Developing, implementing, and managing metadata is crucial to successful knowledge management, and academic libraries have traditionally played a central role in these activities. The Arabian Gulf countries are underrepresented in the existing research into library metadata practices. This exploratory study used semi-structured interviews of metadata managers at 8 universities with the goal of developing understanding of the current state of metadata practices, including descriptive cataloging, identity management, and knowledge organization in academic libraries of three Arabian Gulf countries (Kuwait, Oman, and Qatar), as well as potential future developments to facilitate discovery of resources. Findings provide insights into this previously under-researched area and contribute to understanding of knowledge management and risks on a global scale.

Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …

This workshop is presented by members of a University of North Texas research team. First, the team will overview their experience as members of the research team and share experience in areas such as trust formation, team roles, productivity, work-life balance, faculty-students interaction, peer and faculty mentorship, dissertation preparation, and job seeking. Second, the workshop will discuss and brainstorm how this format can be implemented for organizations both with faculty-student teams and with peer-directed teams. Finally, successes and challenges are openly discussed with audience.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. Since the conference was among the first post-pandemic face to face conferences, the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency. This document highlighted one of the panels that provide an overview of the different methodologies and theories of sense-making from several of the seminal originators of sensemaking.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.

Considerable research has been conducted on the topic of information overload using different approaches, from marketing and customer demand to information technologies and sciences, and even among mental health professionals. In business the critical question is how does information overload impact processes, operations, and profitability, and how can data visualization help to solve issues with data management and consumption in organizations. The ability to quickly and effectively process information and make decisions equates to organizational survival in a dynamic, knowledge-based economy where all segments of society are heavily affected by information technologies and systems and data management industries. The growing number of systems apparatuses challenges both individuals and organizations, resulting in reports of fatigue and experiences that compromise successful performance. The objective of this literature review is to discuss how data visualization tools help address information overload and optimize decision making and the business intelligence process in organizations. It concludes that data visualization, indeed, is critical in helping individuals capture, manage, organize, visualize, and present understandable data, but that decision making is affected by cognitive factors that interfere with data processing and interpretation in decision makers.

Article highlighting the promise of extended reality platforms for greater ecological validity in the clinical, affective, and social neurosciences. This article belongs to the Special Issue: A Decade of Brain Sciences.

This article covers the challenges brought about for teachers and learners from transitioning to online education due to the COVID-19 pandemic. The authors reflect on these challenges based on discussions at EDUsummIT2019 in Quebec about the theme “Learners and learning contexts: New alignments for the digital age." Informed by theoretical conceptualization and empirical evidence the authors identify micro-meso-macro alignments that need to be in place to move education into the digital age: alignments for quality learning contexts, alignments in support for teachers, and alignments through partnerships.

Article exploring how information and knowledge are absorbed by utilizing Constructor Theory and the Substrate-Independence Theory.

This article trained machine learning techniques to classify Norwegian radiology reports of pediatric CT examinations according to their description of abnormal findings. The developed models are robust with respect to different contexts, and may be used in quality assurance processes.

This article develops a virtual reality-based measure of everyday memory functioning for meal preparation tasks. The results support the construct validity of the VKP and suggest that it holds promise as a virtual reality-based measure of memory for meal preparation tasks.

This article presents findings from a series of related studies exploring aspects of Digital Humanities teaching, education, and research in iSchools, often in collaboration with other units and disciplines. Results provide a snapshot of the current state of digital humanities in iSchools which may usefully inform the design and evolution of new DH programs, degrees, and related initiatives.

Article addressing if a new Bayesian framework can be introduced and ways to overcome subjectivity. The authors introduce a new method called Generalized Bayesian Dating (GBD) for inferring dates of language groups from lexical and phonological data. This work has implications for future performance testing in the area of linguistic dating.

Article is a study using cross-sectional methodology to investigate information sources and knowledge concerning the ZIKV virus among 300 under-served pregnant Latinas recruited from prenatal care clinics in the North Texas region. Bivariate and multiple logistic regression models are used to investigate associations between the primary outcomes and patient characteristics.

Article providing a review of the recent research literature on personalized learning as technology is changing how learning can be effectively personalized. The emphasis is on the terms used to characterize learning as those can suggest a framework for personalized and will eventually be used in meta-analyses of research on personalized learning.

Article proposing a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.

This article presents a systematic literature review of papers published from 1960 to 2019 that illustrate hybrid simulation can be as effective as high fidelity simulators in certain training scenarios while at the same time providing a superior training context to enhance learners patient to care-giver interactions and to better immerse the trainee in the feelings and emotion of the scenario.

This article proposes monsters are documents and examines the coding and decoding of monster documents through a functional ontology lens. This article was presented at the 2020 Annual Meeting of the Document Academy.

Working paper exploring ChatGPT. It begins with an introduction to ChatGPT and then proceeds into a transcript of a conversation with the platform about it and related AI technologies’ impact on the future of scholarly publishing, before concluding with some discussion on the further implications.

This paper explores the role of Historically Black College and University (HBCU) archives in preserving the history and cultural heritage of these institutions and investigates the factors that contribute to the disparities observed in the availability of digital collections and the staffing of archivists across HBCUs in the United States. Data was collected from 102 HBCU websites and the National Center for Education Statistics, and analyzed using descriptive statistics, chi-square tests, and ordinary linear regression. The findings revealed notable disparities in the staffing of archivists and the presence of digital archive collections, with geography, enrollment, and endowment emerging as potentially significant factors. The study highlights the importance of understanding key factors in the availability of digital archive collections and the staffing of archivists at HBCUs.

Authors of article conduct a content analysis of research on technology use for teaching mathematics to students with disabilities. They applied word networks and structural topic modeling of 488 studies published from 1980 to 2021. Results showed that the words “computer” and “computer-assisted instruction” had the highest degree of centrality in the 1980s and 1990s, and “learning disability” was another central word in the 2000s and 2010s.

Article discusses how as learners engage, test, and apply new subject knowledge, they often expend their cognitive capacity on the technological tools designed to capture their learning progress and outcomes. The author's research explores the value of developing digital literacy to improve learners’ cognitive flexibility by decreasing technological cognitive load and increasing learning fluency.

Article discusses how, in the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. The authors of the article introduce a novel Device-to-Device (D2D)-assisted system to address this challenge.