Online practical assessment in biochemistry designed on Bloom's taxonomy to assess the student's competency: Prior exposure of the students to online tools beneficial.

Online assessments are needed during the prevailing pandemic situation to continue educational activities while ensuring safety. After conducting the online practical assessment (OPrA) in Biochemistry, we analyzed the students' responses. The blueprint of the OPrA was prepared by the faculty, referring to the various levels and domains of Bloom's taxonomy. Four components were chosen for the online assessment: digital spotters, enumerating the steps of objective structured practical examination, interpretation of quantitative estimation, and case discussion. Each faculty assessed about 12-13 students in separate breakout rooms over 15-20 min on all four components. Feedback on the conduct of the examination was collected from the students and faculty anonymously and analyzed. Out of the 200 students who attended the online assessment, only one scored less than 50%, majority of them scored between 71% and 90%. Under the individual exercises, the average score of students in "Spotters" was 9.8 out of 10; in "OSPE," 8.7 out of 10; in "Quantitative experiments," 15.2 out of 20 and in "Case discussion," 22.4 out of 30. Around 20% had previous experience attending the OPrA. They differed in their opinion from the rest of the students on five aspects; time allotted for the assessment (p value = 0.02, χ2  = 5.07), students using unfair means during the online viva (p value = 0.02, χ2  = 5.57), their computing skills (p value = 0.001, χ2  = 19.82), their performance (p value = 0.001, χ2  = 8.84), and overall conduct of the examination (p value = 0.001, χ2  = 15.55). OPrA tools may be designed referring to Bloom's taxonomy, and prior exposure to the online tools may benefit the students.

Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking

In automated scientific fact-checking, machine learning models are trained to verify scientific claims given evidence. A major bottleneck of this task is the availability of large-scale training datasets on different domains, due to the required domain expertise for data annotation. However, multiple-choice question-answering datasets are readily available across many different domains, thanks to the modern online education and assessment systems. As one of the first steps towards addressing the fact-checking dataset scarcity problem in scientific domains, we propose a pipeline for automatically converting multiple-choice questions into fact-checking data, which we call Multi2Claim. By applying the proposed pipeline, we generated two large-scale datasets for scientific-fact-checking: Med-Fact and Gsci-Fact for the medical and general science domains, respectively. These two datasets are among the first examples of large-scale scientific-fact-checking datasets. We developed baseline models for the verdict prediction task using each dataset. Additionally, we demonstrated that the datasets could be used to improve performance measured by weighted F1 on existing fact-checking datasets such as SciFact, HEALTHVER, COVID-Fact, and CLIMATE-FEVER. In some cases, the improvement in performance was up to a 26% increase. The generated datasets are publicly available. © 2023 Association for Computational Linguistics.

PQSCT: Pseudo-Siamese BERT for Concept Tagging With Both Questions and Solutions

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises, or ignore that exercises may contain multiple concepts. To this end, in this paper, we present a study of concept tagging. First, we propose an improved pre-trained BERT for concept tagging with both questions and solutions (QSCT). Specifically, we design a question-solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions (PQSCT). We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging. 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.

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.

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.

Hey Alexa... Examining Factors Influencing the Educational Use of AI-Enabled Voice Assistants during the COVID-19 Pandemic

The COVID-19 pandemic has resulted in a rapid growth of online learning. While majority of the current research focus on different learning management systems, massive open online courses, or even specific softwares like Zoom and Microsoft Teams, the use of artificial-intelligence (AI) based voice assistants (VAs) for the purpose of online education is very rare. In this work we propose, validate, and test a research model that explains the continuance usage of VAs by students for learning purpose during their home quarantine period. We consider novel pandemic-specific psychological factors like loneliness and self-quarantine, together with anthropomorphic factors like voice attractiveness of the VAs for proposing the research model. The factors of satisfaction and continuance usage are borrowed from Expectation Confirmation Theory. Partial Least Squares Structural Equation Modelling is used for testing the proposed model. © 2023 IEEE.

Producing Generative Digital Data Objects: An Empirical Study on COVID-19 Data Flows in Online Communities

Digital data objects on viruses have played a pivotal role in the fight against COVID-19, leading to healthcare innovation such as new diagnostics, vaccines, and societal intervention strategies. To effectively achieve this, scientists access viral data from online communities (OCs). The social-interactionist view on generativity, however, has put little emphasis on data. We argue that generativity on data depends on the number of data instances, data timeliness, and completeness of data classes. We integrated and analyzed eight OCs containing SARS-CoV-2 nucleotide sequences to explore how community structures influence generativity, revealing considerable differences between OCs. By assessing provided data classes from user perspectives, we found that generativity was limited in two important ways: When required data classes were either insufficiently collected or not made available by OC providers. Our findings highlight that OC providers control generativity of data objects and provide guidance for scientists selecting OCs for their research. © 2022 IEEE Computer Society. All rights reserved.

The use of Deep Learning techniques in E-Learning systems and MOOCs

E-Learning and Massive Open Online Courses are old techniques, but since the Coronavirus, they have become more popular again. Students already suffer from a lack of concentration and motivation in traditional courses;thus, this lack affects online courses. Furthermore, another important Online Learning systems problem is the difference between learners in terms of Learning Styles, abilities, social characteristics as well as preferences, background, and other psychological and mental features. Generally, these features are not taken into account by scientists. Therefore, Deep Learning techniques and Datasets have been used to improve E-Learning systems and MOOCs in several aspects such as: predicting dropout, Learning Styles and performance of online learners, and even their attention after taking an online course. In this work, we have studied and analyzed many recent works in the area of using Deep Learning techniques to improve Online Learning systems and MOOCs. This analysis shows what researchers rely on to improve E-Learning and MOOCs and demonstrates that research does not use the definition of the appropriate Learning Style frequently. However, the most used ones are dropout and performance of learners. In another hand, learners' attention is still gap. © 2022 IEEE.

A virtual research showcase and judging platform created from a patchwork of workplace applications.

Background: Despite the challenges the COVID-19 pandemic placed on libraries' existing workflows and operations, many librarians developed and debuted new services that addressed novel needs that emerged during the pandemic. This report describes how two electronic resource librarians at regional hospitals within a healthcare corporation used exhibition platforms to showcase resident research in an online format as a complement to in-person resident research programming. Case Presentation: Over the course of the pandemic, two exhibition platform variants were implemented, one year apart. This case report describes how each platform was developed. The first online event was conducted using a virtual exhibit platform to minimize in-person contact. The second online event, held the following year, blended a traditional live event with virtual elements using the online exhibit platform. To ensure completion of tasks, project management techniques were adopted throughout the event planning process. Conclusions: The pandemic created opportunities for hospitals to explore transforming meetings from primarily live and onsite into hybrid and fully virtual events. While many corporate hospitals have transitioned back to primarily in-person programming, newly adopted online practices such as online judging platforms and automation of continuing medical education tasks will likely remain. As in-person restrictions within healthcare settings are lifted or eased at uneven rates, organizations may continue to explore the value of in-person meetings versus the video conference experience of the same meeting.

Legal Incentives and Constraints on Innovation: Keeping the Balance

The COVID-19 pandemic forced civil society and business to face a new reality where much greater reliance needed to be placed on networked devices and internet distributed communications, including the provision of services ranging from medical advice to food, entertainment and even the facility to interact with family. The ability to meet in-person with family, friends, colleagues, business associates or customers was severely restricted leaving internationalisation as a utopian dream as borders were closed, students were denied access to a physical classrooms and businesses had to rapidly "pivot” or fail. These alternatives to real life have seemed less appealing to many, with every aspect of life "going online”, whether virtualectures, exams, meetings, mediations, court appearances, job interviews, shopping for a piece of cheese or starting a new trade relationship. Much innovation over the last two years has been around deploying online business models. There has also been a wider use of artificial intelligence to support "efficient” operations partly stimulated by the falling staffing levels due to the pandemic directly through sickness or forced isolations, or indirectly by a growing sense of the futility of working for a business, known as the Great Resignation ("Over the 12 months ending in January 2022, hires totalled 76.4 million and separations totalled 70.0 million…” indicating a huge refocusing on jobs in the USA) This paper looks at the challenge for legal systems to pivot around the growing trends in deployments of online innovation. Some businesses are now widely deploying software-based analysis systems, such as Airbnb, which is using them to "verify the identity and trustworthiness of a user of an online system” and flag potential guests who may be problematic. Although Airbnb is a multibillion-dollar business, it is a good example of how through using publicly available data, user supplied information, and smart software (artificial intelligence) a business can make predictions on the behaviour of its potential customers. Other AI resources have been creating new gaming scenarios, reporting on the news, and even creating new artworks and music. These kinds of use of AI in the marketplace have challenged the legal frameworks that support individual privacy and also ideas around human creativity. © 2022, Academic Conferences and Publishing International Limited. All right reserved.

Network based Learning Platform Application Model for Enhancement of Realtime Working Systems

Learning platforms have become an integral part of the education system these days. Especially after the COVID era, education has become more allied with self-paced and remote learning and learning platforms have made it boom exponentially. Improper construction and implementation of such platforms can lead to huge risks for the users and the company. Data security is not taken much care of while building such platforms;instead, concentration is given to fancy front-end pages and attractive interfaces. This may not be good at all times. Data is one of the most powerful resources and can have a very big impact if misused. This paper proposes a networking-based approach to implementing such platform systems in a safe and organized way. Implementation using networking concepts gives a better hand in managing permissions, access rights, and security in all data-related transfers and communications. In terms of online gaming and real-time video communication, User Datagram Protocol (UDP) is often used because it is faster than Transmission Control Protocol (TCP) and is well-suited for real-time applications that cannot tolerate delays. UDP is a connectionless protocol, which means it doesn't retransmit lost data and therefore has lower overhead, making it a good choice for real-time applications like video conferencing and online gaming. Examples of such applications include Skype, Google Meet, Zoom, and Facetime. Based on these existing applications, this work introduces UDP in the field of Learning Platform Applications and builds a model on top of which real-time applications can be constructed. The proposed system makes use of UDP for all its requests, responses, and file transfers. The protocol itself is not very reliable, but the addition of provisions for acknowledgements in all requests and responses makes this system overcome transfer uncertainty. Implementation using networking concepts improves the speed, security, privacy, and customization abilities of the proposed system. © 2023 IEEE.

Design of an Intelligent Educational Evaluation System Using Deep Learning

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario. © 2013 IEEE.

The Relationship Between Students' Flow Experience and Their Behavior Data in Gamified Educational Systems

The number of students using online educational systems is increasing, especially after the growth of the use of this type of system due to the social isolation caused by the Covid-19 pandemic. This situation highlighted the challenge of analyzing the users' experience in this type of system, especially when evaluating more complex experiences, such as the flow experience. One of the most promising innovative alternatives is to use the behavior data logs produced by students in educational systems to analyze their experiences. In this paper, we conducted a study (N = 24) to analyze the relationships between the behavior data logs produced by students when using a gamified educational system and their flow experience during the system usage. Our results contribute to the automatic users' experience analysis in educational systems. © 2022 IEEE Computer Society. All rights reserved.

Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

How an Online Health Support Community Responded to the COVID-19 Crisis

The scope and severity of the COVID-19 pandemic has introduced new challenges for people seeking health information online. To understand how an existing online community of people living with a chronic health condition navigate meeting their existing health goals alongside the challenges and tensions resulting from COVID-19, we performed a qualitative content analysis of six weeks of discussion in the r/infertility online community. We found that community members collaborated and debated to make dynamic structural and normative changes to their community in accordance with the changing impacts of COVID-19 on their experiences. Additionally, we found that community members information-seeking goals were centered around timelines for their own treatment plans and goals, with the scope of these timelines shifting based on their current state of knowledge of COVID-19. Implications of these findings for supporting health online communities broadly are discussed. © 2022 IEEE Computer Society. All rights reserved.

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.

Boundary Crossing through Text and Image on Instagram in an Online Community of Practice

During COVID-19 lockdown many social media challenges captured the attention of users all around the world, and many online communities of practice used social media platforms for their daily interactions. On Instagram these communities gather around common interests through the platform's sociotechnical affordances. We examined the role that these features play in boundary maintenance processes and boundary crossing practices, analyzing posts from four online communities of practice (CoPs), who were bounded by their hashtags and shared an art recreation challenge that was popular on Instagram at the start of COVID-19 lockdown. We found that while some practices are shared across CoPs, boundary maintenance processes sometimes are not, and the boundaries of some of these CoPs are more permeable than others. Cultural differences, language, and script were critical for boundary maintenance regardless of the platform's visual affordances that served the boundary crossing practices. © 2023 IEEE Computer Society. All rights reserved.

The Man-Machine Relationship on the Web: Motivation to Use the Internet

In an environment characterized by turmoil and unpredictability, by the digital transition and transformation, and by the economic and social effects caused by the global public crisis (COVID19), this study aims to analyze the motivations for using the internet and making online purchases, identifying the perceived benefits and consumer satisfaction. For this, an exploratory study with descriptive design was carried out, through the administration of a questionnaire (google forms). 385 consumers responded. The data show that there are significant differences between groups (buyers and non-buyers) in terms of motivation, perceptions of benefits and satisfaction. The use of online shopping platforms fosters a relationship that favors efficiency and enhances feelings of control and freedom in purchasing behavior. The experiences lived through technological intermediation, given the possibility of interaction and personalization, add value to brands, create an innovative identity, while contributing to obtaining a memorable and satisfying experience. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Efficient Online and Privacy-preserving Medical Pre-diagnosis Based on Growing Learning Vector Quantization

With more and more people turning to online medical pre-diagnosis systems, it becomes increasingly important to protect patient privacy and enhance the accuracy and efficiency of diagnosis. That is because the ever rapidly growing medical records not only contain a large amount of private information but are often highly unequally distributed (e.g., the number of cases and the rate of increase of covid-19 can be much higher than that of common diseases). However, existing methods are not capable of simultaneously boosting the intensity of privacy protection, and the accuracy and efficiency of diagnosis. In this paper, we propose an online medical pre-diagnosis scheme based on incremental learning vector quantization (called WL-OMPD) to achieve the two objectives at the same time. Specifically, within WL-OMPD, we design an efficient algorithm, Wasserstein-Learning Vector Quantization (W-LVQ), to smartly compress the original medical records into hypothetic samples. Then, we transmit these compressed data to the cloud instead of the original records to offer a more accurate pre-diagnosis. Extensive evaluations of real medical datasets show that the WL-OMPD scheme can improve the imbalance ratio of the data to a certain extent and then the intensity of privacy protection. These results also demonstrate that WL-OMPD substantially boost the accuracy of the classification model and increase diagnostic efficiency at a lower compression rate. © 2022 IEEE.