Effect of Distant Education on Children's Academic Achievement in Primary Schools During Corona Pandemic in Dubai Emirate- Field Study

This study aimed to examine the effect of distance education on the level of educational achievement of children during the Corona period in ten primary schools in the Emirate of Dubai. To achieve the objectives of the study the researchers adopted the descriptive analytical approach. The quantitative method of data collection had been applied using the electronic questionnaire tool consisted of four main axes for data collection and had been distributed to the study sample consisted of 190 students' parents and administrators selected by using simple random techniques. The results of the study indicated that the participation of students in the educational process, and in the establishment of appropriate educational programs and applications for the transmission to distance learning have contributed to reducing the negative effects of the process of shifting from traditional education / face-to-face education classroom teaching to virtual classroom (ZOOM).The study recommended the need for strengthening distance education mechanisms, which contribute in developing the student's interests, tendencies, attitudes, concentrating on the study material, and using of safe and secured electronic devices to increase the search for additional information to reach the correct knowledge. Also, the school administration should have good e-learning plan ahead with required financial credits that will help in overcoming the crisis and mange distance learning processes to reach future objectives successfully. © 2022 Owner/Author.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Investigating Factors That Influence College Students' Online Continuous Learning Intention during COVID-19 Epidemic

This study aims to explore university students' continuous intention toward online learning during COVID-19 pandemic. A total of 120 students enrolled in online learning were surveyed to collect their perception of an extended model by adding task value to the expectation-confirmation model. Structural equation modeling was employed to verify the hypotheses proposed in this study. The results indicated that task value and technology usefulness were significant predictors of students' continuous intention toward online learning. More specifically, technology usefulness had a direct impact on students' continuous intention, while students' perceived task value played an indirect role in the prediction of their continuous intention. However, the impacts of both confirmation and satisfaction were not statistically significant on students' continuous intention. The results suggest that practitioners and researchers should pay special attention to the technological usefulness of online learning environments and task value, especially task value, in order to enhance students' retention of online learning. This study would contribute to implications to better design and implement online learning. © 2023 IEEE.

Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

Detection and Classification of Pneumonia Using Deep Learning by the Dense Net-121 Model

A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.

Online Threats vs. Mitigation Efforts: Keeping Children Safe in the Era of Online Learning

As the COVID-19 pandemic resulted in school closures since early 2020, children have spent more time online through virtual classrooms using educational technology (EdTech) and videoconferencing applications. This increased presence of children online exposes them to more risk of cyber threats. Here, we present a review of the current research and policies to protect children while online. We seek to answer four key questions: what are the online threats against children when learning online, what is known about children's cybersecurity awareness, what government policies and recommendations are implemented and proposed to protect children online, and what are the proposed and existing efforts to teach cybersecurity to childrenƒ Our study emphasizes the online risks to children and the importance of protective government policies and educational initiatives that give kids the knowledge and empowerment to protect themselves online. © 2023 IEEE.

Nurturing Social Presence in a Blended Learning Environment

The ambition of this development study is to explore the opportunity to put the knowledge gained during the COVID-19 pandemic into practice in a blended, post-COVID, learning environment. The focus is to explore how a combination of digital and face-to-face activities may allow for fostering social presence among undergraduate students. The Social Presence model and the five elements of Affective Association, Community of Cohesion, Instructor Investment, Interaction Intensity, and Knowledge and Experience, encompass the theoretical framework of the study. The contextual setting is the first course of The Marketing Programme at Linnaeus University in Sweden, a bachelor program with a 50% Swedish intake and 50% international intake. Given the diverse background of the students in this course, challenges are typically encountered in relation to community building. Empirical data was collected during the fall of 2022 among the enrolled students using an online questionnaire. While the results from this study should be seen as preliminary, they offer an inspiring glimpse of how to nurture social presence in a blended learning environment. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

LLDSNet: Limited labeled data segmentation network for COVID-19 CT lesion segmentation

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.

A Survey of Human-Computer Interaction Technology in Intelligent Home for Children's Learning

The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.

Hybrid Learning: A Boon or Bane

The context of higher education is changing. The emerge of social, technical, and intellectual forces has pushed higher education to the point of a significant transformation (Garrison & Vaughn, 2008). Technology enhanced learning have raised concerns about the quality of education and learning environment. For the traditional classroom-based teaching and learning, the breakthrough came during the emerge of Covid-19 pandemic. Online learning, once a separated learning system, was fully integrated into teaching and learning to continue providing education amidst the lockdown. Post the reopening of higher education institutions, hybrid learning was widely implemented in almost all universities across the world, to accommodate students' diverse range of learning needs in the post pandemic era. This case study is intended to gain insights regarding the learning experiences, challenges, and benefits in hybrid learning from both the lecturers' and students' perspective. Based from the gathered qualitative data, results show that both students and lecturers have mixed reviews regarding hybrid learning experience. One of the main findings is that hybrid learning creates a more flexible, engaging learning environment compared to traditional face-To-face learning. Lecturers generally feel that hybrid learning has several pedagogical and technological challenges. However, issues concerning quality of lecture delivery and academic malpractice during online assessments has found to be a concern among lecturers and students. In overall, lecturers and students feel that hybrid learning needs to be evaluated from time to time to address the drawback for continuous improvement towards better quality of learning. © 2023 IEEE.

Assistance tools for the evaluation of machine learning algorithm performance: The decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort

MIDRC was created to facilitate machine learning research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the COVID-19 pandemic and beyond. The purpose of the Technology Development Project (TDP) 3c is to create resources to assist researchers in evaluating the performance of their machine learning algorithms. An interactive decision tree has been developed, organized by the type of task that the machine learning algorithm is being trained to perform. The user can select information such as: (a) the type of task, (b) the nature of the reference standard, and (c) the type of the algorithm output. Based on the user responses, they can obtain recommendations regarding appropriate performance evaluation approaches and metrics, including literature references, short video tutorials, and links to available software. Five tasks have been identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event analysis, and (e) estimation. As an example, the classification branch of the decision tree includes binary and multi-class classification tasks and provides suggestions for methods and metrics as well as software recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. The decision tree has been made publicly available on the MIDRC website to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, estimation, and time-to-event tasks. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Distance Teaching Experience of Campus-based Teachers at Times of Pandemic Confinement

Amidst the outbreak of the coronavirus (COVID-19) pandemic, distance education, where the learning process is conducted online, has become the norm. Campus-based programs and courses have been redesigned in a timely manner which was a challenge for teachers not used to distance teaching. Students' engagement and active participation become an issue;add to that the new emerging effects associated with this setup, such as the so-called "Zoom fatigue", a term coined recently by some authors referring to one's exhaustion feeling that stems from the overuse of virtual meetings. In realising this problem, solutions were suggested in the literature to help trigger students' engagement and enhance teachers' experience in online teaching. This study analyses these effects along with our teachers' experience in the new learning environment and concludes by devising some recommendations. To attain the above objectives, we conducted online interviews with six of our teachers, transcribed the content of the videos and then applied the inductive research approach to assess the results. © 2022 Owner/Author.

A Quantitative Analysis of Different Learning Strategies for Senior High School Students in the 'New Normal'

Along with hitting certain regions of the country, the COVID-19 outbreak substantially impacted all academic institutions, prompting the traditional classroom structure to be adjusted immediately. Governments have shifted to a virtual learning environment to alleviate separation from educational activities and boost involvement. The primary objective of this research is to examine the different learning techniques used by senior high school students at a Philippine University when they engage in online learning. This study investigates the underlying pedagogies and instructional designs employed in the production and delivery of online courses. Numerous challenges, including infrastructure and school readiness for a rapid transition to distant education, would develop due to the rapid transformation in education. The researchers employed a descriptive technique in conducting this study and set survey questions to collect data from respondents. The non-probability sampling approach is used in this study, and the results are analyzed using a 5-point Likert scale to determine the mean and standard deviation. The study's results indicate that although students are dissatisfied with the online setting due to its performance differences from the traditional approach, they believe the course material to be fascinating and relevant for the future. It is also indicated that the online learning materials have a significant impact and are convenient to their education. The researchers recommend that institutions arrange asynchronous and synchronous sessions throughout the week and that institutions pay more attention to course design. Finally, students should investigate the school's potential for online instruction. © 2023 IEEE.

SignalR based Real-Time applications in education

Before Covid, we introduced our own classroom response system to improve the effectiveness of our teaching. To this end, we adopted an open-source technique, SignalR, which provides a framework for building real-time web applications. Overnight, due to the emergency situation starting in 2019, education was moved to the virtual space. Both students and professors had to learn how to teach or learn using only online facilities, without a testing period. During the emergency, a synchronous online teaching mode was required by our university, so the choice was made to use Microsoft Teams, implemented with SignalR for real-time functionality. After the emergency, we were all happy to have our 'old life' back and return to our personal teaching style, but is it possible, is it possible to continue teaching in the same way as before Covid-19 - is it possible to step into the same river twice? Students have become accustomed to convenient, modern, digital options during the online education period and now that we are back in school, they insist that we continue to use the new tools. In this essay, we want to describe the changes in students' attitudes that we can usefully build on in the future and that will influence the further development of our project. © 2023 IEEE.

Effectiveness of digital resources for cloned Linear Algebra courses at the undergraduate level

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.

Design to Improve Social Presence among University Students in Asynchronous Online Learning Environment

Since the COVID-19 pandemic, online lectures are becoming more common in higher education. Specifically, asynchronous online classes have become increasingly popular because of their flexibility. Asynchronous online courses, however, may negatively impact students' academic performance and social development due to the diminished sense of social presence. To explore ways to enhance social presence among students in asynchronous online classes, this paper used a co-design methodology that involved 12 undergraduate students as primary stakeholders. As a result, we developed a design framework for designing in-class interaction to promote social presence in asynchronous online lectures. This framework consists of four high-level elements and sub-categories: interaction topic (direct or peripheral topics related to learning), interaction size (small or entire group), interaction mode (anonymity, synchronicity, instructor involvement), and interaction motivator (lightweightness and entertainment). Our design framework may serve as a guide to future technology for improving asynchronous online classes. © 2023 Owner/Author.

Intelligent Prediction-Intervention approach to Support Students' Success in Web-based Learning Environments: A Case Study in Higher Education

Due to the COVID-19 pandemic, the demand for distance learning has significantly increased in higher education institutions. This type of learning is usually supported by Web-based learning systems such as Massive Open Online Courses (Coursera, edX, etc.) and Learning Management Systems (Moodle, Blackboard-Learn, etc.). However, in this remote context, students often lack feedback and support from educational staff, especially when they face difficulties or challenges. For that reason, this work presents a Prediction-Intervention approach that (a) predicts students who present difficulties during an online learning course, based on two main learning indicators, namely engagement and performance rates, and (b) offers immediate support to students, tailored to the problem they are facing. To predict students' issues, our approach considers ten machine learning algorithms of different types (standalone, ensemble, and deep learning) which are compared to determine the best performing ones. It has been experimented with a dataset collected from the Blackboard-Learn platform utilized in an engineering school called ESIEE-IT in France during 2021-2022 academic year, showing thus quite promising results. © 2022 IEEE.

Work in Progress: A Virtual Educational Robotics Coding Club Framework to Improve K-6 Students Emotional Engagement in STEM

Educational robots allow students deepen their knowledge of mathematics and scientific concepts. Educational Robotic coding clubs provide a learning environment for K-6 students that promotes coding through STEM digital literacy. Students in educationally disadvantaged families may not have the educational and financial capital to engage in STEM learning. Closures of schools and afterschool services during the COVID-19 pandemic increased this digital divide. This research proposes a framework for delivering a virtual robotic coding club in an educationally disadvantaged community. The framework develops young people's emotional engagement in STEM through robotic coding. Synchronous online classes were delivered into family homes using Zoom. Results demonstrate that children achieved emotional engagement as reported through high levels of enjoyment and increased interest after participating in the programme. The research shows promise in increasing children's STEM skills and knowledge, and in improving positive attitudes towards STEM for children and parents. © 2023 IEEE.

Promoting College Student's Systems Thinking

This study examined how college students in a medical school in China engaged in learning in asynchronous online learning environments during the COVID-19 health crisis. A quasi-experimental design approach was employed to compare if a class of students had better learning outcomes and developed systems thinking when asynchronous discussion forums incorporated an inquiry-based pedagogical approach in one unit, whereas the other unit followed a traditional instructor-led approach. In sum, 25 junior students participated in this study. Quantitative results show that the students had statistically significant higher assessment scores and improved systems thinking when the unit incorporated the inquiry-based pedagogical approach. Qualitative findings also demonstrated how students engaged in learning and how the instructor scaffolded students' inquiries and learning. Practical implications for instructors' teaching online courses are also discussed. © The author(s) 2023.

A Case Study on Student Experiences of Social VR in a Remote STEM Classroom

Extended reality (XR) technologies continue gaining traction in multiple higher education contexts. As XR becomes more commercially accessible to students and universities, its convenience for educational purposes presents a renewed potential for exploration. Due to Covid-19 restrictions, there is also a growing interest in cross-platform, socially orientated software for remote educational practices. However, the precise role of XR technologies and how they contribute to student experiences of remote learning, particularly the unique affordances of social virtual reality (VR) for evoking an embodied sense of presence, is relatively unknown. Based on real-world experiences, we present a case study on a social VR intervention in a remote higher education classroom to inspire Human-Computer Interaction (HCI) researchers to investigate further the issues that arise from our practice-based research. Our motivations were to report, analyze, and summarize everyday virtual learning environment (VLE) challenges, identify design considerations for VLE technologies, and comment on social VR's utility in delivering Science, Technology, Engineering, and Mathematics (STEM) subjects in a remote setting. We apply a practical approach to investigate and identify potential HCI problems, capture the unique experiences of STEM students during the lockdown, and explore the effects of tutorial activities that give students agency in constructing VLEs. The findings of this student-focused case study draw attention to the design of social VR activities that support conventional, web browser-based VLEs. © 2023 Owner/Author.