ICT Deployment for Teaching in the COVID-19 Era: A Quantitative Assessment of Resource Availability and Challenges in Public Universities

Despite the changes in human behaviour and interactions occasioned by the COVID-19 pandemic, many institutions are yet to adapt to the new normal fully. While some educational institutions switched entirely to e-learning to promote teaching and learning, others could not offer education due to physical and social restrictions. Previous studies in Africa have identified reasons for the poor ICT adoption for educational purposes. However, the degree to which these factors affect ICT utilisation is barely analysed. Using a quantitative approach, this study assessed ICT deployment for teaching in the COVID-19 era by focusing on their availability and challenges. A sample of 344 respondents from a population of 2,867 academic staff at two Nigerian public universities participated in the study. After receiving face validity from experts, a structured questionnaire was used for data collection. Cronbach alpha reliability indices of the questionnaire ranged from 0.72 to 0.94. Descriptive statistics (simple percentages, mean, and standard deviation) and inferential statistics (one-way ANOVA) were used for data analysis. Findings generally revealed a moderate extent in the availability of ICT resources in public universities. The cost of data, computer literacy, and electricity supply are challenges that significantly affected ICT deployment in the COVID-19 era. Based on this finding, it was concluded that personal and institutional challenges affect how ICT resources are deployed in public universities. It was recommended, among other things, that the management of each tertiary institution should apportion proceeds from internally generated revenue to procure ICT resources specific to the need of the school. The study provides the ground for further research into students' use of ICT for educational purposes.

Mining Frequently Traveled Routes During COVID-19

The aviation industry has been one of the biggest employers of labor through its value chain until the world came to a halt due to the outbreak of the novel Coronavirus Disease 2019 (COVID-19). During the first quarter of 2020, many countries started implementing travel restrictions to minimize the spread of the virus, leading to a severe impact on the revenues of travel and tourism industry operators. As the world begins to reopen, it has become imperative for airlines to create attractive travel packages to meet the demands of passengers in a post-COVID era. This study introduces an associative data mining approach, using the Apriori algorithm – an associative rule mining algorithm tasked with finding frequent patterns in large heterogeneous datasets. Our preliminary findings show that London had the most frequently traveled destination for travellers out of a dataset containing flight information from March 2020 to April 2021. Our results may assist aviation operators in tailoring ticket fares at affordable prices to meet the demands of passengers. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.