Learning: A Case Study of an Educational Institute in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayanett, W. I.; Jayalal, Shantha
With the covid-19 pandemic, e-learning has shown significant growth in Sri Lanka over the last few years. As a remedy to sudden school closure during the covid-19 outbreak, educational institutes have adopted e-learning to minimize the disruption of education. Even though there are benefits, teachers complained that the satisfaction of secondary level students is declining, and it has impacted the academic performance to become low. Therefore, this research is conducted to investigate the factors influencing the secondary level students’ satisfaction in e-learning at an educational institute in Sri Lanka from students’ perspectives. This study takes 211 students from secondary-level students in an educational institute as participants. The data were gathered through online questionnaires undertaking a Quantitative approach. Overall results indicate that flexibility is the most influencing factor. Respectively, the quality of the e-learning system/platform, Interactivity, quality of the Internet, and quality of the learning material influence students’ satisfaction. As per the recommendations, the educational institute is suggested to select a suitable e-learning platform and use Learning Management System (LMS). Also, they are suggested to provide a fixed timetable for teachers. The teachers are encouraged to be more interactive and to use computer-based learning materials to deliver the content. Also, an educational institute is suggested to provide adequate teacher training in creating resource materials. The Ministry of Education is suggested to provide a free e-learning system and data package for less cost. Also, the Ministry of Education is recommended to take strategic decisions to enhance school curriculums to be interactive. E-learning system designers should be aware of the school curriculum in designing e-learning systems. And the Government is encouraged to increase the coverage and infrastructure facilities to establish a satisfying e-learning environment.
Sinhala Language Fake News Detection in Social Media Using Autoencoder-Based Method
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Adihetti, Rahul; Jayalal, Shantha
The spread of fake news in the social media has grown significantly over the past few years. According to the New York Times, fake news is defined as "made-up articles meant to deceive." Additionally, the way they are released is almost identical to that of conventional news organizations. The issue is that a significant number of news outlets outside the major and reliable ones are disseminating unreliable information. This problem is exacerbated by the ease with which anything can be published from anywhere on well-known social networking and social media platforms. People can use this to their advantage by disseminating any type of message on various social networking sites to accomplish their objectives. In the Sri Lankan context, content posted in Sinhala greatly impacts fake news in Sri Lanka. Because utilizing the Sinhala language to describe emotions and feelings makes it easier to connect with Sinhala-speaking people than using content that has been published in other languages, like English. The use of Sinhala on social media has grown over the past few years. Additionally, as the use of the Sinhala language expanded, so did the number of occurrences of fake news. Based on the literature, approaches to identifying fake news depend on the features of the news content. Therefore, this research proposed an autoencoder- based method for Sinhala fake news detection, which is an unsupervised method. The method uses Text, User, Propagation, and Image features from the news content. And also, this research found the best feature combination to detect Sinhala language fake news content, which is a combination of Text, User, and Image features. The method gained an accuracy of 98% and 88% in Precision, Recall, and F1 Score by outperforming other existing anomaly detection methods. The main stakeholder of this study was fact-checking organizations in Sri Lanka.