‘Lockdown’: Digital and emergency elearning technologies—A student perspective

The COVID-19 pandemic and the subsequent emergency measures had a fundamental and disruptive impact on societies and, in particular, on the educational sector. The transition of the modality of educational delivery from face-to-face to online occurred within days; this research study considered the concepts of digital trust and digital access, using structuration, postmodernism, and critical social theory as lenses to understand Higher Education (HE) student experiences during the first wave of the lockdown. The study compared these aspects in Hungary, South Africa, and Wales (UK) with different digital and network readiness indices. The digital development of the countries is presented using both the Digital Intelligence and Network Readiness indices. The research approach was cross-country, international, comparative, inductive, and quantitative

‘Lockdown' : digital and emergency eLearning technologies - a student perspective

As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN's architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses