UNT College of Information - 439 Matching Results

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Dublin Core Metadata Created by Kuwaiti Students: Exploration of Quality in Context: Conference paper reporting results of the examination of metadata records for Arabic-language eBooks to support ongoing metadata education in the Arabian Gulf region. The results are presented in-context, after introducing the metadata teaching practices at this undergraduate program, and the major Dublin Core skill-building assignment. This is a manuscript version of a published work. Citation information is available for the published version of the work.
Exploration of Accuracy, Completeness and Consistency in Metadata for Physical Objects in Museum Collections: Conference paper for an exploratory study that examined student-created metadata for physical non-text resources. The authors applied in-depth qualitative and quantitative content analysis to the Dublin Core (DCTERMS) metadata created by the graduate students in two sections of an introductory digital library metadata course. Finding of comparative analysis for the asynchronous course section and the section with synchronous class meetings are also presented. Implications are discussed, along with future directions for research. This is a manuscript version of a published work. Citation information is available for the published version of the
The impact of high-immersion virtual reality on foreign language anxiety: Authors of the article assert that public speaking, especially in a foreign language, is associated with increased anxiety. The authors found that VR technology had a positive effect on practicing public speaking in a foreign language, the research findings have practical implications for professionals and curriculum designers in various domains where public speaking skills are essential.
DEVO: an ontology to assist with dermoscopic feature standardization: Article describes how the utilization of dermoscopic analysis is becoming increasingly critical for diagnosing skin diseases by physicians and even artificial intelligence. The authors aimed to develop a domain-specific ontology to formally represent knowledge for certain dermoscopic features.
Phrasal Proper Names in German and Norwegian: Article discusses the morpho-syntax of phrasal proper names like Deutsche Bahn 'German Railway' and Norske Skog 'Norwegian Forest' in German and Norwegian. The authors document that phrasal proper names may show features of recursivity evidenced most clearly in Norwegian.
Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network: Article describes how accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level.
Spatial Analysis and visual Communication of Emergency Information through Augmented Reality: Article describes how during emergencies like fire and smoke or active shooter events, there is a need to address the vulnerability and assess plans for evacuation. This paper presents the development and evaluation of the mobile augmented reality application designed specifically for acquiring spatial analysis, situational awareness, and visual communication.
Scaling Students' Self-Efficacy on Machine Translation Post-Editing: Psychometric Properties of the Scale and Their Associations: Article describes how Machine Translation Post-Editing has emerged as a productivity-enhancing practice in the language service industry, where human editors correct the output of machine translation systems. this research paper aims to assess students' self-efficacy in translation learning, specifically in the context of MTPE, and explore the factor structure, psychometric properties, and internal associations of their self-efficacy.
Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study: Article describes how the authors' study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events.
Joint Optimization of Computation, Communication and Caching in D2D-Assisted Caching-Enhanced MEC System: 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.
Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success: Article asserts that international students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research.
Definition and Goals of Descriptive Linguistic Fieldwork: Book chapter defining descriptive linguistic fieldwork, explores tasks that fall under this definition, outlines goals of descriptive linguistic fieldwork, and identifies aspirations and limitations of linguistic fieldworkers.
Content-Based Characterization of the End of Term Web Archive: Article describes how, since 2008, the End of Term Web Archive has been gathering snapshots of the federal web, consisting of the publicly accessible .gov and .mil websites. This paper describes the decisions made in the creation of these derivatives, the technologies used, and introduces the WARC Metadata Sidecar, which presents a useful approach for creating and storing auxiliary metadata for web archives.
End of Term Web Archive Dataset: Longitudinal Web Archive of .GOV and .MIL Domains: Article describes how the End of Term (EOT) Web Archive Dataset presents a longitudinal dataset of the US federal web which includes publicly available .gov and .mil domains, created during the 2008, 2012, 2016, and 2020 presidential elections in the United States. The authors describe how based on the End of Term Web Archive, this dataset presents 461TB of WARC data and accompanying derivative files in WAT, WET, and CDX format.
Using existing metadata standards and tools for a digital language archive: a balancing act: Article discusses how building a digital language archive requires a number of steps to ensure collecting, describing, preserving and providing access to language data in effective and efficient ways. This paper introduces the reader to the background of this project and discusses some of the areas important for representing language materials where both University of North Texas Libraries (UNTL) metadata and CoRSAL metadata practices were adapted to better fit the needs of intended audiences.
What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity: Article describes how the authors present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures they study are corpus-based measures of morphological complexity with varying requirements for corpus annotation.
Detecting interdisciplinary semantic drift for knowledge organization based on normal cloud model: Article describes how, to reduce the conceptual ambiguity in interdisciplinary knowledge organization systems (KOSs) and enhance interdisciplinary KOS management, this paper proposes a framework for interdisciplinary semantic drift (ISD) detection based on the normal cloud model (NCM). The research indicates the meaning of an interdisciplinary concept will drift from the high KPE discipline to the low KPE discipline as long as interdisciplinary knowledge potential differences (KPD) exist between these two related disciplines.
Exploring the richness of collection-level subject metadata in three large-scale digital libraries: This article reports results of a study that used an in-depth comparative content analysis to assess free-text and controlled vocabulary collection-level subject metadata in three large-scale digital libraries in the European Union and the USA.
Adaptive learning: toward an intentional model for learning process guidance based on learner’s motivation: Article describes how, the goal of ITS is to support learning content, activities, and resources, adapted to the specific needs of the individual learner and influenced by learner’s motivation. This research proposes an intentional model that adopts Map formalism to support personalized learning guidance by considering learner’s motivation.
A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method: Article describes how in digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. The authors state that by combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper.
Assessing approaches to learning with nonparametric multidimensional scaling: Article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in Whyville, an online game environment with 8 million registered young learners. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth.
Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets: Article discusses how, during the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors.
Creativity and Innovative Processes: Assemblages and Lines of Flight: Article provides assemblage maps showing the elements related to creativity, innovation, and creativity and innovation. These assemblage maps highlight virtual and dynamic flight lines that represent potentially active components with varying intensity and direction, which provides a tool for managers and practitioners to identify potentialities for future predictions better.
A content analysis of research on technology use for teaching mathematics to students with disabilities: word networks and topic modeling: 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.
Toward an intentional model aware of learner cognitive traits for pedagogical process guidance: Authors of the article suggest that the novelty of Intentional Model of Pedagogical Process Guidance (IMPPG) is the effectiveness use of Cognitive Trait Model to be aware of different traits of learner. This model has been experimented and assessed with tutors and students learning spreadsheet management in a first-year studying in applied license’s degree in Business English and applied license’s degree in Education.
Correction: Adaptive learning: toward an intentional model for learning process guidance based on learner’s motivation: Correction to article changes the name of one of the authors.
Future Directions for digital Literacy Fluency using Cognitive Flexibility Research: A Review of Selected Digital Literacy Paradigms and Theoretical Frameworks: 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.
Exploration of Metadata Practices in Digital Collections of Archives with Arabian Language Materials: Article for a study aimed to develop understanding of the current state of metadata practices in digital collections of archival institutions in the Arabian Gulf region. It also explored perspectives (including attitudes and possible barriers) for development of large-scale regional portals that would facilitate discovery of Arab digital archives (including language collections) by aggregating metadata. It was presented at the 2nd International Workshop on Digital Language Archives held on June 30, 2023 as part of the ACM/IEEE Joint Conference on Digital Libraries 2023.
Language Archiving Training: A Case Study of a Metadata Course in Library and Information Science Graduate Program, 2020 - 2023: Article explores the training gap between the way these materials are organized and represented and the understanding of that data – and expectations towards the more functional ways of its organization and representation – by language preservation and revitalization researchers, and by members of language communities. Information resources collected by language archives have unique attributes of importance to their target user groups, and these attributes and their representation are not currently widely addressed by the formal training provided to information professionals. It was presented at the 2nd International Workshop on Digital Language Archives held on June 30, 2023 as part of the ACM/IEEE Joint Conference on Digital Libraries 2023.<
Status of Historically Black College and University (HBCU) Archives: 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.
Research Teams: Fostering Scholarship and Practice: 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.
Increasing Information Certainty for Post-Traumatic Growth: Trauma, and its associated effects, can be conceptualized as a period of information uncertainty. The natural psychological response to trauma is a period of post-traumatic stress. Trauma occurs when an existing knowledge base has been challenged. Any event that challenges important components of an individual’s assumptive world is said to be traumatic. This post-traumatic period is akin to many theories and concepts in information science including uncertainty reduction, Everyday Life Information Seeking, Sensemaking Theory, Making Meaning and Anomalous States of Knowledge. One possible outcome after the post- traumatic period is post-traumatic growth. Researchers agree post-traumatic growth primarily occurs across one or more of the following domains: personal strength, new possibilities, relating to others, appreciation of life and spiritual change. That is, people affected by trauma tend to grow when they find new or additional paths of information certainty.
Metadata Practices of Academic Libraries in Kuwait, Oman, and Qatar: Current State, Risks, and Perspectives for Knowledge Management: 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.
Using Data Visualization Tools to Mitigate the Influx of Information in Organizations: 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.
Application of Big Data Analytics in Precision Medicine: Lesson for Ethiopia: Precision medicine is an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person. Big data analytics (BDA) using cutting-edge technologies helps to design models that can diagnose, treat and predict diseases. In Ethiopia, healthcare service delivery faces many challenges specifically in relation to prescribing the right medicine to the right patient at the right time. Thus, patients face challenges ranging from staying on treatment plans longer, and then leaving treatment, and finally dying of complications. Therefore, the aim of this paper is to explore the trends, challenges, and opportunities of applying BDA in precision medicine globally and take lessons for Ethiopia through a systematic literature review of 19 peer reviewed articles from five databases. The findings indicated that cancer in general, epilepsy, and systemic diseases altogether are areas currently getting big attention. The challenges are attributed to the nature of health data, failure in collaboration for data sharing, ethical and legal issues, interoperability of systems, poor knowledge skills and culture, and poor infrastructure. Development of modern technologies, experimental technologies and methods, cloud computing, Internet of Things, social networks and Ethiopia’s government initiative to promote private technological firms could be an opportunity to use BDA for precision medicine in Ethiopia.
Stock2Vec: An Embedding to Improve Predictive Models for Companies: 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.
Prediction of Concrete Bridge Deck Condition Ratting Based on Climate Data in Addition to Bridge Data: Five States as a Case Study: 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 data - and their prediction performances were compared. The results indicated that: (1) random forest outperforms GBM with an accuracy of 63.3%, and (2) the change in the prediction performance after further learning from climate data was marginal since the accuracy reached to 64.9%.
Social Media and People Perception of Global Warming During Critical Environmental Events: the Impact of Misinformation through the Lens of Social Noise: 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.
An Interactive Web-Based Dashboard to Examine Trending Topics: Application to Financial Journals: Understanding trends is helpful to identify future behaviors in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering introduced in Behpour et al. (2021) study. The developed and freely available web application enables users to detect well defined trending topics in financial journals by experimenting with various levels of temporal bias - from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.
Changing Landscape of Scholarly Communications: Open Access: 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 and the panelists argue that open access to scholarly knowledge production should be the modus operandi in the time and age we live in. Open access to knowledge is critical not just to accelerate advances in finding solutions to societal issues, but also to meet the growing expectations around higher education institutions’ social responsibilities in times of uncertainties.
Sense-Making: Panel of Discovery: 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.
A Chat with ChatGPT: How will AI and GPT impact scholarly publishing?: 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.
Defining Data Literacy: An Empirical Study of Data Literacy Dimension: Poster on an analysis of publications from 2002-2021 on data literacy to identify relevant topics and trends. This is a part of preliminary work done to support a proposal for an Institute of Museum and Library Services (IMLS) grant. It was presented at the 2021 Association for Library and Information Science Education (ALISE) Annual Conference held virtually September 20-24, 2021.
A Comparative Analysis of Data Literacy Competency Frameworks: Poster on a comparative analysis of data literacy competency frameworks. Seven competency frameworks were selected for in-depth analysis, and the results are compiled in a table. This work is part of an Institute of Museum and Library Services (IMLS) grant. It was presented at the 2022 Association for Library and Information Science Education (ALISE) Annual Conference held October 24-26, 2022.
Comparison of glide path and pathfiles in canal preparation by cone-beam computed tomography: An original research: This article is a study describing the canal transportation of the different file systems in various combinations of the “Glide path (Gp)” by cone-beam computed tomography (CBCT).
Student Data Literacy Needs in Community Colleges: Perceptions of Librarians, Students, and Faculty: Grant narrative for the grant, "Students Data Literacy Needs in Community Colleges: Perspectives of Libraries, Students and Faculty." The University of North Texas will conduct an 18-month planning project to examine the current perspectives of community college librarians, faculty, and students regarding data literacy; identify the data literacy competencies needed for community college students; and develop data literacy action plans for community college libraries to assist community college librarians in assessing their capability and creating a road map to incorporate data literacy into their existing literacy programs. The findings of this project will identify the role and position of community college libraries in facilitating and enhancing the development of the data literacy competencies of students.
The Substrate-Independence Theory: Advancing Constructor Theory to Scaffold Substrate Attributes for the Recursive Interaction between Knowledge and Information: Article exploring how information and knowledge are absorbed by utilizing Constructor Theory and the Substrate-Independence Theory.
Understanding the Impact of COVID-19 Pandemic on Assistive Technology Services: This article is an introduction to the special issue Assistive Technology Services During and After the COVID-19 Pandemic, which was created to provide a historical record of the impact of the COVID-19 pandemic on the provision of assistive technology services in schools.
Library Computing Services in Rural Texas during the COVID-19 Pandemic: Article analyzing the 2020 Texas Public Library Statistics and a portion of the 2019 and 2018 data. It examined various services from libraries located in the areas with a population of less than 5,000. This is the Accepted Manuscript version of a published article.
Prenominal possessives in Yiddish: mayn khaver versus mayner a khaver: Article provides a systematic comparison and detailed analysis of two prenominal possessive constructions in Yiddish, the familiar mayn khaver ‘my friend’ and the less well-known mayner a khaver ‘a friend of mine.’

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