ABSTRACT
The widespread of (covid-19) has become the major reason for many physical illnesses in addition to psychological encounters to the whole world. The psychological challenges brought in due to the Covid-19 pandemic have resulted in decrease in the learning curve of students to a very large extent risking the academic ability of students due to psychological/mental health. Hence it is a challenge to identify valid cues for disorientation in the learning ability of the student at the right time and to suggest necessary support and guidance. This paper aims to describe about the work done so far and analyzes the future challenges to be addressed based on the learning curve of a student and gives an insight of how a student can be identified to be psychologically disturbed. © 2023 IEEE.
ABSTRACT
With rapid proliferation of digitalization and compulsion by COVID-19 pandemic, learning formats have been changing from face-To-face to online. Online education enables learners to take courses from anywhere, anytime, but it can also cause some problems for learners who struggle to maintain motivation. In addition, for STEAM education, it is important to engage in hands-on activities, but the ongoing pandemic has made it difficult for students to gather in one place to perform such activities. Incorporating gamification into online education can potentially motivate students and make STEAM education more interactive. On this premise, we have developed PhyGame as a learning system to help high-school students learn Physics. The system includes common game elements such as badges and leaderboards, and interactive simulation of Physics concepts embodying game-like charm. It also includes three modes of learning that allow students to adjust the difficulty according to their own learning levels, and a function that automatically saves learning log. For evaluation, PhyGame was used by students (N=23) at a high school in central Tokyo. The students rated the system on a scale of 1 to 10, and the main results are as follows: (1) Using PhyGame made learning enjoyable (mean score: 7.74);(2) PhyGame provided a good UI/UX (mean score: 7.83);(3) The overall experience with PhyGame was satisfactory (mean: 7.00). Our evaluation results show that interactive and gamified learning systems like PhyGame have a positive impact on user engagement and motivation. © 2023 IEEE.
ABSTRACT
The changes forced by the COVID-19 pandemic, have made educational institutions adopt new practices in the use of VLEs platforms and, one of these is to homogenize virtual classrooms, for which this study aims to diagnose how effective are the digital resources for cloned courses, taking as a pilot the subject of Linear Algebra. The development of this research is longitudinal, empirical-analytical, and quantitative. The study is carried out in two periods, from October 2021 to September 2022, at the Salesian Polytechnic University in the city of Guayaquil, Ecuador, with a total of 944 first year students of engineering careers. As a result, fewer courses for academic risk monitoring were obtained, as well as a higher satisfaction among students and professors involved. It is concluded that the cloned classrooms are a factor of improvement in the learning results to be achieved. © 2023 IEEE.
ABSTRACT
With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
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As the number of MS Teams, Zoom, and Google Meet users increases with online education, so do the privacy and security vulnerabilities. This study aims to investigate the privacy, security, and usability aspects of few tools that are frequently used for educational purposes by Bangladeshi universities. Consumer security, privacy, and usability are also concerns when it comes to online-based software. This study assesses the most commonly used tools that are used for online education based on three important factors: privacy, security, and usability. Assessment factors concerning the privacy, security, and usability aspects are initially identified. Afterwards, each of the applications was assessed and ranked by comparing their characteristics, functionalities, and terms and conditions (T&C) in contradiction of those factors. In addition, for the purpose of additional validation, a survey was carried out with 57 university students who were enrolled at one of several private universities in Bangladesh. Microsoft Teams, Zoom, and Google Meet have been ranked based on an evaluation of their security, privacy, and usability features, which was accomplished through the use of a knowledge base and a user survey. © 2022 IEEE.
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This paper reports the results of a study to determine whether the emotional impact of online education during the pandemic affected male and female students differently. We see these results as an important contribution to the redesign of courses either for online classes generally or for more urgent applications should a similar event occur. In general, we found that females were more likely to be prone to detrimental emotions than males – stress, negative feelings about the online learning experience, and the need to vent their frustrations. Males on the other hand were more positive about the online learning experience and less likely to vent. © (2022) by Association for Information Systems (AIS) All rights reserved.
ABSTRACT
The mega-scale online education conducted nationwide during the COVID-19 epidemic has enabled online learning to move from individualized participation to full participation, practicing and advancing the development of wisdom education to a large extent. In the post-epidemic era, a new educational order that integrates online and offline learning is gradually taking shape, and online learning has become a new norm from emergency. The popularization and promotion of online education has been the general trend. The "double reduction"policy has led to a trust dilemma, a communication dilemma, a cooperation dilemma and an organizational dilemma in the practice of home-school-society collaborative parenting, and an unprecedented challenge for school education and teachers teaching. This study proposes an intelligent operating system based on big data and adaptive learning traction model, rooted in rich pedagogical theories, to solve the above-mentioned challenges in online education by virtue of "wisdom". © 2023 IEEE.
ABSTRACT
Due to the COVID-19 outbreak, people worldwide had to self-quarantine in their homes, resulting in the youth having to continue their education online. The lockdown and the effects of the pandemic impacted students' mental health, exhibiting frustration, stress, and depression. The latter is not ideal for a healthy learning environment, as it involves many coping mechanisms. This study analyzed a database compiling the habits of 1182 individuals in different age groups at various educational institutes in the Delhi-National Capital Region (NCR), India. It identifies factors leading to proposing recommendations to improve students' online education experiences worldwide and facilitate their learning while caring for their mental health. A CRISP-DM methodology was followed to build a model capable of predicting students' satisfaction ratings for online classes by analyzing the students' demographic information and daily habits. © 2023 IEEE.
ABSTRACT
To assess the status of its performance based on expectations and feedback specifically from the educators who are users of Fr. Saturnino Urios University's (FSUU) learning management system (LMS). The researchers investigated and undermined gaps in training and learning using the analytical report. The finding showed that LMS's purpose is not only to deliver online education but also to provide a wide range of services like acting as a platform for online courses and content and learning activities both asynchronous and synchronous instructions. An LMS may provide classroom management as well as facilitation in the perspective and paradigm of higher education with an instructor-led training system or in the context of a flipped classroom, which ushered in the inevitable arrival of a new normal in part because of this global pandemic, COVID 19. Recent LMSs contain clever algorithms that automatically recommend courses based on a user's ability profile and extract metadata from various learning resources to produce such effective recommendations, improving s their accuracy. One of the various resources available to instructors to aid in teaching and learning is FSUU Learn. Furthermore, due to its numerous and versatile instructional elements, it is promising even after the epidemic and in face-to-face learning. The purpose of this study is to determine the elements that affect faculty and teachers at the university's acceptance of the LMS FSUU Learn as well as whether their usage of ICT affects their acceptance of other LMS FSUU Learn features during the peak of pandemic. The results revealed that the actual usage is 67% while the behavioral intention to use the FSUU Learn is 56.8%. The variables used in the study were able to predict 62% of the variance that could explain the acceptance of the FSUU Learn based on the perception of the faculty/teacher users. © 2023 IEEE.
ABSTRACT
OBJECTIVE: This study aimed to compare the attention levels, of Turkish children and adolescents with Attention Deficit/ Hyperactivity Disorder (ADHD) in on-line education classes with healthy controls. METHODS: This study is a cross-sectional, internet-based, case-control study that recruited 6-18 years old patients diagnosed with ADHD and receving treatment and healthy controls from eight centers. The measurements used in the study were prepared in the google survey and delivered to the participants via Whatsapp application. RESULTS: Within the study period, 510 children with ADHD and 893 controls were enrolled. Parent- rated attention decreased significantly in both groups during on-line education classes due to COVID-19 outbreak (p<0.001; for each). Children and adolescents with ADHD had significantly elevated bedtime resistance, problems in family functioning difficulties than control children according to parental reports (p=0.003; p<0.001; p<0.001, respectively). Furthermore, bedtime resistance and comorbidity significantly predicted attention levels in on-line education. CONCLUSION: Our findings may underline the need to augment student engagement in on-line education both for children without attention problems and those with ADHD. Interventions shown to be effective in the management of sleep difficulties in children as well as parent management interventions should continue during on-line education.
ABSTRACT
COVID-19, or SARS-CoV-2, is an extremely deadly virus that is responsible for over half a million deaths of people in the world. This virus originated in China in December 2019 and rapidly spread worldwide in 2-3 months, and affected every part of the world. Its life-threatening nature forced governments in all countries to take emergency steps of lockdown that affected the entire world's education, health, social and economic aspects. Due to the implementation of these emergencies, the population is facing psychological, social and financial problems. Additionally, this pandemic has significantly influenced the health care systems as all the resources from governments of all countries were directed to invest funds to discover new diagnostic tests and manage COVID-19 infection. The impact of the COVID-19 pandemic on the education and social life of the population is described in this article. Additionally, the diagnosis, management, and phytoremedia-tion to control the spread of COVID-19 and traditional medicinal plants' role in managing its mild symptoms have been discussed.Copyright © 2023 Bentham Science Publishers.
ABSTRACT
Online learning has been present since the 1960s and has risen in popularity over time. World-class universities have been using online teaching-learning methodologies to fulfill the needs of students who reside far away from academic institutions for more than a decade. Many people predicted that online education would be the way of the future, but with the arrival of COVID-19, online education was imposed upon stakeholders far sooner and more suddenly than expected. When the COVID-19 pandemic broke out, educational institutions began to explore digital ways to keep students studying even when they couldn't be together in person as governments enacted legislation prohibiting large groups of people from gathering for any reason, including education. The future of such a transition looks promising. However, transitioning from one mode of education to another is not easy. Historically, when educators adopt new tools, learning still continues in the conventional manner. Based on the responses of 176 students, this paper studies the challenges of Digital transformation in the Education sector. The research is extremely beneficial in evaluating the scope of societal opposition to change. © 2022 IEEE.
ABSTRACT
Study buddy is an all-in-one package for your studies. We provide an automatically generated timetable as well as pdfs and video links all for Free for our students of class 10th, 12th and, those appearing for JEE. The aim is to increase job opportunities for college students with expertise in a particular subject as well as retired teachers to help school students divide their time and stay motivated. Kids nowadays are always stressed during exam days because they can't make a schedule to study. So we wanted to bring an end to those days of anxiety and fear. We decided to make a website, "Study Buddy"where a student will have the following 4 options: a) Generate Time table b) Reference Material c) Discussion Forum d) Mentor Help.In our research, we would walk through the above-mentioned points and also compare how our proposed method and software package stands out from the already existing software. This will be followed by our system design, result and future scope. This software is a buddy for the students during their hard times. It will not only help the students but will also provide an employment opportunity to retired teachers and college students. We also plan to implement machine learning in order to understand the basic trend of student psychology during exams. © 2022 IEEE.
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Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario. © 2013 IEEE.
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Online learning and training continue gaining momentum worldwide resulting in the reduction of the traditional form of face-to-face education with its temporal and spatial limitations. Online education improves access to education and training, as witnessed during the Covid-19 pandemic. This article focuses on online education adoption in Spain. A representative survey on ICT use in households conducted annually by the Spanish National Institute of Statistics is used to construct a panel database for the years 2008–2020. The first objective is to provide an econometric model for adopting online education using this panel data. Next is to measure the effects of relevant observable individual socioeconomic variables on adoption. A Heckman selection model allows for estimating the impact of gender, age, education, digital skills, habitat, and income. The article also measures the effects of Covid-19 in 2020 on different population groups. The drivers and impediments have the expected signs and plausible sizes. The paper concludes with policy recommendations and suggestions for further research. © 2023 Elsevier Ltd
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The COVID-19 pandemic prompted a rapid shift to online learning at universities, leading to an acceleration of changes directed at creating more inclusive education models. The goal of this research is to explore various aspects of online learning patterns, including students' online behavior, and attitude towards online communication. The study incorporates qualitative and qualitative data analysis. Based on 1562 survey responses from Polish and Ukrainian students, it has been found that there are still differences in digital competencies between men and women, which may be rooted in traditional gender roles. The analysis of students' attitudes towards online education also identified both positive and negative aspects of this form of learning, providing insight into areas that could be improved. The main research limitation stems from the interpretative nature of the findings, which have restricted generalization power. The research findings may be useful in shaping future educational policies at Polish and Ukrainian universities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Access to technology is essential to educational success in today's digitized society, but disparities in access to technology can handicap students. This study examines to what extent this digital divide exists among underserved students in online instruction during COVID-19 and in their adoption of free Technology Loaner programs. Focusing on underserved students that are characterized by their generational status, minority background or low income, we predict that underserved college students will show lower levels of technology access and higher levels of free technology adoption than their counterparts. However, the quantitative analysis of survey data (n=258) collected from a U.S. minority-serving university provides mixed, surprising results. Follow-up analysis of qualitative data from 10 interviews offers us further insights and partial explanations for these unexpected results. Our study suggests that individual background should be considered in designing a policy to mitigate digital divide and enhance student learning in online education. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
Coronavirus first appeared in January 2020 and has spread dramatically in most parts of the world. In addition to exerting enormous impacts on public health and well-being, it has also affected a broad spectrum of industries and sectors, including transportation. Countries around the world have imposed restrictions on travel and participation in activities due to the outbreak of the virus. Many countries have adopted social distancing rules requiring people to maintain a safe distance. Therefore, the pandemic has accelerated the transition into a world in which online education, online shopping, and remote working are becoming increasingly prevalent. Every aspect of our life has witnessed a series of new rules, habits, and behaviours during this period, and our travel choices or behaviours are no exception. Some of these changes can be permanent or have long-lasting effects. To control this situation, these changes must first be recognised in various aspects of transportation in order to provide policies for similar situations in the future. In this regard, this study seeks to examine how transportation sectors have changed in the first waves of the pandemic. Iran has been selected as the case study in this paper. This research is divided into two parts. The first part focuses on the effects of the Coronavirus pandemic on rural transportation in Iran. This is followed by assessing the impacts of the virus on urban transportation in Tehran (the capital of Iran). The behaviour of more than 700 travellers in terms of trip purpose, travel time, and mode choice is evaluated using a questionnaire. Results indicate that the number of passengers has reduced dramatically in rural transportation systems. In such systems, considerations such as keeping social distancing, disinfection of passengers and their luggage, and unemployment of a group of personnel working in the transportation industry have been more evident. In urban transportation, education trips have dropped the most. This might relate to an increase in online teaching and health concerns. The same pattern can be seen in the passengers who used bicycles, public taxis, and other public transportation systems. Finally, during the pandemic, drivers' speed has increased, which justifies the need for traffic calming for drivers. © 2022 Warsaw University of Technology. All rights reserved.
ABSTRACT
In the case of COVID-19 epidemic, online education supported by computing technology is playing an increasingly important role, while online education resources, especially micro-lectures, are seriously insufficient, which greatly hinders the development of online education. In this paper, a micro-lecture resource construction scheme for online courses with teacher-student collaboration was proposed based on the learning pyramid theory. The practice proved that this scheme can make full use of students' technical foundation in the Internet era to build micro-lectures, which can not only improve the quality of online courses, but also build curriculum resources quickly and with high quality, thus providing a strong resource guarantee for the follow-up online teaching. © 2022 Association for Computing Machinery.
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Due to the global spread of COVID-19, the world's educational institutions had been ordered to close. As a direct result of this, the time-tested method of acquiring knowledge by visiting classes is gradually being replaced by online education. In virtual classrooms, teachers had difficulty detecting student postures and determining whether or not students were comprehending the material. This research suggests using a computationally efficient method based on computer vision and machine learning to determine the attention levels of e-learning students. The method extracts characteristics using HoG and SIFT. Using K-means and PCA, the resulting feature vector is optimized for dimension reduction. The attentiveness is classified using the classifiers Decision Tree, KNN, Random Forest, and SVM. Random Forest yielded the best accuracy at 99.2% with a dataset of 15000 images. © 2022 IEEE.