Résumé
This study demonstrates how significantly the COVID-19 pandemic has affected Bangladesh's formal education system. Despite the fact that Bangladesh introduced Information and Communication Technology (ICT) in educational practices before the pandemic, effective ICT deployment could not be integrated at all levels. Even though online classes and other print- and electronic media-based approaches evolved into the "new normal" in an effort to address the difficulties brought on by the pandemic, both teachers and students have faced and continue to encounter many challenges. A convergent parallel mixed method design was followed for this study. Data were collected from 205 Bangladeshi students and 50 Bangladeshi teachers through semi-structured questionnaires. In addition, 11 parent interviews and 12 Key Informant Interviews were conducted. According to the findings, the lack of proper training for teachers, poor socio-economic conditions, lack of internet availability and speed, the shortage of ICT equipment, students not being technologically exposed, and the disruption of electricity are major issues hindering the fruitful implementation of online education. Creating an ICT framework, providing subsidised internet for students and instructors for instructive purposes, promoting alternative means to carry on formal education, dedicating instruction hours in TV and radio channels, ensuring proper IT infrastructure and tools, and taking initiatives to promote the learning management system can play a significant role in creating the ideal environment to promote online education. In summary, this study suggests a holistic framework to continue formal teaching-learning in different levels of education to achieve sustainable development goals (SDG) without any disruption in emergency contexts such as the COVID-19 pandemic.
Résumé
By trade we usually mean the exchange of goods between states and countries. International trade acts as a barometer of the economic prosperity index and every country is overly dependent on resources, so international trade is essential. Trade is significant to the global health crisis, saving lives and livelihoods. By collecting the dataset called “Effects of COVID-19 on trade” from the state website NZ Tatauranga Aotearoa, we have developed a sustainable prediction process on the effects of COVID-19 in world trade using a deep learning model. In the research, we have given a 180-day trade forecast where the ups and downs of daily imports and exports have been accurately predicted in the Covid-19 period. In order to fulfill this prediction, we have taken data from 1st January 2015 to 30th May 2021 for all countries, all commodities, and all transport systems and have recovered what the world trade situation will be in the next 180 days during the Covid-19 period. The deep learning method has received equal attention from both investors and researchers in the field of in-depth observation. This study predicts global trade using the Long-Short Term Memory. Time series analysis can be useful to see how a given asset, security, or economy changes over time. Time series analysis plays an important role in past analysis to get different predictions of the future and it can be observed that some factors affect a particular variable from period to period. Through the time series it is possible to observe how various economic changes or trade effects change over time. By reviewing these changes, one can be aware of the steps to be taken in the future and a country can be more careful in terms of imports and exports accordingly. From our time series analysis, it can be said that the LSTM model has given a very gracious thought of the future world import and export situation in terms of trade. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.