E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

Evaluation of a Video Generation Method Linking Dance and Scenes

Owing to the impact of COVID-19, the venues for dancers to perform have shifted from the stage to the media. In this study, we focus on the creation of dance videos that allow audiences to feel a sense of excitement without disturbing their awareness of the dance subject and propose a video generation method that links the dance and the scene by utilizing a sound detection method and an object detection algorithm. The generated video was evaluated using the Semantic Differential method, and it was confirmed that the proposed method could transform the original video into an uplifting video without any sense of discomfort. © 2023 SPIE.

ICT Transformation in Education: Its Impact on Language Teachers' Professional Identities

Rapid and continuous changes in digital technologies have changed both classroom practices and teacher profiles in education. It can be argued that a new context of teaching may lead some teachers to develop a different teacher identity in order to meet the needs of the era. Within this perspective, this case study attempts to explore the impacts of Information and Communication Technologies (ICT) revolution in education on teachers' professional identity through the lens of three English instructors from three different contexts in Turkey. The study particularly focuses on reflections of teachers during the pandemic. As a theoretical framework, the study adopts Wenger's (1997) social theory of learning and, within this framework, it discusses these teachers' professional identities in relation to their ICT usage. In particular, three modes of belonging, Engagement, Imagination and Alignment, are underlined. A qualitative approach is employed based on the written history documents of the participants and semi-structured interviews as data collection tools. The findings are gathered with a deductive thematic analysis, and they illustrate that teachers have some external and internal difficulties regarding their ICT usage, and they form a new shape of professional identity mainly through collaboration, community expertise and contributing new ideas in their school contexts. Although the use of new digital technologies mostly enables them to adopt a positive and modern teacher identity in their teaching contexts, it also leads some of them to sometimes question their teacher identity due to their limited ICT knowledge and competence. Thus, the study suggests some implications both for language teachers to invest in their digital identities, and for school administrations to create a friendly atmosphere where the community of expertise can be shared freely among teachers.

Impact of Teachers' Support Strategies on Students' Engagement of Online College English Class Experiences

During the covid-19 pandemic, students' online learning quality is imbued with teachers' support strategies while students' learning engagement is another great indicator underlies their learning experiences. Through a questionnaire survey of 500 freshmen who have had their college English class online in 2022 fall, an investigation using exploratory factor analysis, Pearson correlation analysis, stepwise regression analysis and parallel mediator model reveals the impact of teachers' support strategies (the six dimensions of challenge, authentic context, curiosity, autonomy, recognition and feedback) on the learners' online college English learning engagement (the four dimensions of cognitive engagement, behavioral engagement, emotional engagement, social engagement), thus particular concern is also given to the correlation with students' online learning experiences. It was found that even under diversified and comprehensive guiding strategies from teachers, university students' online college English learning engagement is at the medium level, among which the cognitive engagement should be devoted more. The experimental data also shows that teachers' support strategies have significant influence on learners' engagement, especially teachers' feedback and challenge setting will stimulate students to involve more in their study. In addition, both teachers' support strategies and students' learning engagement involves significant reflection of learning experiences accordingly. Based on this learning concept, related proposals see different degrees of prominence reflected in online instructional design, teachers' and students' feedback literacy, and technology-enabled innovative teaching practice are put forward, in order to effectively play the role of teacher scaffolding, learning experiences enrichment and students' engagement enhancement of online English learning. © 2023 IEEE.

Chinese Normal Students' Satisfaction of Emergency Remote Learning in Higher Education during COVID-19 Pandemic

Despite the widespread use of emergency remote learning (ERL) during the COVID-19 pandemic in higher education, little is known about the determinants of Chinese normal student satisfaction with ERL. This study uses a questionnaire survey method to examine how Chinese normal students' satisfaction with ERL during the COVID-19 pandemic. The results show that Chinese normal students prefer face-to-face teaching to online teaching to some extent. According to the findings, it is important to emphasize students' pre-class preparation, adjust course assessment methods, change teachers' teaching strategies, create a positive teaching environment, boost students' learning confidence, and help them deal with their anxiety during ERL to improve the online course experience for Chinese students at normal universities. © 2023 ACM.

Interdisciplinary Teaching Reform of Financial Engineering Majors Based on the Analytic Hierarchy Process in the Post-Pandemic Era

In the post-epidemic era, the labor market has become increasingly complex, making it even more crucial to incorporate sustainability into employment demand. As we enter the post-pandemic era, a globalization trend has become more apparent. It is crucial to modernize employability through educational reform in order to assist employees in enhancing their professional skills. This study began by analyzing the importance of financial engineering practice instruction and graduate employability in the post-epidemic era. Second, the study proposed the content and a plan for inter-disciplinary teaching reform to address talent cultivation needs based on labor market requirements. Third, a face-to-face survey and interview were conducted with students affected by changes in teaching, and the results were analyzed and summarized. On this basis, the impact of education reform was evaluated using both the expert scoring method and the analytic hierarchy approach. The results indicated that the suggested financial engineering teaching reform program improved the school's discipline strength, enrollment rate, employment rate, and competition awards, especially discipline strength. This research can be used to inform the teaching of financial engineering majors in various countries, assist job candidates in enhancing their professional skills, and build a formidable talent pool for the labor market.

Research on the Relationship between Teacher Expectations and College Students' Online English Learning Engagement

As a critical influencing factor of learning engagement, teacher expectation plays a vital role in ensuring the quality of online teaching under COVID-19. This paper investigates the relationship between teacher expectations (three dimensions of teacher support, teaching interaction, and academic feedback) on students' online English learning engagement (three dimensions of cognitive engagement, behavioral engagement, and emotional engagement) in e-learning through a questionnaire survey of 513 college students. Pearson correlation analysis and multiple regression analysis were applied as research methods. The results manifest that college students' online English learning engagement was above average, but emotional engagement needs improvement. In addition, teacher expectations of teaching interaction positively and significantly predict English e-learning engagement. Based on this, the article puts forward suggestions on the future of online teaching from the aspects of online teaching design, feedback quality of teachers and students, innovative teaching practice of technology empowerment to effectively play the role of teachers as scaffolding and improve the effectiveness of online English teaching. © 2023 IEEE.

The Sociological Role of Empathy in the Classroom

Teaching during a global pandemic has prompted many discussions about how faculty can best support students and create classrooms where deep learning and engagement occur. In this conversation, we argue there is a role for empathy in college classrooms. We present data from interviews with faculty at a small, Midwestern, teaching-focused university during the fall of 2020. We map these perspectives onto the empathy paths framework and suggest that the therapeutic and instrumental paths are most useful for understanding empathy in the classroom. We also discuss why it is important for faculty to think about empathy and the role sociology can play in these conversations. Finally, we present a series of empathetic practices individual faculty can incorporate into their pedagogy and structural supports that departments and universities can provide to help faculty engage in empathetic practices in the classroom.

Navigating old and new terrains of academic practice in higher education: indelible and invisible marks left from the Covid-19 lockdown

Higher education has been (re)shaped by the Covid-19 pandemic in ways which have left both indelible and invisible marks of that period. Drawing on relevant literature, and informed by an exchange catalysed through a visual narrative method, authors from four European universities engage with two reflective questions in this article: As academics, what were our experiences of our practice during the lockdown periods of the Covid-19 pandemic? What might we carry forward, resist or reimagine in landscapes of academic practice emerging in the post-Covid future? The article explores how academics experienced and demonstrated resilience and ingenuity in their academic practice during that turbulent time. Particular insights include entanglements of the personal and professional, and the importance, affordances and limitations of technology. In addition, the authors reflect on some of the ongoing challenges exacerbated by the pandemic, such as education inequalities. The article concludes by reprising the key points about what marks are left behind in the post-Covid present, and how these relate to the future in which relational pedagogy and reflexivity are entangled in the ways in which we cohabit virtual and physical academic spaces. [ FROM AUTHOR] Copyright of London Review of Education is the property of UCL Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

Recent progresses in rapid detection of novel coronavirus SARS-CoV-2

The global pandemic coronavirus pneumonia (COVID-19), the disease infected by the new coronavirus (SARS-CoV-2), is extremely contagious. It is mainly spread among people through respiratory droplets, aerosols, direct or indirect contact, fecal-oral transmission, and cold chain transportation. Especially, patients who are in the incubation period or have no obvious symptoms already have the ability to infect others. SARS-C0V-2 is a positive-sense single-stranded RNA virus, with a single linear RNA segment. Each SARS-CoV-2 virion is 60-140 mm in diameter. Like other coronaviruses, SARS-CoV-2 has four structural proteins, known as the spike (S), envelope(E), membrane (M), and nucleocapsid (N) proteins. To date, a variety of detection methods for the SARS-CoV-2 have been developed based on the virus structural basis and 'etiological characteristics, which would provide an effective guarantee for the diagnosis of COVID-19 patients and the control of the epidemic. In order to help for the early diagnosis and prevention of COVID-19, the pathogenic characteristics and recent progresses of detection base on nucleic acid, immunology and biosensors of the SARS-CoV-2 are reviewed in this paper.

Four Legged Parallel Manipulator for Autonomous Delivery Robot

The COVID-19 outbreak has been designated a pandemic and is spreading quickly around the world. The industries most impacted by COVID-19, which has proved a barrier to every major business, were the e-commerce businesses that use door-to-door delivery methods. It's critical to have an unmanned strategy that can be applied to diverse sites during this key time. Although the driverless vehicle is not a novel idea, problems can occur when these systems run into the uneven pavement or unexpected obstacles. The methods for ensuring the stability of the commodities delivered by autonomous robots are discussed in this research. This mechanism guards against product damage. Additionally, a motor that stabilizes a robot's product compartment uses a gyroscope sensor to detect angular rotation and axial movement and preserve the orientation of a quadrupedal leg. In order to conduct trials that mimic problems in the real world, rectify errors, and offer solutions, a prototype model of a robot's stability platform has been created. This type of technological advancement will aid us in future efforts to combat global catastrophes. © 2023 IEEE.

Video-Based Online Interactive System Using CNN Segmentation Method

In recent years, the COVID-19 has made it difficult for people to interact with each other face-to-face, but various kinds of social interactions are still needed. Therefore, we have developed an online interactive system based on the image processing method, that allows people in different places to merge the human region of two images onto the same image in real-time. The system can be used in a variety of situations to extend its interactive applications. The system is mainly based on the task of Human Segmentation in the CNN (convolution Neural Network) method. Then the images from different locations are transmitted to the computing server through the Internet. In our design, the system ensures that the CNN method can run in real-time, allowing both side users can see the integrated image to reach 30 FPS when the network is running smoothly. © 2023 IEEE.

Evaluating the Federal Innovative Assessment Demonstration Authority: Early Implementation and Progress of State Efforts to Develop New Statewide Academic Assessments. Evaluation Report. NCEE 2023-004

Education officials have long hoped that the statewide academic assessments most students take each year could be used not only for accountability but also to guide instruction. Congress established the Innovative Assessment Demonstration Authority (IADA) program in 2015 to help address this goal, offering up to seven states temporary flexibility from federal testing requirements so that they may more easily make progress toward replacing their current assessments with more innovative ones. The key incentive to participate in IADA is that students trying out the innovative assessment are not required to also take the state's current assessment. However, states approved for IADA must still show that their innovative assessments meet most requirements for federal accountability, and they are expected to implement the new assessments statewide within 5 years. This report describes the progress of the first five assessment systems approved under IADA in order to help policymakers consider expanding the program to more states. The report is primarily based on an analysis of states' IADA applications and performance reports to the U.S. Department of Education through the 2020-2021 school year and is part of a broader evaluation of IADA required by Congress. [For the Appendix, see ED627873. For the Study Highlights, see ED627880.]

Improved method for COVID-19 Classification of Complex-Architecture CNN from Chest CT volumes using Orthogonal Ensemble Networks

This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance. © 2023 SPIE.

DIBR–DOMBI–FUZZY MAIRCA Model for Strategy Selection in the System of Defense

Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–"DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model's sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.

Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction

This study proposes a graph convolutional neural networks (GCN) architecture for fusion of radiological imaging and non-imaging tabular electronic health records (EHR) for the purpose of clinical event prediction. We focused on a cohort of hospitalized patients with positive RT-PCR test for COVID-19 and developed GCN based models to predict three dependent clinical events (discharge from hospital, admission into ICU, and mortality) using demographics, billing codes for procedures and diagnoses and chest X-rays. We hypothesized that the two-fold learning opportunity provided by the GCN is ideal for fusion of imaging information and tabular data as node and edge features, respectively. Our experiments indicate the validity of our hypothesis where GCN based predictive models outperform single modality and traditional fusion models. We compared the proposed models against two variations of imaging-based models, including DenseNet-121 architecture with learnable classification layers and Random Forest classifiers using disease severity score estimated by pre-trained convolutional neural network. GCN based model outperforms both imaging-only methods. We also validated our models on an external dataset where GCN showed valuable generalization capabilities. We noticed that edge-formation function can be adapted even after training the GCN model without limiting application scope of the model. Our models take advantage of this fact for generalization to external data. © 2023 IEEE.

Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.