ABSTRACT
Statistical literacy is key in this heavily polarized information age for an informed and critical citizenry to make sense of arguments in the media and society. The responsibility of developing statistical literacy is often left to the K-12 mathematics curriculum. In this article, we discuss our investigation of K-8 students' current opportunities to learn statistics created by state mathematics standards. We analyze the standards for alignment to the Guidelines for the Assessment and Instruction in Statistics Education (GAISE II) PreK-12 report and summarize the conceptual themes that emerged. We found that while states provide K-8 students opportunities to analyze and interpret data, they do not offer many opportunities for students to engage in formulating questions and collecting/considering data. We discuss the implications of the findings for policy makers and researchers and provide recommendations for policy makers and standards writers.
ABSTRACT
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.
ABSTRACT
This article summarizes a research whose general objective was to analyze the way in which the documentary corpus associated with the "Learn at home" strategy reproduces the relations of power, control, social-educational inequality and exclusion in its recipients. The units of analysis were organized in textual visualization matrices with double coding: one open, cross-coded and the other using NVivo v.12 software. Subsequently, the main lines of inquiry were categorized and an inductive categorical interpretation was carried out, relating the categories discourse and society with social knowledge as an interface. The findings indicate that the discursive structures analyzed reproduce power, control, inequality and exclusion, maintaining the status quo, prolonging educational social injustice and privileging symbolic elites;furthermore, the issuers resort to discursive strategies such as the principle of influence, values and praise to achieve the purposes of social domination. As for the research design, this was a qualitative documentary research, of discourse analysis type, in critical perspective from the socio-cognitive approach
ABSTRACT
With rapid proliferation of digitalization and compulsion by COVID-19 pandemic, learning formats have been changing from face-To-face to online. Online education enables learners to take courses from anywhere, anytime, but it can also cause some problems for learners who struggle to maintain motivation. In addition, for STEAM education, it is important to engage in hands-on activities, but the ongoing pandemic has made it difficult for students to gather in one place to perform such activities. Incorporating gamification into online education can potentially motivate students and make STEAM education more interactive. On this premise, we have developed PhyGame as a learning system to help high-school students learn Physics. The system includes common game elements such as badges and leaderboards, and interactive simulation of Physics concepts embodying game-like charm. It also includes three modes of learning that allow students to adjust the difficulty according to their own learning levels, and a function that automatically saves learning log. For evaluation, PhyGame was used by students (N=23) at a high school in central Tokyo. The students rated the system on a scale of 1 to 10, and the main results are as follows: (1) Using PhyGame made learning enjoyable (mean score: 7.74);(2) PhyGame provided a good UI/UX (mean score: 7.83);(3) The overall experience with PhyGame was satisfactory (mean: 7.00). Our evaluation results show that interactive and gamified learning systems like PhyGame have a positive impact on user engagement and motivation. © 2023 IEEE.
ABSTRACT
The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation - recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good - here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect. © 2023 Owner/Author.
ABSTRACT
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.
ABSTRACT
The development of laboratory practices is necessary for training mechatronics engineering students because they must learn in scenarios that allow checking the theories reviewed in class and implementing their solutions to real-world challenges posed in a course. Unexpectedly, the COVID pandemic caused a rethinking of how to develop the laboratory as a form of teaching, looking for online alternatives using simulation platforms, portable instruments, and 3D printing to design prototypes. This work presents the experience of two online laboratory practice activities in two mechatronics engineering subjects, which allowed students to complement their training without the risk of contagion, develop the planned competencies, and acquire skills in this form of teaching. © 2023 IEEE.
ABSTRACT
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.
ABSTRACT
The COVID-19 pandemic has changed the way that universities teach and how students learn. Operating system is the basic course of computer, software engineering, big data technology and other majors in colleges and universities, and occupies a very important position in the cultivation of computer categories. In the process of online teaching of the Linux application part of the operating system course, the teaching team explored the online teaching mode of the practical course and summarized the experience of the online teaching of the practical course. © 2023 IEEE.
ABSTRACT
This case study describes a usability testing course in which students learn by practicing several evaluation methods. The on-campus format makes it possible for teachers and students to meet to discuss recorded test sessions and students can observe other students' execution of pilot studies conducted on campus. The COVID-19 pandemic placed new demands on this course. In-person activities were avoided by some students and many test participants. Some student teams tried remote usability testing. Interestingly, screen recordings (with sound) of the test sessions show that remote testing sometimes helped the students focus more on observation and less on (inappropriately) guiding the test subjects. Another effect was that the students found it easier to recruit participants than during the previous years when the university was teeming with students, lecturers, and non-academic staff. However, the recruited participants were often notably limited to the students' circles of friends. © (2022) by Association for Information Systems (AIS) All rights reserved.
ABSTRACT
Human resources is a crucial factor in supporting the development of tourism as a labour-intensive industry. This research enhances the understanding of China's tourism education associated with the spread of COVID-19 and its implications for tourism recovery. Initial findings imply that: COVID-19 had a profound lagging negative effect on the intention to apply for tourism-related majors of examinees, which is severe challenging for tourism recovery, and the impact was more pronounced in typical tourism-dependent cities than in non-tourism-dependent cities. The MICE Economics and Management was least affected, while the Sports Tourism was most affected. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
To assess the status of its performance based on expectations and feedback specifically from the educators who are users of Fr. Saturnino Urios University's (FSUU) learning management system (LMS). The researchers investigated and undermined gaps in training and learning using the analytical report. The finding showed that LMS's purpose is not only to deliver online education but also to provide a wide range of services like acting as a platform for online courses and content and learning activities both asynchronous and synchronous instructions. An LMS may provide classroom management as well as facilitation in the perspective and paradigm of higher education with an instructor-led training system or in the context of a flipped classroom, which ushered in the inevitable arrival of a new normal in part because of this global pandemic, COVID 19. Recent LMSs contain clever algorithms that automatically recommend courses based on a user's ability profile and extract metadata from various learning resources to produce such effective recommendations, improving s their accuracy. One of the various resources available to instructors to aid in teaching and learning is FSUU Learn. Furthermore, due to its numerous and versatile instructional elements, it is promising even after the epidemic and in face-to-face learning. The purpose of this study is to determine the elements that affect faculty and teachers at the university's acceptance of the LMS FSUU Learn as well as whether their usage of ICT affects their acceptance of other LMS FSUU Learn features during the peak of pandemic. The results revealed that the actual usage is 67% while the behavioral intention to use the FSUU Learn is 56.8%. The variables used in the study were able to predict 62% of the variance that could explain the acceptance of the FSUU Learn based on the perception of the faculty/teacher users. © 2023 IEEE.
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Join us for a conversation with three Chicago-based Chief Information Officers. Learn how each has navigated the disruptions and changes associated with COVID-19 pandemic at their institutions and how their IT departments are faring under new conditions of remote and hybrid work. We'll look at the biggest challenges facing their institutions today and how they approached possible solutions. Meet our CIOS below. © 2023 Owner/Author.
ABSTRACT
Artificial intelligence leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers decision making of clinicians. Starting from data (medical images, biomarkers, patients' data) and using powerful tools such as convolutional neural networks, classification, and regression models etc., it aims at creating personalized models, adapted to each patient, which can be applied in real clinical practice as a decision support system to doctors. This chapter discusses the use of AI in medicine, with an emphasis on the classification of patients with carotid artery disease, evaluation of patient conditions with familiar cardiomyopathy, and COVID-19 models (personalized and epidemiological). The chapter also discusses model integration into a cloud-based platform to deal with model testing without any special software needs. Although AI has great potential in the medical field, the sociological and ethical complexity of these applications necessitates additional analysis, evidence of their medical efficacy, economic worth, and the creation of multidisciplinary methods for their wider deployment in clinical practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic was the main reason why many organisations decided to include information and communication technologies in their processes to allow them to continue with their activities, be it providing services to users (food, medicine, etc.), training/education or disseminating culture. In the field of culture, some museums incorporated technology into their operating environment, moving from face-to-face visits to virtual visits. However, in many museums, the lack of apps designed to solve the problem of virtual visits caused some to stop receiving visitors during the pandemic. In this context, this paper describes the development of an application with a user-centred design that incorporates extended reality to allow virtual visits to the Remigio Crespo Museum in the city of Cuenca (Ecuador). The evaluation carried out to verify the application's usability and learnability is also included. The results obtained indicate that users/visitors found the application usable and easy to learn. © 2022 IEEE.
ABSTRACT
Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc. © 2023 IEEE.
ABSTRACT
One of the common practices during the COVID-19 pandemic is to work or study from home. This study aims to reexamine the factors affecting individual continuance intention of e-learning. During the pandemic, via a survey conducted in 2022, we assessed workers' continuance intention of e-learning from different sectors in Taiwan. This research brought motivations as mediators in continuance intention to e-learning. Through the statistical analysis, we identified the mediation effect of motivations based on the self-determination theory. The results show that autonomous motivation facilitates the learners' computer self-efficacy, the quality of the system and content toward continuance intention;controlled motivation could mediate the monetary award in influencing the continuance intention. The internalization of motivation is also an effective mediator. The obtained results not only add new knowledge of what affected the continuance intention of e-learning during the pandemic but also provide guidance for employers to allocate resources to boost e-learning after the pandemic. © 2023 IEEE Computer Society. All rights reserved.
ABSTRACT
This paper focuses on an important problem of early misinformation detection in an emergent health domain on social media. Current misinformation detection solutions often suffer from the lack of resources (e.g., labeled datasets, sufficient medical knowledge) in the emerging health domain to accurately identify online misinformation at an early stage. To address such a limitation, we develop a knowledge-driven domain adaptive approach that explores a good set of annotated data and reliable knowledge facts in a source domain (e.g., COVID-19) to learn the domain-invariant features that can be adapted to detect misinformation in the emergent target domain with little ground truth labels (e.g., Monkeypox). Two critical challenges exist in developing our solution: i) how to leverage the noisy knowledge facts in the source domain to obtain the medical knowledge related to the target domain? ii) How to adapt the domain discrepancy between the source and target domains to accurately assess the truthfulness of the social media posts in the target domain? To address the above challenges, we develop KAdapt, a knowledge-driven domain adaptive early misinformation detection framework that explicitly extracts rel-evant knowledge facts from the source domain and jointly learns the domain-invariant representation of the social media posts and their relevant knowledge facts to accurately identify misleading posts in the target domain. Evaluation results on five real-world datasets demonstrate that KAdapt significantly outperforms state-of-the-art baselines in terms of accurately detecting misleading Monkeypox posts on social media. © 2022 IEEE.
ABSTRACT
Ahstract-SARS-CoV-2 virus has long been evolving posing an increased risk in terms of infectivity and transmissibility which causes greater impact in communities worldwide. With the surge of collected SARS-CoV-2 sequences, studies found out that most of the emerging variants are linked to increased mutations in the spike (S) protein as observed in Alpha, Beta, Gamma, and Delta variants. Multiple approaches on genomic surveillance have been performed to monitor the mutational status and spread of the virus however most are heavily dependent on labels attributed to these sequences. Hence, this study features a system that has the capability to learn the protein language model of SARS-CoV-2 spike proteins, based on a bidirectional long-short term memory (BiLSTM) recurrent neural network, using sequence data alone. Upon obtaining the sequence embedding from the model, observed clusters are generated using the Leiden clustering algorithm and is visualized to monitor similarities between variants in terms of grammatical probability and semantic change. Additionally, the system measures the validity of a user-generated next-generation sequence capturing potential sequence mutations indicative of viral escape, particularly mutations by substitutions. Further studies on methods uncovering semantic rules that govern spike proteins are recommended to learn more about other viral characteristics conclusive of the future of the COVID-19 pandemic. © 2023 IEEE.
ABSTRACT
This article questions the innovative pedagogical practices and the contribution of the digital technologies during the "pedagogical continuity” phase. It aims to clarify certain aspects of remote learning at the Ibn Tofail university. This non programmed learning method aims to find an effective response to the urgent situation related to the spread of the pandemic of "Covid-19”. To collect the necessary data, we used two types of online questionnaires. The first one was for professors in order to examine the way to manage remote courses (adaptation of courses - satisfaction - achievement of learning objectives - student participation). The second questionnaire was addressed to students, which was interesting not only to raise the satisfaction criteria and the constraints encountered but also to relieve their capacity to learn independently. Based on research work related to the role of digital technology in the act of learning, mainly remote learning, this study has made it possible to identify relevant information that would promote the improvement of courses via the Moodle platform. It highlighted the effectiveness of the digital resources used to ensure pedagogical continuity and in particular the ability of students to overcome the physical absence of the professor and to learn independently. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.