Augmented, Virtual and Immersive Reality as Learning Support in TEC21 Educational Model

Virtual, augmented, and immersive reality opens a world of possibilities in education by allowing students to recreate authentic situations, such as operating machinery, assembling a product, or training tool handling, to mention a few. In the TEC21 educational model, the core is the challenge: A project with a real-world challenge assigned by the training partner results in students offering solution proposals.The trigger that accelerated the development of virtual, augmented, and immersive reality activities in distance learning was COVID-19 confinement. During this, these technologies recreated the laboratory and its facilities' learning through augmented reality (AR) and virtual reality (VR) experiences.Using these technologies in the classroom allows students to achieve a great learning experience and develop skills for postgraduate studies and professional futures.Furthermore, now that we have returned to our physical facilities and laboratories, we can accelerate the learning obtained at the training partners' facilities, recreating processes and machinery through these immersive technologies and a hybrid experience for our students.The present research shows the activity learning design process and the statistical treatment of the data to provide continuous feedback during the activity;we examine the three transcendental variables in the educational process: The learning (academic rigor), the development of competencies, and the involvement or immersion of the students in the classroom. © 2023 IEEE.

The New Normal: Student Preferences for Online Advising Before, During, and After the Pandemic

Learning advisers have traditionally been reluctant to conduct online appointments. We compared proportional student use of online appointments over the course of the pandemic, and relative student satisfaction between online and in-person appointments in 2021. After the initial lockdowns eased and students were able to choose their preferred appointment type, online appointments have remained popular on an ongoing basis, although in-person appointments are slightly preferred overall. There was no difference in student satisfaction between modes.

Impact of academic stress by parent-proxy on parents' learning-support-services: a moderated-mediation model of health anxiety by parents' educational level

PurposeThis study focused on parents' health anxiety by proxy about their children when they started learning online during the COVID-19 pandemic, to explore the impact of academic stress by parent-proxy on parents' learning support services with the mediating role of health anxiety by parent-proxy and the moderating role of parental educational level.Design/methodology/approachIn total, 8,940 primary school students' parents participated in the study. Bootstrapping was performed to test the constructed model.Findings(1) Academic stress by parent-proxy positively predicted health anxiety by parent-proxy. (2) Health anxiety by parent-proxy significantly positively predicted learning support services. (3) Academic stress by parent-proxy also significantly positively predicted learning support services. (4) Academic stress by parent-proxy positively predicted parents' learning support services through the mediating effect of health anxiety by parent-proxy. (5) Parental educational level moderated the relationship between academic stress by parent-proxy, health anxiety by parent-proxy, and learning support services. Academics and parents will benefit from the conclusions of this study in both theory and practice.Originality/valueDuring the COVID-19 pandemic, offline learning has been replaced with online learning, which has brought with it many physical and mental health problems, including additional academic stress. Most studies on learning support services have focused on offline learning. However, this study explored the relationships between academic stress by parent-proxy, health anxiety by parent-proxy, learning support services, and parental educational level in the context of online learning. Results show that it is necessary to pay attention to academic stress and health to provide children with appropriate learning support services.

IoTeach:Learning Support System for IoT Programming by Integrating Real Devices and Sequential Contents

In recent years, Internet of Things(IoT) has become popular, the requirements for sharing the operation/mechanisms of IoT devices, such as Arduino and M5Stack are increasing. Moreover, owing to the coronavirus pandemic, many educational institutions have adopted online lectures, such as on-demand classes and online classes using video conference systems. For IoT programming education, these methods have challenges, such as a lack of linkage with real-world devices and source codes. In this study, we propose a system called "IoTeach", which supports the learning of IoT programming by attaching a scripting language to sequential contents, such as videos and slides shared on the Web. The IoTeach can link videos and slides with real-world IoT devices and source codes. We describe the concept and implementation of the system in this study. © 2023 Owner/Author.

Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage

Machine Learning for ligand based virtual screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

Classification And Errors Analysis of COVID-19 Using Supervised Machine Learning Algorithm and Visualisation

During pandemic many people died as a result of the covid-19 sickness, which appeared in 2019 and spread over the world. The objective of research work is to wards the occurrence of COVID to improve classification accuracy and threshold curve predictions on real-life dataset for Receiver Operator Characteristics (ROC) value. This paper goals the real-life COVID patients from the five countries to test the experiment. The proposed methodology involves of two steps;used Weka for calculating the accuracy by applying Decision Table machine learning classifier and compare the results of all the five countries, secondly, the improvement in ROC value in terms of initial care predictions by area under ROC analysis. For our COVID dataset has 209 instances and 16 attributes, Weka has performed on the number of training instances are 184, number of Rules applied is 20, search direction has been applied in forward direction, total number of subsets evaluated is 96, merit of best subset found is 82.609 and time taken to build model is 0. 06 seconds. One advantage of our suggested mode list hat it keeps the original data intact, ensuring experiment quality. A further advantage is that the model can be used with additional data sets to produce the highest accuracy and ROC analysis out comes.Copyright © 2022, Anka Publishers. All rights reserved.

Machine Learning Models Towards Prediction of COVID and Non-COVID 19 Patients in the Hospital's Intensive Care Units (ICU)

The global proliferation of COVID-19 prompted research towards the virus's detection and eventual eradication. One important area of research is the use of machine learning (ML) to realize and battle COVID-19. The goal of this study is to use machine learning to monitor COVID and non-COVID-19 patients and decide whether or not to transfer them to the intensive care unit (ICU). The precise disease diagnosis was essential due to the lack of oxygen supplementation in the majority of hospitals around the world. It will improve the effectiveness of the ICU facilities and lessen the load on the medical personnel and the ICU facilities by accurately forecasting how patients will be treated. If stable patients are recognized among all patients, home treatment could be established for stable patients. In this research, three machine learning algorithms were chosen as the method used, which are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Extra Tree Classifier. These algorithms were chosen for their simplicity and robustness and based on the conducted literature review. A dataset containing 100 ICU and 131 stable patients of Covid and non-Covid samples from 24th Moscow City State Hospital was used. By using SMOTE technique with 10-fold cross-validation and feature selection on the dataset, KNN achieved an accuracy of 94.65%, SVM with an accuracy of 94.65%, and an accuracy of 96.18% for the Extra Tree Classifier. The outcomes of this research on the selected dataset prove how accurate these algorithms were able to predict the classes © 2022, Mathematical Modelling of Engineering Problems.All Rights Reserved.

Online support for students with diverse learning needs at an inclusive private school in South Africa

Teachers of diverse classes often include different forms of support in their teaching to accommodate the varying learner needs, while some benefit from the services of Learning Support Educators (LSE) who manage the learning needs of students who experience barriers to learning. With the onset of lockdown due to the coronavirus pandemic, the shift to online teaching and learning left students with diverse learning needs feeling adrift and on their own to grapple with the change, without prior preparation. As teachers also had to adapt to the shift to online teaching, the diverse learner needs became a secondary concern to them. The LSE were also unable to offer support in the traditional form due to physical distancing and lockdown rules. This paper explores possible ways in which teachers of diverse classes and LSEs can assist learners with diverse needs to succeed in their online learning. The study followed action research methodology that was conducted by an LSE who case-managed seven learners in a Johannesburg private school with inclusive teaching practices. The qualitative research study explored new ways to support diverse learners in their online learning by finding possible alternatives to the challenges that online learning posed for them. Observations, self-reflections, and information collected from students, their teachers and parents, were analysed using content analysis technique to propose possible workable solutions. Checking in on learners and their parents regularly during lockdown and giving them strategies for coping was found to be helpful. Teachers who had previously incorporated ‘blended' teaching in their practice were found to be generally more collaborative: they varied their teaching, had their students produce better results, were able to include online inclusive activities and groupwork activities in their teaching in order to avoid learner boredom, and they were found to be giving quality feedback to the learners and their parents. © The Author(s) 2023.

A Comparative Study of Supervised Machine Learning Algorithms for Fake Review Detection

The behavior of shopping has shifted into online shopping. Especially after Coronavirus Disease of 2019 (COVID-19), people choose online shopping rather than going to the market for economic and hygienic reasons. Reviews help the seller to make customers trust their products, but since some sellers are not honest, they use fake reviews to help boost their products. Fake reviews are commonly generated randomly by a computer bot or someone not using the product. Some researchers are already working on fake review detection to help this problem using many methods. In this paper, we compared three supervised machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). By preprocessing the data and using the Term Frequency-Inverse Document Frequency (TF-IDF) feature, we begin the experiment process without tuning. We apply the tuning parameters to each algorithm for the other experiments using 5-fold cross-validation. The result showed that SVM algorithms outperform the best algorithms of the three before and after tuning, with 88.89% and 89.77%, respectively. © 2022 IEEE.

A Model based on Deep Learning for COVID-19 X-rays Classification

Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy. © 2023, Modern Education and Computer Science Press.

Improved Accuracy in Speech Recognition System for Detection of COVID-19 using Support Vector Machine and Comparing with Convolution Neural Network Algorithm

The objective of the research aims to detect Covid-19 patients by innovative speech recognition using a Support Vector Machine (SVM) and comparing accuracy with Convolutional Neural Network (CNN). Speech recognition using SVM is considered as group 1 and Convolutional Neural Network is considered as group 2, where each group has 20 samples. A T-test with 95% CI, G-power of 80%, and alpha=0.05 was used to compare the two sets of data. CNN achieves an accuracy of 87.5% and SVM achieves an accuracy of 92.5% with significance value 0.043 (P<0.05). Covid-19 prediction using an innovative speech recognition using SVM achieves significantly better accuracy than CNN. © 2022 IEEE.

Measuring Student's School Engagement in Blended Learning Model

The development of technology in the last decades made blended learning implemented wider, especially in the covid-19 pandemic last years. Online learning activities has some new issue to be defined. One of them is the difficulty of school engagement (disengagement). School counselors have the role of supporting student development tasks to avoid student disengagement. This research aims to measure the students' school engagement, especially in the blended learning context. This research design used a quantitative survey. Research subjects include students in the city of Malang. The instrument used is the school engagement inventory. The study results indicate that students with mid-levels of school engagement have slightly more numbers than students with high levels of school engagement. Support engagement is a finding in this study as the specific aspect of engagement in the blended learning model. The suggestion is a more practical assessment of the demographic conditions of students to test and get a more appropriate construct of support engagement. © 2022 IEEE.

University-assisted community schools as partners in neighborhood revitalization efforts

Urban policymakers, city officials, and community residents utilize neighborhood revitalization initiatives to establish safe and empowered neighborhoods. In 2016, leaders in Columbus, Ohio, launched a neighborhood revitalization effort designed to improve safety, access to opportunities, and economic development in the historically underserved Linden neighborhood. A priority focus involved strengthening Linden schools through the development of two university-assisted community schools (UACS). Using the community collaboration model as a guide, leaders from the schools, university, nonprofit, and local government sectors partnered to support school improvement processes in two Linden K-6 elementary schools. Annual stakeholder surveys have demonstrated marked improvements in perceptions of neighborhood safety, school climate, and the overall learning support system. The prevalence of behavioral incidences among students has decreased. Further, during the COVID-19 pandemic, the UACS model helped sustain student engagement and virtual learning and keep families connected to the schools. This article describes implementation outputs and evaluation outcomes associated with adopting the UACS model in these two Linden elementary schools. Findings contribute to a greater understanding of how UACS can serve as partners in neighborhood revitalization efforts. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

"Wearing a High Heel and a House Shoe at the Same Time": Parents' Information Needs While Navigating Change in their Child's Behavioral Care

Change is an inevitable part of a parent's role, whether due to their child's development, family life, or external events. To understand the information needs of parents navigating change, we studied the effects of the COVID-19 pandemic as a widely experienced disruption in the lives of parents and children. We interviewed 16 parents about their caregiving experience as the COVID-19 pandemic collapsed boundaries between home, school, and work. In particular, we asked about adjustments to behavioral care, or the social learning, supports, and interventions through which children develop social and emotional skills. We focused on parents of children already receiving accommodations and behavioral support from their school, to understand how disruptions in these services affected the role of the parent in meeting their child's individual needs. Applying role theory and the Kübler-Ross change curve, we describe the coping mechanisms that parents used to navigate the stages of change, as well as the information needs that remained unmet, despite their efforts. We discuss how practitioner-initiated and parent-centered supports can be designed around the lived experience of change, by accommodating a parent's capacity to accept and use help at different stages. © 2022 ACM.

Understanding the Indonesian EFL Zen Students' Synchronous Engagement in a Research Course

Synchronous learning engagement has been studied during the pandemic Covid-19. However, few studies investigated Zen participants' engagement in particular courses. The study aims to disclose the EFL Zen students' devotion to synchronous instructional research courses during the disruption phase. The study focuses on the students';miscellaneous preferences, commitments, technological learning support, topic searching reports (TSR), and research proposals submitted (RPS). The design is a survey study with descriptive qualitative and quantitative data. 24 students were purposively chosen as total samplings. Three instruments were employed to take data;Google forms (GF), electronic observation, and reflection. The 24 participants answered questions four times in a semester through GF. The data were analyzed in descriptive and parametric statistics. The result shows that the participants changed their time preferences due to changing habits and practical reasons. Google Meet and Zoom have caused different impacts and raised various insights. The majority engaged technologically at the basic level. This study also reveals the poor quality of the participants in presenting some essential parts of a thesis proposal. They faced various difficulties in presenting sections of their RPS. The statistics test shows a significant positive correlation between TPR and RPS (r =0.862, p<0.01). It is concluded that the EFL Zen students perform their optimum engagement and learning achievement despite being supported by sophisticated technology. This research implies that teachers need various scenarios to employ online platforms and charge more creative activities to harvest optimum learning outcomes. © 2022, Pegem Egitim ve Ogretim Dergisi. All Rights Reserved.

Development of a Japanese language learning support system for international students using video contents

In recent years, globalization has progressed, and Japan's international students have increased. However, many international students study while working part-time, and due to the impact of Covid-19, face-to-face conversation with people has become difficult. Therefore, regular study time alone has become insufficient for practicing the Japanese language. On the other hand, there are video distribution services that have become popular in recent years. Therefore, we thought we could create a language learning support system by using them. This research aims to develop a language learning support system that uses the subtitle function of a video distribution service to improve learning motivation and to solve the lack of time to learn a foreign language(the Japanese, in this case). This paper mainly reports on the development of the system by using those video content. © 2022 IEEE.

i-Comments: On-screen Individualized Comments for Online Learning Support

With the rapid development of digitization in education, online learning has become an alternative and promising way to enable flexible learning scenarios. Especially after COVID-19, online learning draws more and more attention. However, online learning has inherent disadvantages and problems. One of them is that learners have difficulty in concentrating, and the other one is that they may feel lonely during lessons. In this study, a new model of on-screen individualized comments, namely i-Comments, is proposed for online learning support. In the proposed model, three elements, i.e., timing, content, and quantity of i-Comments, are defined respectively, which are used to help learners improve concentration and decrease the feeling of loneliness. Furthermore, an experiment is designed to evaluate the model and verify whether the proposed model can help learners gain a better learning experience and improve their learning effectiveness. © 2022 IEEE.

Artificial Intelligence in the secondary education: the paradigm of Edu4AI

Technology Enhanced Learning (TEL) and Artificial Intelligence can substantially maximise the student learning experience and support students by acquiring both technical and traversal life skills. The COVID-19 pandemic in the last two years has accelerated the introduction of new methods and tools supplementing traditional practices. In this paper we present the methodology posed within the European funded project Edu4AI “Artificial Intelligence and Machine Learning to Foster 21st Century Skills in Secondary Education” to introduce artificial intelligence in secondary education curricula, based on the interdisciplinary cooperation of educational and technical partners from four European countries exploring how user-friendly and mostly graphical environments together with simple engaging pilot projects can be used for this purpose. Furthermore we present the criteria, selection methodology and description of these pilot projects and give an outlook for planned future work. © 2022 Copyright for this paper by its authors.

Supporting Student Learning Needs in Tertiary Education: Institutional Support Structures Based on the Institutional Support Questionnaire.

This article presents and focuses on the Institutional Support Questionnaire (ISQ) that was developed and validated to complement the Learning Needs Questionnaire (LNQ). While the LNQ, validated and published earlier, assessed students' perceived learning needs, the ISQ assesses students' psychological perspectives of their institution, particularly how they perceive their institution supports their learning. Both questionnaires work in tandem to support resource optimisation efforts in establishing targeted academic support structures within teaching-focused tertiary institutions. This study found that the 42-item ISQ had adequate psychometric properties and that institutional support could be represented by four factors (i.e., academic competency support, teaching practices, tutors' characteristics, and use of technology in instruction) that reflected in large part the factors characterised by the LNQ (i.e., perceived academic competency, time management, preferred tutors' characteristics, and use of technology). Practical applications of the use of both the ISQ and LNQ (i.e., how both could be applied in a tertiary education setting to identify perceived students' learning needs and whether an institution is providing adequate support to meet these needs) and limitations on their use are discussed.

Remote teaching practices and learning support during COVID-19 lockdowns in Portugal: Were there changes across time?

The COVID-19 pandemic challenged countries, regions, schools, and individuals. School closures due to lockdowns forced changes in the teaching practices and the learning support provided to children at home. This study aimed to provide insights on the changes between the first and the second lockdowns in Portugal, concerning remote teaching practices and family support to children's education. A self-report questionnaire was filled by 144 parents of third grade students. The results show that, between the two lockdowns, there was a significant decrease in the amount of support provided at home to school assignments and activities, as well as in the amount of time spent by students in TV broadcasted lessons and in reading training supported by the family. Inversely, families reported a significant increase in the amount of time spent by students in independent reading activities and in the time spent in training reading guided by teachers. The number of synchronous lessons with a teacher and the number of times students trained reading during a synchronous lesson also increased in the second lockdown. Additionally, in the second lockdown, parents perceived synchronous lessons to be more effective at improving their child's reading skills and perceived themselves as more capable of supporting their child in reading acquisition. These findings are used to discuss school responses and remote teaching and learning practices during the COVID-19 pandemic.