Impact of e-Learning in Education Sector: A Sentiment Analysis View

The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.

Demystifying and Analysing Metaverse Towards Education 4.0

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word 'Metaverse' especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education. © 2023 IEEE.

The Relationship of SEDS, Food Intake and Work Engagement among Telecommuting Employees of the Education Sector during the COVID-19 Pandemic: A Mixed Method Study

Due to COVID-19, the shift to telecommuting became a widely used work set-up to maintain economic balance. This work set up is associated with risks to employees' wellness. As prevention to the risks, employees must be provided with ways to understand the telecommuting attributes. In relation, this study targets in understanding the links between the socio-economic demographic status, work engagement, and food intake of the education sector's tele-employees. The 110 samples are gathered from the Senior High school Department using convenience sampling, an online survey, and the mixed method. ANOVA and multi-linear regression are used as statistical treatments. The study found that the older generation with higher Income is more likely linked with higher work engagement. The younger generation, low-income earners, and males are inclined more toward unhealthy foods as compared to their counterparts. Low-income earners perceived that their work engagement falls under the category that energy to work is at a bare minimum level. The participants' education attainment revealed significance with energy-giving or carbohydrate-source foods. The qualitative data highlighted job position was perceived with a link to food intake and work engagement. Unhealthy food consumption is perceived with a beneficial association with work engagement, although it is suggested for further investigation. With these findings, the education sector's stakeholders, nutrition, mental health professionals, and future researchers would mainly benefit from this study for intervention generation. © 2023 ACM.

Hybrid Education and Institutional Readiness

Progress is unidirectional by default. The global irreversible shift witnessed in the education sector with the COVID-19 pandemic left teaching and learning far from the same. The current study aims to consider global trends in hybrid education by exploring existing hybrid study models and recognizing institutional readiness by means of hybrid initiatives successfully adopted by institutions. Being conceptual research, the methodology of the current study is based on an extensive review of literature, related to hybrid education, selected carefully from the wealth of academic publications comprising scholarly articles, research journals, webpages, conference proceedings, dissertations, thesis, and authentic material related to the study. A conceptual framework represents the relationship between the research variables. Founded on the qualitative analysis of the data gathered, the study concludes that, while integration of technology in teaching is important, it does not imply total restructuring of what and how we teach. Flexibility in teaching, learning, and testing must become the basis for current pedagogy in meeting the goals of hybrid education. The implications of the current study are proffered to all stakeholders in education along with the recommendations for future research. © 2023 IEEE.

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.

Baseline Estimation in Face Detection for AI Proctored Examinations through Convoluted Neural Networks

The COVID-19 pandemic took the world by surprise, and everything came to a halt. The education sector had to adjust accordingly by shifting to online learning. If the online delivery experience was overall successful, assessment integrity becomes questionable as examinees still manage to circumvent the anti-plagiarism mechanism put in place. In this paper, we propose an artificial intelligence solution using face and head pose detection to estimate the neutral position of the examinee which will form the basis to detect any suspicious behavior. The resulting implementation achieved a 97% accuracy when detecting the examinee in the frame and a 98% accuracy when there are multiple faces detected. © 2023 IEEE.

Application of Cloud Computing in an Education Sector through Education and Learning as a Service and its Cost Benefit Analysis

An education sector across the globe is facing numerous challenges. It is being one of the most badly affected sectors due to Covid-19. This paper presents a perspective on applying Cloud Computing technologies in the field of education at several ion levels. In addition, it proposes Education and Learning as a Service model and Decision-Making Matrix for an Organization. © 2023 IEEE.

Implementation of Data Analytics and Machine Learning in Thailand Education Sector

Since the global epidemic of the coronavirus disease 2019 (COVID-19) over the past few years, Thailand education sector has been affected by the requisites for a digitization system and distance education. This sudden change has affected the quality of learning and statistical evaluations in the long term. Consequently, data analysis and categorization in learning quality assessment are critical for predicting the number of future students and learning performance after the COVID-19 outbreak. However, vast data analytics might be applied to the education sector in many aspects. In addition, machine learning can influence the categorization of students that are useful for analyzing the performance of different educational systems. Therefore, this study reviews the perspective and usability of data analytics and machine learning that influences current situations in Thailand education sector © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Shrinking the digital divide in online learning beyond the COVID-19 pandemic: A Systematic Literature Review

The emergence of the COVID-19 pandemic disrupted many businesses, including the education sector. As the World Health organization (WHO) urged numerous countries worldwide to implement lockdowns and social distancing laws to curb the spread of the pandemic, higher education institutions responded by embarking on online learning. This article aims to use the systematic literature review to review articles on reducing the digital divide in online learning during the COVID-19 pandemic. Using the technology-organisationenvironment (TOE) model, the benefits and challenges profiled will aid in proffering a way forward in handling and managing the digital divide beyond the COVID-19 pandemic in online learning. Using the keywords: digital divide, COVID-19 and online learning to search from online databases, the inclusion criteria resulted in 10 articles being reviewed. The articles revealed that: students faced many technological challenges in connecting to online learning during the COVID-19 epidemic, students need to be properly trained to be able to use online learning systems and they need to subside data packages for many students to afford the data. The study concludes and recommends that the TOE model can be used to elucidate the effects of the digital divide in online learning. The technological, organisational and environmental factors can help proffer a sustainable online learning model for the future, beyond COVID-19 pandemic. © 2022 IEEE.

Reshaping Teaching-Learning Process During COVID – 19 Pandemic

The education sector and the students are the ones who have been largely affected by the spread of the global pandemic. The sudden outbreak of the coronavirus has taken an enormous emotional and psychological toll on people. Across the world, people had to face various problems starting from very simple daily life necessities to education, work-related problems. In the domain of education, adaptability came along with a set of challenges and took some time to settle. The transition in the teaching-learning process across the world has been truly undeniable. Like all other things, even this new mode of imparting education has its own set of benefits as well as drawbacks. Since the outbreak of COVID-19, a lot of research has been going on in understanding the kind of psychological impact it causes and how the students are impacted due to reshaping the teaching-learning process. As per UNESCO 2020 report, greater will be the risk of children losing out on their youth, and future due to the closure of the educational institutes. Adequate training and assessment are required for the teachers to adjust to the new technology-based learning. This article tried to analyze the transition in the teaching-learning process and its effect on students and how is it affecting their quality of life as well as we also shed some light on how it has shown some positive aspects and has proved to enrich the learning process amidst the adversities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Engagement Estimation using Time-series Facial and Body Features in an Unstable Dataset

The global education sector has been deeply shaken by COVID-19 and forced to shift to an online teaching model. However, the lack of face-to-face communication and interaction in online learning is critical to high-quality teaching and learning. Research on engagement is a crucial part of solving this problem. Because engagement is of time-series data with an ongoing change, research datasets used for engagement analysis need a certain preprocessing method to capture time-series related engagement features. This research proposed a novel deep learning preprocessing method for improving engagement estimation using time-series facial and body information to restore traditional scenes in online learning environments. Such information includes head pose, mouth shape, eye movement, and body distance from the screen. We conducted a preliminary experiment on the DAiSEE dataset for engagement estimation. We applied skipped moving average in data preprocessing to reduce the influence of the extracted noises and oversampled the low engagement level data to balance the engaged/unengaged data. Since engagement is continuous and cannot be captured at a particular instant in time or single images, temporal video classification generally performs better than static classifiers. Therefore, we adopted long short-term memory (LSTM) and Quasi-recurrent neural networks (QRNNs)sequence models to train models and achieved the correct rate of 55.7% (LSTM) and 51.1% (QRNN) using the original key points extracted from OpenPose. Finally, we proposed the optimization structure network achieved the engagement estimation correct rate of 68.5% in proposed LSTM models and 64.2% in QRNN models. The achieved correct rate is 10% higher than the baseline in the DAiSEE dataset. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Application of Deep Learning on Student Attendance Checking in Virtual Classroom

Due to the situation of the COVID-19 outbreak, the importance of both virtual and hybrid conferences has unexpectedly increased, especially for the education sector. Additionally, the virtual classroom is the only method to make the education system grow while the COVID-19 outbreak situation is currently spreading. However, class attendance checking is a time-wasting problem for various online classes, which have more than 50 students. This research aims to help the course instructor by adapting the use of intelligent technologies in order to save the time that has been spent on the class attendance checking method. The deep learning techniques together with automatic screen-capturing mechanisms have successfully been designed and developed with Python, Flask, Sqlite, Html, CSS, and Javascript. Therefore, our proposed approach for attendance checking can automatically check the attendance of the enrolled students that belong to that particular class. In addition, this research uses front-face photos to train a deep learning model and deal with face recognition and image analytics for the student attendance checking methods. Therefore, the highlight of the system is the ability to take attendance automatically and continuously throughout the learning period. As a result, the use of our proposed system efficiently saved the attendance checking time and the proposed deep learning model has practically achieved acceptable accuracy at 91.77. © 2022 IEEE.

Inadvertent Use of ICTs in the South African Higher Education Sector During the Pandemic: Benefits and Challenges

Globally, higher education (HE) is under pressure to include more diverse groups of students and produce the skills needed to adapt to a rapidly changing world. One of the key catalysts which has been touted to drive the changes is information communication and technologies (ICTs) but the uptake has not been to the optimum levels especially in developing countries. However, after the COVID-19 pandemic, HE sectors, in South Africa particularly, have experienced an increase in ICT uptake for the purpose of responding to educational needs. This paper examines how harnessing the positives of ICT's could benefit the higher education sector in the post-COVID-19 era. In particular, the paper explores the benefits and challenges of ICTs in catering for educational needs within higher education during the COVID-19 pandemic. We used information systems (IS) continuation theory to move from academic theorising about inadvertent use of ICT tools toward harnessing their continued successful implementation. Documents were analysed using thematic analysis. The results showed several positive potentials from ICT uptake such as self-regulated learning and the saving of the academic calendar as well as student lives in the face of any future pandemics. This paper suggests a continued maintenance of the infrastructure growth model necessitated by the pandemic. Furthermore, we propose that the Department of Higher Education and Training must regulate establishment of ICT infrastructure funding in institutions of higher education. © 2022 IEEE.

Online Learning and Students' Ethical Behavior During Covid-19: For Better or for Worse?

With Covid-19 limiting social interaction to prevent the spread of viral infections, the education sector in Indonesia, 'forced' schools and higher education to carry out online learning. However, there is a concern that online learning may affect the students' ethical behavior. As there is no human interaction, students are getting used to not meeting people or socializing, so they tend to ignore ethical norms in socializing. The purpose of the study is to find out if students' ethical behavior is related to interpersonal and communication skills. The study used a quantitative method with a sample of 132 active students at the Creativepreneurship Study Program using purposive sampling. The findings showed online learning supports communication and interpersonal skills, but unable to form students' ethical behavior. Online learning behavior, online courses require definite objectives, inner motives, synchronous feedback, and independence of the learners. Even though there are rules regarding ethical behaviour, the rules themselves are not able to influence the student's ethical behavior. Online learning and students' behavior is not only the responsibility of the lecturer, but also the responsibility of the students and their parents. © 2022 IEEE.

Big Data: A lifeline to Next Generation Online Teaching Strategies for Universities

Amidst the recent COVID-19 pandemic, the education industry was forced to completely flip its very traditional self to an online system in order to thrive and ensure continuity of operations. This moved academics and support staff to rely on system-captured data to monitor performance and plan for the future. The purpose of this research is to study the importance of big data (captured by university systems over time) and its crucial role in policy & strategy making in the higher education sector. 10 pilot interviews were conducted with education industry practitioners which indicated that awareness regarding the importance of big data analysis exists, however, there is a need of in-depth knowledge to better support this sector. The results indicate the current state/status of implementation and challenges faced by the higher education sector wherein a clear need of having a data management strategy for optimizing the use of big data is being identified. © 2022 IEEE.

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning. © 2022 IEEE.

Students' Perspective on Sustaining Education and Promoting Humanising Education through e-Learning

The COVID-19 pandemic has shifted the education sector towards an e-learning approach to sustain education. Sustaining student education through e-learning significantly impacts student learning experiences and outcomes, which can be influenced by e-learning infrastructure, e-learning materials, and learning engagement. The concept of humanising education refers to the learning process that reflects students' moralities and values. Focus group discussion is a practical approach to assess students' perspectives on sustaining and humanising their education through e-learning. This qualitative focus group study aimed to discuss the e-learning factors that will sustain education and promote humanising education in the virtual learning environment. Thirty students from Information Technology (IT) and business fields have participated and provided a different view on sustaining education through e-learning. Thematic analysis was used to analyse the focus group data. Five themes were identified: (1) e-learning technologies and infrastructure;(2) e-learning principles: pedagogy and materials;(3) health and wellness;(4) equality;and (5) engagement: communication and collaboration. The analysis enlightened the e-learning design for sustainable e-learning. In addition, this paper outlines how the findings support sustainable development goals. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Blockchain Application in Incentivizing Students on Participation in Online Classes

The world we know has changed over a brief period, with the ascent and spread of Covid-19. This affected the education sector resulting in offline classes to online classes. Technologies made it easy with numerous websites, materials, video lectures, courses, and techniques for the students. In this situation, the main problem arises with students not participating in the class having many reasons such as illness, being introverted, and feeling that they may be wrong. If we are not interested in something to talk about or are shy, we must face it so that we will make the best out of it. People remember the things they listen to carefully, so we can probably study less if we listen. In this research paper, we proposed a Blockchain based application, so students who are going to attend online classes would be able to participate more in the class. They will be able to gain incentives that are based on crypto currency and by using those cryptocurrencies they can spend it on fees or any other resources in the university which would be beneficial for students. They will be able to gain incentives that are based on cryptocurrency and by using those crypto currencies they can spend it on fees or any other resources in the university. We are using the most popular technology which is Block chain technology to make sure that students who are attending online classes will be able to pay more attention. Also, we are using the most famous functionality of Blockchain which is incentivization. To give rewards to the students we will be using incentivization so they can pay more attention to the classes. This design is beneficial for teachers also to look at the status of each student and get in contact with them. © 2022 IEEE.

Feature Selection Mechanism for Attention Classification using Gaze Tracking Data

The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time. © 2022 IEEE.

A novel approach to preventing SARS-CoV-2 transmission in classrooms: A numerical study

The education sector has suffered a catastrophic setback due to the ongoing COVID pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5 × 3 × 5 m3 classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian-Lagrangian framework. Based on the stochastic dose-response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected and (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission, reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks has also failed to provide adequate protection against infection, reducing the infection risk only by 50%. Furthermore, the shields serve as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. The manifestation of infection risk in the domain was investigated, and it was found out that in the case of screens the maximum infection risk reached the value of only 0.2 (20% infection probability) in 1325 s. © 2023 Author(s).