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In the current era, the use of corpora in language teaching is mainly explored in English classes as it has become a trend in education. Hence, this research aimed to identify the corpus metadata, frequently used words, and unique words related to the Islamic boarding school context to be used in the English instructional process. This research employed a mixed method combining quantitative and qualitative data analysis methods. Two English Islamic boarding school books, several articles covering the scope of Islamic boarding school, and students' speech texts were selected as the data. Then, they were analyzed using the Voyant tool. The finding showed total words of 49,970: 5,417 specific words, 0.108 vocabulary density, and a 12,980-readability index. The finding will be incorporated into instructional resources for developing Islamic boarding school students' general and/or specialized vocabulary. The words, in particular, will provide a foundation for students in constructing Islamic speech texts, delivering speeches, and using English in an Islamic boarding school environment.

Historical and modern scientific thought is dominated by the English language, colonized science, patriarchal norms, and Westernized "ways of knowing." By making materials that lie outside that narrative discoverable (e.g., non-Western science, Indigenous/Aboriginal knowledge, women in science, etc.) we can assert that science is a global endeavor by representing diverse scientists on physical library shelves or ebook "shelves." The Cataloging and Metadata Librarian and Science and Outreach Librarian at Furman University, a small liberal arts college, collaborated on a project to address discriminatory practices in the catalog records of a subset of the libraries' STEM collection. Spurred by another project to update deprecated Cutters in Library of Congress call numbers, we assessed collection content with an eye to improve future collection development and created a local method for collocating DEI materials within an Alma consortium catalog. The intended goals of the project were to update the collection, improve metadata to increase discoverability, and develop an auditing process to use for other collections.

The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate background knowledge to cognitively receive the recommended learning objects. For that reason, when designing an efficient e-learning recommendation method, these challenges should be considered. To address those issues, in this paper, we first propose extracting pairs concept prerequisites using Linked Open Data (LOD). Then, we evaluate the proposed list of prerequisite relationships using machine learning predictive models. Then, we present the recommendation approach based on matching concept's prerequisites relation and courses metadata through a similarity score. The experimental result of prerequisite identification was evaluated using four well-known machine learning algorithms while achieving an accuracy of 90%. Moreover, using three known evaluation metrics, the final prerequisite-based recommendation demonstrates very good results (NDCG@10 = 86%). This solution will enhance recommendations on online learning platforms. Additionally, it will overcome the cold-start issue and accomplish the needed prerequisites and background knowledge for learners to attain their learning objectives.

A college class of non-science majors completed a metadata project in 15 weeks for a Women's History collection at a southern 4-year university. The class "First and Second Wave of Feminism" explored for the first time a scientific method of cataloguing while learning to promote, restore and preserve the history of women in the United States. This class completed a service-learning project with the university's Special Collection and Archives enabling them to learn library science metadata skills. The Judith and Warren Kaplan Women's History collection includes ephemera, documents and books all dealing on women's history and therefore of interest to the students in the above-mentioned class. The user centric metadata included open-ended questions for students to answer such as "Importance" of women's history and "Why" the item was chosen to catalogue. After careful reading of student's reflections on the project's impact on them, several conclusions can be reached. Students preferred to express their choice and description as much as the reflection necessary to complete the Service-Learning project. Students also prefer to relate each item from something learn in the classroom as a way of matching both book and activity-centered experiences.

Bioinformatics develops methods to understand biological data and to explain biological processes. The discipline operates on huge data sets and is computationally intensive. The fast growth of the field and its specialization into many subdisciplines makes it hard to search for, find, and to keep track of scientific results relevant for one's own research. Being able to get knowledge of prior studies, and the data they rely on, is a prerequisite to further one's own research and to ensure effective progress of bioinformatics as a field. At the same time, scientists' own research and reputation benefit from the best possible searchableness of their research data. The FAIR data movement draws from this motivation. Before research data can be accessed and reused, however, it must first be found or discovered. For this purpose, the large space of highly diverse research data must be conquered, it must have a high quality of searchableness. To increase searchableness, we have devised a metadata schema to describe the entire field of bioinformatics by a small set of descriptors. Our metadata schema has been inspired by Dublin Core and aims at replicating its success in the domain of bioinformatics. The schema aims at complementing the many metadata schemes used by bioinformaticians in practice by extracting their common core, yielding to a schema that can be used across bioinformatics subdisciplines. Our minimal schema for bioinformatics metadata is complemented by a Web-based annotation tool where such metadata can be provided in an effective, time-saving, and concise manner. [ABSTRACT FROM AUTHOR]

Abstract Background Light exposure significantly impacts human health, regulating our circadian clock, sleep–wake cycle and other physiological processes. With the emergence of wearable light loggers and dosimeters, research on real-world light exposure effects is growing. There is a critical need to standardize data collection and documentation across studies. Results This article proposes a new metadata descriptor designed to capture crucial information within personalized light exposure datasets collected with wearable light loggers and dosimeters. The descriptor, developed collaboratively by international experts, has a modular structure for future expansion and customization. It covers four key domains: study design, participant characteristics, dataset details, and device specifications. Each domain includes specific metadata fields for comprehensive documentation. The user-friendly descriptor is available in JSON format. A web interface simplifies generating compliant JSON files for broad accessibility. Version control allows for future improvements. Conclusions Our metadata descriptor empowers researchers to enhance the quality and value of their light dosimetry datasets by making them FAIR (findable, accessible, interoperable and reusable). Ultimately, its adoption will advance our understanding of how light exposure affects human physiology and behaviour in real-world settings.

The quality of metadata has a direct effect on the ability to find, identify, select, obtain, and explore information; therefore, training to create high-quality metadata is an important, yet challenging, task for LIS educators. To provide such training effectively, instructors need to develop a vision for where best to focus their efforts, informed by understanding of the common metadata quality problems in student-created records and the way in which observed patterns correlate with the content and methods of instruction. Our exploratory case study addresses this need through empirical data. The article provides an overview of the metadata-creation skill building in instructional design of an online graduate metadata course at a US university and includes results of a comparative content analysis of student-created Dublin Core metadata records in three semesters based on the criteria of accuracy, completeness, and consistency. Findings reveal that accuracy and completeness issues are prevalent and that metadata quality problems revolve mainly around representation of relations and subjects and the application of controlled vocabularies. Results also indicate student confusion regarding the purpose of several metadata elements. The article discusses the findings in relation to content and methods of instruction, considers how the observed quality issues in student-created metadata might be addressed in improving skill building through curriculum development, and proposes directions for further research.

Discussions surrounding digital libraries and their metadata have become less robust than before, given that creating and hosting digital collections have become the norm for most cultural heritage institutions over the last 20 years. Consequently, there are fewer discussions of how the characteristics of digital collections and their metadata have changed over time. This article showcases the evolution of digital collections and their metadata at the University of Illinois Urbana-Champaign (UIUC) Library in the last 20 years. It discusses the growth of its collections and their characteristics, examines historical changes in the use of metadata elements, and explores responses to the changing nature of digitized and born-digital materials. Based on a large-scale data analysis of the digital collections and their metadata housed in UIUC Digital Library, the paper also examines the challenges and opportunities of the curation and management of digital collections and digital libraries in the future. [ABSTRACT FROM AUTHOR]

In the Arabian Gulf countries, including but not limited to Kuwait, digital library metadata education is currently in its early stages. Empirical data assessing student learning outcomes of metadata instruction would allow for data-driven curriculum development as the courses and academic programs that are intended to provide such education evolve in the region. The study presented in this article provides these much-needed data from an undergraduate library and information science program in Kuwait. In this project, we examined the metadata records created to represent Arabic-language eBooks as part of individual and group assignments. A total of 187 student-created Dublin Core metadata records were collected from multiple sections of the metadata course (including female-only and male-only sections) over four semesters in 2021 and 2022. The dataset included 177 individually created records, and 10 records created by student teams. Accuracy and completeness of these metadata records were evaluated and compared across sections and between individual and team projects. The study's findings are presented and discussed in the context of metadata teaching practices in this Kuwaiti undergraduate program, and specifically with regard to activities and assessments intended to develop the Dublin Core record-creation skills. The study results demonstrate that students overall experience the most problems with representing copyright and intellectual rights and source information, describing what times and locations the information resource is about, and representing those who made non-major contributions to the information resource. Substantial differences were also observed in the patterns of metadata accuracy and completeness between individual work and teamwork, and between the metadata created by students in female-only and male-only course sections. Ideas for future research projects that would collectively allow forming a robust understanding of current metadata education and its outcomes in the Arabian Gulf region, as well as suggested curriculum enhancements, are discussed. [ABSTRACT FROM AUTHOR]

Machines produce and operate using complex systems of metadata that need to be catalogued, sorted, and processed. Many students lack the experience with metadata and sufficient knowledge about it to understand it as part of their data literacy skills. This paper describes an educational and interactive database activity designed for teaching undergraduate communication students about the creation, value, and logic of structured data. Through a set of virtual instructional videos and interactive visualizations, the paper describes how students can gain experience with structured data and apply that knowledge to successfully find, curate, and classify a digital archive of media artifacts. The pedagogical activity, teaching materials, and archives are facilitated through and housed in an online resource called Fabric of Digital Life (fabricofdigitallife.com). We end by discussing the activity's relevance for the emerging field of human-machine communication.

Audiovisual resources available online gain more importance in satisfying users' information needs, yet the quality of metadata that ensures discoverability of these resources is not yet emphasized, mainly due to lack of benchmark data. This article addresses the need by presenting results of the comparative evaluation of accuracy and completeness of Dublin Core metadata records created by metadata beginners to represent audio recordings and video recordings. We present our findings in the context of how the metadata learning is organized at the program which currently prepares most of those entering the information profession in Kuwait. Findings reveal some similarities, as well as some pronounced differences in metadata accuracy and completeness patterns for two kinds of online digital resources: audio recordings and video recordings. Video metadata was found to be of substantially higher quality than audio metadata created by the same beginners. Overall, for audiovisual information resources, we found the Type metadata field to be the least prone to completeness errors, and Format to accuracy errors. Our data suggests that the Source metadata field is the most vulnerable for accuracy errors in both audio metadata and video metadata. However, Dublin Core metadata fields with the highest possibility of completeness errors did not exhibit overlap between the two sets of beginner-created metadata records. We discuss examples of the most common metadata errors and compare results with findings of previous research. Empirical data obtained in this study allows assessing preparedness of information professionals to create metadata that is functional in supporting resource discovery. [ABSTRACT FROM AUTHOR]

This article reports comparison results from studying the alignment of selected metadata models, used to manage E-Learning Materials (ELM) in the medicine and healthcare education, against the accreditation standards of the European Accreditation Council for Continuing Medical Education (EACCME). The study aims to (1) illustrate the variety of application profiles developed as metadata models in that field, (2) identify the alignment of the metadata models with the criteria set by the EACCME. This study compared several application profiles, developed based on established metadata standards, using an evaluation matrix created using the accreditation criteria set by the EACCME. Further technical criteria were added to compare between the metadata models and their alignment with the new techniques used for publishing data online. Such techniques aid in the searchabililty and discovery of online content via the internet. Therefore, the use of XML and RDF technologies combined with the use of ontologies and controlled vocabulary was emphasized in the implementation of many metadata models. One limitation faced during conducting this research was the poor documentation of the methodology for designing the metadata models. Despite this, it is apparent that the EACCME criteria can work as best practices guidelines for describing the content of digital libraries with medical and healthcare content in order to be accredited later on. The study showed that the medicine and healthcare educational content requires specialized metadata models that consider having specific properties such as clinical history and expiry time of its material shown in some of the application profiles. Further research should be conducted to evaluate the completeness and conformity of applying the application profiles in digital libraries and online repositories to the standards' guidelines.

Quality assessments of institutional repository (IR) metadata afford opportunities for improvements to resource description and standards. This article describes a systematic review and enhancement of existing metadata and description practices in [author institution] Institutional Repository. The assessment revealed inconsistencies and omissions that could impede access. OpenRefine, R, and Python were used to remediate, supplement, or replace description with metadata exported from Web of Science and Scopus. This project improved description standards and access to IR resources, and future assessments will be performed annually to ensure metadata conform to IR standards and seek ways to improve standards. [ABSTRACT FROM AUTHOR]

Abstract The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate background knowledge to cognitively receive the recommended learning objects. For that reason, when designing an efficient e-learning recommendation method, these challenges should be considered. To address those issues, in this paper, we first propose extracting pairs concept prerequisites using Linked Open Data (LOD). Then, we evaluate the proposed list of prerequisite relationships using machine learning predictive models. Then, we present the recommendation approach based on matching concept’s prerequisites relation and courses metadata through a similarity score. The experimental result of prerequisite identification was evaluated using four well-known machine learning algorithms while achieving an accuracy of 90%. Moreover, using three known evaluation metrics, the final prerequisite-based recommendation demonstrates very good results (NDCG@10 = 86%). This solution will enhance recommendations on online learning platforms. Additionally, it will overcome the cold-start issue and accomplish the needed prerequisites and background knowledge for learners to attain their learning objectives.