Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students' performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.

Acute Inflammatory Cns Diseases Following Vaccination against Sars-Cov-2

Background Vaccination is a recognised trigger of ADEM and approximately 50% paediatric cases have antibodies to MOG. The SARS-CoV-2 mass vaccination programme could therefore trigger cases of MOGAD. Neuromyelitis optica (NMO) is an autoimmune inflammatory condition of the CNS associ- ated with antibodies to AQP4. Method Ten patients (ages 22 - 65 years) with antibodies to MOG or AQP4 were referred to the NHS England NMO service having developed acute onset CNS inflammation within 8 weeks of vaccination. Results Eight patients had MOGAD, seven of whom received the AstraZeneca vaccine (AZV) and one the Pfizer vaccine (PV). Only the post-PV MOGAD patient presented with typical adult-onset phenotype of isolated ON. All post-AZV MOGAD patients presented atypically;85.7% had LETM and 71.4% had intrac- erebral lesions, resembling ADEM more commonly seen in paediatric MOGAD. The atypical presentation supports a causative role of AZV, but the role of PV is less convincing. Two patients had AQP4-NMOSD with typical demographic features. Both received AZV. Less typically, one young adult presented with LETM rather than characteristic young adult ON, the other had a silent short segment myelitis, which is rarely seen in AQP4-NMOSD. Both patients achieved good outcomes. Conclusion We discuss the potential causation and pathophysiological mechanisms.

Achieving Personalized Precision Education Using the Catboost Model during the COVID-19 Lockdown Period in Pakistan

With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student's participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students' performance. © 2023 by the authors.

Institutional Monitoring and Feedback in Developing and Delivering Online EFL Courses: A Case Study

The sudden outbreak of the COVID-19 pandemic necessitated a paradigm shift from face-to-face to online teaching of English as a Foreign Language (EFL) courses through widely recognized virtual platforms. The suddenness of the movement did not give enough time for the tertiary-level EFL teachers in Bangladesh to be prepared. In consequence, the sustainability of higher educational institutions was at stake since ensuring English education to the students of all majors is one of the crucial demands of the respective stakeholders. The academic harmonization of these higher educational institutions requires institutional intervention in terms of monitoring the quality of education and giving feedback to the teachers. This study investigates the role that an institutional monitoring and feedback system played in developing and delivering online EFL courses in a private university in Bangladesh, which is relatively unexplored by international and local research. A mixed methods research design was used: a questionnaire survey and two focus group discussions. Participants were EFL teachers and administrative officers involved in the process of central monitoring and feedback system of the institution. The analysis critically seeks to discover the perceptions of the participants on three issues: (i) the impact of institutional monitoring and feedback;(ii) areas of teaching-learning pedagogy that the process addressed;and (iii) challenges and suggestions. The results show that the teachers benefitted greatly from this system in general, but it did not address specific issues related to EFL courses. The system lacked the involvement of the stakeholders in policymaking and assessment rubric formation. The study suggests that EFL courses should be monitored differently than other courses and EFL teachers should be empowered and involved directly with the decision-making process of central monitoring and feedback system. © 2022 Asociacion Mexicana de Maestros de Ingles MEXTESOL A.C. All rights reserved.

Maritime Education after COVID-19 Era

The COVID-19 pandemic has very quickly changed various human behavior including the world of education. Maritime Education is one that feels the impact of the pandemic. Various Solutions in educational technology must be quickly adjusted to fill the gap and ensure learning is not disrupted. Although online learning is not new, with the pandemic can accelerate digital learning used to overcome and improve the quality of online learning. Article we want to convey our experience in organizing maritime education at the Semarang Shipping Science Polytechnic using case study techniques. The results of this study revealed that although online learning is very helpful for teaching and learning activities during the pandemic, it has not been able to replace practical learning. With the decline of Covid-19 pandemic cases in Indonesia, the author wants to make short-term breakthroughs and possible long-term solutions that can contribute to learning in the future. The use of learning recordings with simulators, practicum recordings and theoretical learning recordings is expected to help maritime learning in the future.

Initiatives taken by NGOs and private companies to fight the COVID-19 pandemic

In times where all other companies are on the verge of shutting down, app-based companies have found new meaning to their businesses through helping people. They have risen from the ashes like a phoenix and have set a marvellous example for all other companies about the meaning of life and how the goat of every company should be set. There is a saying that everything has a bright side, even a life-threatening pandemic like COVID-19 has a bright side to it. And one of those is the new definition of business and partnership. The outbreak of Coronavirus, countrywide lockdown and people in need has taught companies that making profit is not the only goal of business and partnerships. Helping people in times of crisis is even bigger than profit. You go after doing good for the people, profit will follow you anyway, and that trend has been initiated by app-based companies in Bangladesh. Copyright © 2021 Inderscience Enterprises Ltd.