The COVID-19 pandemic has impacted the education system in Sri Lanka, similar to many countries in the world. As a result, the mode of education shifted from conventional face-to-face classes to online mode. The main objective of this study is to provide a comprehensive overview of the changes to the educational system due to the COVID-19 pandemic among engineering undergraduates of Sri Lanka over three identified pandemic periods. Quantitative descriptive analysis was used together with chi-square statistics to answer the research questions using the data collected through a google survey from engineering undergraduates in Sri Lanka. According to the results, students’ attendance in online classes has improved over time compared to the initial pandemic period. Nearly 50% of students’ family income has been impacted, either stopped or reduced due to the pandemic. Most students have issues regarding computing devices, internet connectivity, and the home environment. According to the chi-square statistics results, few of these issues had a statistically significant relationship between the family income; lower the income, higher the negative impact on students. More than half of the students felt isolated when studying at home during the pandemic. Still, more than 50% of students agreed that lecturers were well prepared to guide and deliver lessons remotely. The overall recommendations of the study are implementing workshops, training on new technologies, awareness programs for educational stakeholders, providing incentives to purchase digital devices, and improving internet connectivity to improve the new standard education system of Sri Lanka.

The COVID-19 outbreak caused a disruption of the conventional education system worldwide. This resulted in a shift towards online mode of delivery. This study analyzed the pandemic’s effect on the education of students from diverse socioeconomic groups in Sri Lanka. The required data for this study was collected through a nation-wide field survey, covering 3020 households. The factor analysis (FA) performed identified seven significant factors. Thereafter, K-means and spectral clustering were used to cluster the population in order to identify the socioeconomic groups that were impacted in a similar manner. This resulted in the identification of three clusters whose demographics and relationship to the extracted factors were extensively analyzed and interpreted. The study concluded that students in high-income households were more emotionally affected, whereas students in low-income households were mostly affected by a lack of resources. The study also concluded that students from mid-income households were the least affected emotionally.

The impact of the COVID-19 pandemic was diverse and disproportionate among nations, and population segments. The impacts of the disease and the containment strategies adopted are broad and cut across multiple facets of life, society, and the economy, which are intimately interlinked. Therefore, a large household survey was conducted to ascertain the socioeconomic impact and human behavior changes due to the pandemic and the containment strategies covering all provinces of Sri Lanka. The ramifications on mobility and human behavior, income, economic status, food consumption, education, access to health services and information, and cultural and psychological changes were explored, and the data are reported in this paper. The survey was conducted on 3020 households, selected using a multistage clustering technique, to assess the impacts of the pandemic through three distinctly identified waves/phases of the pandemic in Sri Lanka. This dataset will enable researchers and policymakers to analyze the impact of the pandemic through a multifaceted perspective enabling a more holistic approach to decision -making. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Coastal systems are a major source of food for Indigenous communities. Climate change poses a high risk to coastal communities’ food security. Successful climate change adaptation practices are essential to ensure food security among Indigenous peoples. Yet, limits and constraints challenge climate change adaptation practices. Our study seeks to identify these limits and constraints in the context of food security among coastal Indigenous peoples. We performed a global scale systematic literature review using 155 scholarly articles to examine the constraints and limits to climate adaptation in the coastal food security and Indigenous peoples’ context. The three research questions are as follows: (i) What are the key constraints? (ii) What are the limits? (iii) What are the ways of overcoming the constraints? First, we found that, globally, the main constraints to adapting to climate change in coastal food security settings are related to governance, institutions and policies. Second, most limits are soft, to be solved, compared to hard limits on coastal systems. Third, we unveiled ways of overcoming the constraints, such as restoring coastal food system resilience, improving food accessibility and building the adaptive capacity of Indigenous peoples. The findings of the study provide valuable insights for policymakers, researchers and other relevant stakeholders involved in decision-making regarding coastal food security in the climate change adaptation context.

The COVID-19 outbreak caused a disruption of the conventional education system worldwide. This resulted in a shift towards online mode of delivery. This study analyzed the pandemic's effect on the education of students from diverse socioeconomic groups in Sri Lanka. The required data for this study was collected through a nation-wide field survey, covering 3020 households. The factor analysis (FA) performed identified seven significant factors. Thereafter, K-means and spectral clustering were used to cluster the population in order to identify the socio-economic groups that were impacted in a similar manner. This resulted in the identification of three clusters whose demographics and relationship to the extracted factors were extensively analyzed and interpreted. The study concluded that students in high-income households were more emotionally affected, whereas students in low-income households were mostly affected by a lack of resources. The study also concluded that students from mid-income households were the least affected emotionally.