Browsing by Author "Yapa, C.G"

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Bridging tradition and innovation: exploring vegetable harvest loss reduction strategies in Sri Lanka
(Emerald Publishing, 2026-01-15) Jayasuriya, N; Yapa, C.G; Rathnayake, T.A; Dilhara, A; Rathnayake, I.D; Mathangadeera, R
Purpose – This study aims to address a significant gap in the literature regarding vegetable harvest loss reduction methods, exploring both traditional and modern perspectives in Sri Lanka, which is largely driven by an agricultural economy. This study explores the diverse strategies employed and how they are going to be integrated by Sri Lankan vegetable farmers, highlighting both traditional and modern pre- and post-harvest practices aimed at improving productivity, sustainability and resilience in agricultural systems. Design/methodology/approach – The study was conducted across key agricultural districts in Sri Lanka, with data collected through semi-structured interviews with vegetable farmers using the snowball sampling method. Thematic analysis was employed to identify patterns and themes in the data. Findings – The findings emphasize the importance of traditional methods, including cultural practices such as cultivating at auspicious times, established pest control and irrigation techniques. These are complemented by advanced agricultural innovations, modern harvest protection methods and improved packing and transportation techniques. This integrated approach showcases farmers' adaptability in reducing vegetable losses despite the challenges they face. Originality/value – Post- and pre-harvest loss reduction in Asian countries can be considered an understudied area. Furthermore, the focus on traditional methods is rare in the field. Therefore, this study provides a clear understanding of traditional and modern methods that are suitable for farmers in developing countries.
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From AI Assistance to Critical Thinking: Exploring Cognitive Offloading and Metacognition as Mechanisms within Personalized Learning Environments
(Institute of Electrical and Electronics Engineers Inc., 2026) Gunathilake, N; Gamage, A; Rajapakshe, D; Jayasooriya, M; Wisenthige, K; Yapa, C.G
The rapid adoption of AI-assisted learning tools in higher education has completely transformed the undergraduate study system, but empirical evidence on their impact on deep cognition and learning processes is limited. This study investigates the effects of AI-assisted learning tool usage on metacognition, cognitive offloading, personalized learning, and critical thinking among Sri Lankan undergraduates. Using positivism philosophy and a deductive quantitative approach, data were collected from 379 students in computing, management, and engineering subjects through a 35-items, five-point Likert scale questionnaire, and analyzed using PLS-SEM. The findings reveal significant direct, indirect, and moderating relationships among key variables, suggesting that the use of AI-assisted learning tools influences students’ learning regulation, reliance on external support, and development of higher-order thinking. This study provides empirical evidence for the cognitive and psychological effects of AI-assisted learning tools, and helps address an important research gap, and offers practical insights for educators, curriculum developers, and policymakers to use balanced and effective AI integration in higher education.