A Comprehensive Study to Investigate Student Performance in Online Education during Covid-19

During the recent Covid-19 pandemic, there has been a tremendous increase in online-based learning (e-learning) activities as nearly every educational institution has transferred its programs to digital platforms. This makes it crucial to investigate student performance under this new mode of delivery. This research conducts a comparison among the traditional educational data mining techniques to detect the best performing classifier for analyzing as well as predicting students’ performance in online learning platforms during the pandemic. It is achieved through extracting four datasets from X-University student information system and learning platform, followed by the application of 6 classifiers to the extracted datasets. Random Forest Classifier has demonstrated the highest accuracy in the first two out of the four datasets, while Simple Cart and Naïve Bayes Classifiers presented the same for the remainder two. All the classifiers have demonstrated medium to high TP rates, class precision and recall, ranging from 60% to 100% for almost all of the classes. This study emphasized the attributes that have a direct impact on students’ performance. The outcomes of this study will assist the instructors and educational institutions to identify important factors in the analysis and prediction of student performance for online program delivery. © 2022 MECS.

An Empirical Comparison of Students' Performance in Online vs Offline Platforms Using Ensemble Learning Models

Online learning is a paradigm shift from traditional offline education;recently there has been a remarkable surge in e-learning platforms due to Covid 19 outbreaks. There is a significant difference in students' performance on both platforms. The primary focus of this study is to investigate how the students perform in both learning methods. Moreover, five ensemble-learning approaches are compared to predict student performance in online and offline education platforms. Ensemble learning is a prominent machine learning meta-approach that integrates predictions from several models to improve prediction. Students' performance data for both offline and online platforms were extracted from a private university's student database. Five ensemble-learning methods were applied to both datasets for predictive analysis. According to the findings of this study, students do better on online platforms than in traditional education systems. Furthermore, XGBoost, Gradient Boost, and Stacking KNN fared better for online data, whereas stacking neural networks and stacking random forest performed better for offline data. The findings of this study will assist educational instructors to concentrate more on students' performance based on their particular learning system. © 2022 ACM.

Plasmonic Au nanodots array device for detection of SARS-CoV-2 virus

Localized surface plasmon resonance of Au nanodots array are very sensitive and resonance field disturbance due to 100 nm sized SARS-CoV-2 virus can be detected via resonance wavelength shift. We have proposed Au nanodots (100 nm diameter and 200 nm pitch) array plasmonic biosensing platform for SARS-CoV-2 virus detection. © 2022 The Author(s).

Automatic detection of COVID-19 from chest x-ray images using deep learning model

The infectious disease caused by novel coronavirus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized coronavirus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods. © 2022 Author(s).

Evidence of occult global and regional myocardial strain abnormalities in adolescent athletes after covid-19 infection

Introduction: There is increased scrutiny on cardiac screening of competitive athletes after COVID19 illness, and cardiac magnetic resonance (CMR) is frequently undertaken. Limited reports with echocardiography-based strain techniques suggest occult abnormalities in collegiate athletes after COVID-19. Cardiac involvement in the adolescent athlete has not been well characterized. The purpose of this study is to describe CMR findings, including strain encoded (SENC) imaging, in adolescent athletes after COVID-19 (C19+AA). Methods: Retrospective review was performed of ambulatory C19+AA patients who underwent CMR (Group A). Healthy athletes (Group B) and nonathlete healthy controls (Group C) underwent CMR with SENC for comparison. Myocardial strain was evaluated by MyoStrain (Myocardial Solutions, Morrisville, NC). LV global (GLS) and regional strain (from modified AHA-16 segment model) were compared between the three groups with abnormal defined as magnitude <17 and statistical significance set at p < 0.05. Results: Group A patients were younger (n = 87, 52% male, age 15.4±1.8 yrs) than Group B (n = 19, 63% male, age 21.3±1.6 yrs) and Group C (n = 9, 52.6% male, age 19.3±1.1 yrs) with no difference in LVEF between the three groups (Group A = 59.1±3.9%, Group B = 60.3±6.2%, Group C = 61.0±4.1%). Despite preserved global function, Group A had significantly lower GLS (-17.6±2.3% vs Group B =-20.8±1.4%, p <0.04 and Group C =-19.1±2.4%, p = 0.02) with no difference between the latter groups (p = 0.07). Higher numbers of abnormal segments were observed in Group A (6.9±3.7, 43.1%) vs Group B (2.4±1.9, p < 0.0001) and Group C (3.7±3.5, p < 0.0001) with no difference between the latter groups (p = 0.1). Conclusions: Global and regional strain abnormalities were common in C19+AA in the setting of normal LVEF. This may represent occult myocardial abnormalities in adolescents after COVID-19. Future longitudinal studies with age matched controls are needed to monitor for progression.

Student performance classification and prediction in fully online environment using Decision tree

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary field of study that focuses on methodologies for extracting useful knowledge from data. During the latest Covid-19 pandemic, there was a significant uptick in online-based learning (e-learning) operations as every educational institution moved its operations to digital channels. To increase the quality of education in this new normal, it is necessary to determine the key factors in students' performance. The main objective of this study is to exploit the regulating factors of education via digital platforms during the covid-19 pandemic by extracting knowledge and a set of rules by using the Decision Tree (j48) classifier. In this study, we developed a conceptual framework using four datasets, each with a different set of attributes and instances, collected from "X-University"and Microsoft teams. 'Final term' and 'Mid-term' examinations acted as the root node for all four datasets. The findings of this study would benefit higher education institutions by helping instructors and students to recognize the shortcomings and influences controlling students' performance in the online platforms during the covid-19 pandemic, as well as serve as an early warning framework for predicting students' deficiencies and low school performance. ©AJSE 2021.

Predicting spread, recovery and death due to COVID-19 using a time-series model (Prophet)

The world is facing its biggest challenge since 1920 due to spread of COVID-19 virus. Identified in China in December 2019, the virus has spread more than 200 countries in the world. Scientists have named the virus as Novel Corona Virus (belongs to SARS group virus). The virus has caused severe disruption to our world. Educational institutions, financial Services, government services and many other sectors are badly affected by this virus. More importantly, the virus has caused a massive amount of human deaths around the world and still its infecting people every day. Scientist around the world are trying to find a solution to stop the COVID-19. Their solutions include identifying possible effective vaccine, computer-aided modelling to see the pattern of spread etc. Using Machine Learning techniques, it is possible to forecast the spread, death, and recovery due to COVID-19. In this article, we have shown a machine learning model named as Prophet Time Series Analysis to forecast the spread, death, and recovery in different countries. We train the model using the available historical data on COVID-19 from John Hopkins University's COVID-19 site. Then we forecast spread, death, and recovery for seven days using a well known forecasting model called Prophet. This interval can be increased to see the effect of COVID-19. We chose 145 days of historical data to train the model then we predict effect for seven days (15 June 2020 to 22 June 2020). To verify out result, we compare the predicted value with actual value of spread, death and recovery. The model provides accuracy over 92% in all the cases. Our model can be used to identify the effect of COVID-19 in any countries in the world. The system is developed using Python language and visualization is also possible interactively. By using our system, it will be possible to observe the effect of spread, death and recovery for any countries for any period of time. © 2021 AIUB Office of Research and Publication. All rights reserved.