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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.

This study aims to create a comprehensive bibliometric map of published scientific articles on e-learning in mathematics education (ME). We used Web of Science (WoS) database to analyzed 341 articles published by 1018 authors representing 79 countries between 2012 and 2022. In this context, we examined scientific articles in terms of scientific production, network analysis, trend topics, thematic change, and conceptual structure. Bibliometric analysis was performed in the study. According to findings, the number of published articles differ according to the years, but the number of citations is constantly increasing. Albano, and Dello Iacono are the most prolific authors. The most productive institutions are Salerno and Black Sea Technical Universities. China, USA, and Russia lead the countries of the corresponding authors. According to the network analysis, the journals of Computers & Education and Computers in Human Behavior form strong links. The most used keywords are students, technology, and mathematics. The pandemic, COVID, reality, and process are trend topic titles. It determined that the best size reduction obtained in the conceptual analysis constituted approximately 31% of the total variability. We presented some suggestions based on the findings obtained at the end of the research.

This article introduces the CELI corpus, a new learner corpus of written Italian consisting of ca. 600,000 tokens, evenly distributed among CEFR (Common European Framework of Reference for Languages) proficiency levels B1, B2, C1 and C2. The collected texts derive from the language certification exams administered by the University for Foreigners of Perugia all around the world. The corpus contains rich metadata pertaining to text-related and learner-related variables. It expands the domain of learner corpora by being, among other things, both freely available online to the research community, and by focusing on a target language other than English. The article also presents and evaluates the POS-tagging procedure, thus contributing to best practices in learner corpus annotation.

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.

Hollywood blockbuster films have long attracted not only mass audiences but also scholarly attention. In line with contemporary applied linguistics interests in telecinematic discourse, the present study draws upon concepts and techniques in corpus linguistics to describe the language of American mainstream film scripts. The concept of lexical bundles was employed to identify linguistic patterns characteristic of scripts of American mainstream films produced by entertainment conglomerates, which are popular in the U.S. Results show that American mainstream film scripts are characterized mainly by spoken formulaic expressions. However, descriptive expressions, such as place-referential and action-related lexical bundles, also predominantly make up the given register. Further qualitative analysis reveals that these common multi-word expressions have functional contributions to film scripts in terms of creation of conflicts in plots, characterization, and building engagement with audiences.

The present study is undertaken to investigate research towards thinking skills and creativity in the field of education by utilizing the bibliometric mapping method. The metadata of a total of 326 studies completed between the years 1975-2021 retrieved from the Web of Science (WoS) database constitute the scope of the study. In this context, the program VOSViewer was used to reveal the most used keywords and common citations. The findings present a systematic map demonstrating the increasing interest in thinking skills and creativity in education. The findings demonstrate that the majority of the studies consist of papers and articles of Indonesian, Chinese, and American origin written in English. In addition, the results highlight that the number of studies in the relevant field increased rapidly between 2012 and 2021. King Abdulaziz University and The Hong Kong Polytechnic University have been identified as the leading educational institutions in terms of thinking skills and creativity in education. The results of the current study appeal not only to the academic community but also to educators and politicians to strengthen the spirit of thinking skills and creativity in education, design effective policy tools, and ultimately improve social well-being. In addition, it was observed that the academics who conducted research in this field explained the theme of thinking skills and creativity by associating it with various teaching processes and technology-supported elements at different levels of education. Therefore, it is predicted that our study will contribute to the field of thinking skills and creativity in education as a guide.

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.

Background: Combining Game-Based Approaches, video feedback, and debate of ideas "(débat d'idées)" constitutes an emerging field within pedagogical literature in Physical and Health Education. Nevertheless, more work is needed to understand how this digital tool can be effectively integrated into the teaching and learning process. Purpose: This study sought to further investigate the effective implementation of video tagging in games lessons with 14-15-year-old students. The central aim was to explore how a Game Sense Approach can be effectively integrated with video tagging and student-led debates. The secondary aim was to investigate the emergence of so-called 'action rules' (game plans) which emerge from these verbal exchanges. Participants and Setting: Game Sense pedagogy and video tagging were used as a stimulus for student-led debates during seven lessons. A pragmatic epistemological approach underpinned iterative cycles of action research using recordings from student-led debates critical collegial discussions, and teacher reflections to inform the implementation of the approach. A "post-hoc" thematic analysis of the PHE teachers' reflections and content analysis of student debates were then conducted. Findings: The results of the action research process revealed that "organisation" and "what to tag" were key considerations in the successful implementation of the approach. The results from student interactions showed a high level of team-based positive action rules, with a low level of negative feedback. Conclusion: This study highlights the importance of critically considering technology integration, the content of student interactions, and their pedagogical implications. The integration of video tagging in diverse game-based situations can provide pedagogical and organisational challenges. However, key considerations of "organisation" and "what to tag" may help PHE teacher identify appropriate learning situations to use video tagging as a stimulus for the debate of ideas.

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.

Loop-based intelligent tutoring systems (ITSs) support the learning process using a step-by-step problem-solving approach. A limitation of ITSs is that few contents are compatible with this approach. On the other hand, recommendation systems can recommend different types of content but ignore the fine-grained concepts typical of the step-by-step approach. This work contributes to the solution of this state-of-the-art challenge by proposing an approach for the recommendation of learning objects from different areas of knowledge, considering the refined concepts of ITSs. To deal with this challenge, we formulate the learning object recommendation problem as the set covering problem that belongs to the NP-hard class problems. An exact algorithm and a greedy heuristic were properly adapted, resulting in a promising approach to solve these problems, as shown by the results. This resulted in more personalised content for students using collaborative filtering and an ontology that models their knowledge, learning styles and search parameters.

In this article, the research of multimedia teaching video content feature extraction is carried out. According to the file structure, data type, and storage mechanism of the teaching video, a program is developed to automatically extract the structural features of the teaching video content, and a storage and retrieval database is established. The research results show that the accuracy rate of various videos compiled through genie 8.0 for teaching videos exceeds 94%. The recall rate of various videos exceeds 95%. The accuracy and recall of advertisements have reached 100%. Among the elements of teaching video content features, the number of graphs is the highest, followed by film clips, accounting for 14.18. Image, vivid, and interactive multimedia teaching video technology has greatly improved teaching effectiveness, promoting students to better understand and remember knowledge points. The research results provide theoretical data support for multimedia feature extraction to optimize classroom teaching quality.

This conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self-regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm.

The rapid increase and use of educational technologies have a significant impact on students' privacy, personal information and metadata in higher education. The past two decades have also witnessed the process where information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems (LMS) and online learning tools is increasing and is stored in cloud computing platforms, software-as-a-service providers and other vendors. There are reported cases where students' personal data and information have been leaked, with possible privacy violations. Although this issue affects all students, it is important to understand the perspectives of culturally diverse students on the balancing of personal data/information and the use of technology integration in higher education. The purpose of this research was to investigate diverse students' perspectives on privacy, control of personal information, confidentiality and technology integration. The study also aimed to examine the challenges and experiences of loss of control over personal data and metadata encroachment, and protection of privacy in online education. Data were collected following a qualitative research design. Forty in-depth and semi-structured focus-group interviews (26 female and 24 male students) from four academic disciplines (Education, Social Sciences, Business, and Health Sciences) in a public university in the United States were obtained and analysed following a constant comparative analysis approach. Findings show evidence of fear and anxiety about data encroachment, challenges from bureaucratic procedures and lack of educational awareness, privacy concerns about LMS and social media. These results contribute to a new understanding regarding diverse students' knowledge, challenges and experiences on how to balance privacy, confidentiality and technology integration in higher education.

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.

Advances in Information Technology (IT) and computer science have without a doubt had a significant impact on our daily lives. The past few decades have witnessed the advancement of IT enabled processes in generating actionable insights in various fields, encouraging research based applications of modern Data Science methods. Among many other fields, education research has also been adopting different analytical approaches to advance the state of education systems. Moreover, developments in software engineering and web-based applications have made collection of education data possible at large scales. This systematic review aims to explore the 21st century's state of the art applications of text mining methods used in the field of education. We analyse the metadata of all publications that use text mining or natural language processing in educational settings to report on the key themes of application of text mining methods in educational studies providing an overview of the current state of the art and the future directions for research and applications.

This bibliometric study aims to map the value education model research to provide direction for future research, especially in education and teaching. It is essential because value is the core of education. After all, in addition to opening the window of knowledge, education also has a role in transferring values for internalizing them in the education model. Therefore, conducting studies related to the concept and model of value education is essential for an educator. The focus of this study is to examine the bibliography related to the value education model based on the Scopus database in the period 2011-2020. This research took four stages; first: using Publish or Perish application to search articles from Scopus database. Second, performing filter by setting bibliographic criteria to be analyzed. Third, checking and completing articles' metadata through the EndNote reference manager application. Fourth, perform bibliometric analysis through the VOSviewer application. Through these four stages, seven analyses were explored; the trend of publications related to the value education model, analysis of the keywords used, collaboration of authors, citation patterns, institutions, and countries that were contributors to the study of the value education model, and abstract analysis. The results of this exploration conclude that there is a tendency for academics to pay less attention to the value education model when viewed from the number of publications related to the topic. The contribution of this research can be expected to provide direction for other researchers in conducting research and development related to educational models.

Many traditional Information Systems (IS) programs are either redesigning current courses to incorporate business/data analytics or expanding curricular offerings to include business /data analytics; our IS program chose the former route to meet the demand (from employers) for business/data analytics. In that transition, a traditional systems analysis and design (SA&D) course was redesigned to focus on the procedures and skills needed to perform business/data analytics; this required changes to the focal topical areas. In 2017-18, curricular changes transitioned a traditionally focused database systems analysis and design course to incorporate business/data analytic skills. This paper focuses on the course redesign and the new business/data analytic topics, activities, and technologies; we highlight the important shifts created to the topical areas of this course. The paper emphasizes the rationale and student outcomes generated.

We developed "metadta," a flexible, robust, and user-friendly statistical procedure that fuses established and innovative statistical methods for meta-analysis, meta-regression, and network meta-analysis of diagnostic test accuracy studies in Stata. Using data from published meta-analyses, we validate "metadta" by comparing and contrasting its features and output to popular procedures dedicated to the meta-analysis of diagnostic test accuracy studies; ("midas" [Stata], "metandi" [Stata], "metaDTA" [web application], "mada" [R], and "MetaDAS" [SAS]). We also demonstrate how to perform network meta-analysis with "metadta," for which no alternative procedure is dedicated to network meta-analysis of diagnostic test accuracy data in the frequentist framework. "metadta" generated consistent estimates in simple and complex diagnostic test accuracy data sets. We expect its availability to stimulate better statistical practice in the evidence synthesis of diagnostic test accuracy studies.

The main goal of this research is to enhance the understanding of quality criteria for DB metadata for assessment and recognition as factors increasing their value in higher education (HE). To attain this goal, a case study approach centered in one HE institution was used, aiming (a) at an analysis of the "status quo" description of metadata of DBs issued by the HE institution to identify the value of DBs in terms of assessment and recognition procedures, and (b) a list of quality criteria for DB description metadata was proposed on the basis of academic research and on expert interview results. The results of the research demonstrate that in the institution under research, these criteria are not present in most cases of DB descriptions as teachers do not provide them. Distinct assessment and recognition criteria make an important quality factor for the DBs to become valid and valued digital credentials in HE.

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.

Distance education is an education model in which the lessons can be taught simultaneously using technical material without time and space restrictions. It has gained importance after the COVID-19 pandemic processes and has been implemented as a valid educational model in all educational institutions. Due to the sudden pandemic measures, distance education has brought about a lot of technical problems at unprepared educational institutions against the pandemic. In this paper, a classification approach is proposed by machine learning methods on Twitter instead of the usual structured research methods such as survey, one-on-one meeting for technical problems of distance education. The most encountered and commented distance education problem, which can be defined in different languages by the proposed method, have been analysed with Turkey example. Sentiment analysis has been made from negative and neutral tweets about distance education. The problems have been classified by natural language processing methods based on Turkish word analysis.

Scholarship of teaching and learning plays an important role in informing educational practices in every discipline. The aim of this study was to identify the trends of research on library and information science (LIS) education. A total of 1,986 articles on LIS education published from 1999 to 2018 were retrieved from bibliographic databases using topical and thesaurus-based queries. They were analyzed for their topics, methods, educational level and aspect, country, and outlet. The results showed that curriculum (what we teach) received more research attention than pedagogy (how we teach). Teaching organization of information (including cataloguing, classification, and metadata) was the most researched topic. Educational aspects of some sectors such as archives and record keeping and academic libraries have received more attention, while some other sectors such as school libraries have not been covered as widely. Survey was the most popular research method used in research articles. There were 134 articles covering LIS education in the United States, representing the greatest coverage by geographic region. While some topics such as serials librarianship and law were discussed in early 2000, in recent years topics such as political literacy, crisis management, privacy, digital humanities, and GLAM convergence have emerged in the literature.

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.

We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students (N = 2 080) and was found to be highly accurate and robust against variation in course structures, teaching and learning styles, programming exercises and classification algorithms. By using interpretable machine learning techniques, the framework also provides insight into what aspects of practising programming skills promote or inhibit learning or have no or minor effect on the learning process. Findings showed that the framework was capable of predicting students' future success already early on in the semester.

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database searching, handsearching is an important supplementary technique that helps increase the likelihood of identifying all relevant studies in a literature search. Traditional handsearching requires reviewers to manually browse through a curated list of field-specific journals and conference proceedings to find articles relevant to the review topic. This manual process is not only time-consuming, laborious, costly, and error-prone due to human fatigue, but it also lacks replicability due to its cumbersome manual nature. To address these issues, this paper presents a free and open-source Python package and an accompanying web-app, "Paperfetcher," to automate the retrieval of article metadata for handsearching. With "Paperfetcher's" assistance, researchers can retrieve article metadata from designated journals within a specified time frame in just a few clicks. In addition to handsearching, it also incorporates a beta version of citation searching in both forward and backward directions. "Paperfetcher" has an easy-to-use interface, which allows researchers to download the metadata of retrieved studies as a list of DOIs or as an RIS file to facilitate seamless import into systematic review screening software. To the best of our knowledge, "Paperfetcher" is the first tool to automate handsearching with high usability and a multi-disciplinary focus.

How can novel AI techniques be made and put to use in the library? Combining methods from data and library science, this article focuses on Natural Language Processing technologies, especially in national libraries. It explains how the National Library of Sweden's collections enabled the development of a new BERT language model for Swedish. It also outlines specific use cases for the model in the context of academic libraries, detailing strategies for how such a model could make digital collections available for new forms of research, from automated classification to enhanced searchability and improved OCR cohesion. Highlighting the potential for cross-fertilizing AI with libraries, the conclusion suggests that while AI may transform the workings of the library, libraries can also play a key role in the future development of AI.

Purpose: Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience. Design/methodology/approach: The development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement. Findings: To achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm. Originality/value: Finally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

Applying a content warning to metadata and archival descriptions is a practice that libraries increasingly embrace, even though the American Library Association considers content labeling to be censorship under the Library Bill of Rights. The language used in a content warning, such as "offensive" or "harmful," carries important implications for the responsibility the library assumes and the actions it might take. Before deciding to apply a content warning, libraries should consider a range of questions posed by such warnings and be prepared to respond to the inherent conflict they create with librarianship's commitment to intellectual freedom and anti-censorship.

In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn't delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts.

The organization of sources into layers may have an impact on the way readers evaluate conflicting documents online. Two experiments (n = 131) examined whether undergraduates use metadata from the document to evaluate the contents and embedded sources included in that document. Participants read two texts about treatments for a rare disease put forward by two neutral characters (the embedded sources). Each text was manipulated so that it was published by a trustworthy or untrustworthy document source. In Experiment 1, participants performed the task using their own criteria. In Experiment 2, they received a pre-training on how to evaluate sources. Participants used more information (cited more sources and preferred the treatment) and rated the embedded source as more trustworthy when associated to a trustworthy document, but only in Experiment 2. In conclusion, readers can strategically use multiple source layers, suggesting a networked source representation, but contingent to task specifications.

As classroom diversity increases across educational domains, additional stress is placed on instructors to provide effective and personalised learning experiences. Formative assessments can inform instructors of learning differences between student cohorts and individual students, allowing adaptation of learning experiences to individual student needs. This work is motivated by the need for faculty to implement formative assessment strategies, such that instructors can adapt the classroom-assessment environment by identifying learning goals, categorising or tagging assessment questions by learning goal categories, and organising the assessment data by tag. This paper summarises how tag-organised assessment (TOA) may be implemented and visualised to facilitate formative assessment strategies. We validated our TOA approach by interviewing instructors on needs in classroom-assessment environments and the usefulness of TOA visualisations to address assessment needs. Instructors perceived TOA visualisations as beneficial for synchronising learning goals with assessment coverage, improving student feedback, and communicating learning goals and success criteria to students.

Whilst the links between learner corpus research (LCR) and Second Language Acquisition (SLA) have long been debated, McEnery et al. (2019. "Corpus Linguistics, Learner Corpora, and SLA: Employing Technology to Analyze Language Use." Annual Review of Applied Linguistics 39: 74-92. doi:10.1017/S0267190519000096) claim that learner corpus data are not yet sufficiently integrated in SLA research. This article aims to go one way towards bridging the LCR/SLA gap by illustrating the benefits of collecting and analyzing data sets that better document multiliteracy practices. We first contextualize our work within the field of LCR where calls for more multidimensional data sets have been made. We then present a new database called MulTINCo - Multilingual Traditional, Immersion, and Native Corpus - collected in the framework of a project on Content and Language Integrated Learning in French-speaking Belgium. As our data set contains rich metadata and blends corpus data with other data types, we illustrate its potential for SLA research. In Sections 3 and 4, we describe the data collected and the interface. In the last section of the paper, we wrap up with a discussion on the methodological assets of such multidimensional data sets for SLA studies, and present directions for future research.

Humanities scholars can today engage in research inquiry using data from a range of varied collections which are often characterised by poor subject access, often resulting in systems that underperform and even effectively prevent access to data, information and knowledge. In spite of the availability of professional standards and guidelines to provide quality-controlled subject access through knowledge organisation systems (KOS), subject access in such collections is rarely based on KOS. At the same time, KOS themselves may come with problems such as being slow to update, being rigidly structured and not incorporating end-users' vocabulary. It may therefore be useful to consider methods for remediating these deficiencies in KOSs, such as collecting user-generated metadata via social tagging or complementing automated indexing techniques with manual ones. To help address the above problems, the paper discusses these challenges and points to possible solutions in different contexts. It does so by reflecting on an open educational resource (OER) devoted to this theme, titled Introduction to Knowledge Organisation Systems for Digital Humanities. It was developed as part of an EU project called DiMPAH (Digital Methods Platform for the Arts and Humanities), 2021-2023, creating seven OERs for inclusion in DARIAH Teach.

Despite questions about academic rigor, undergraduates use Wikipedia for opportunistic learning: self-regulated study to learn, relearn, or be introduced to concepts. STEM topics are potentially immune from some of Wikipedia's criticism, as many are based on well-established facts. This article evaluates 28 articles from seven STEM disciplines on four criteria important for undergraduate opportunistic learners. Some articles contained inaccuracies, while common criticisms included poor conceptual development, poor procedural explanations, poor diagrams and failing to utilize internet technologies. Often, the best advice is to read the first paragraph: the rest of the article generally did not enhance opportunistic learning.

Scholarly web-based application programming interfaces (APIs) allow users to interact with information and data programmatically. Interacting with information programmatically allows users to create advanced information query workflows and quickly access machine-readable data for downstream computations. With the growing availability of scholarly APIs from open and commercial library databases, supporting access to information via an API has become a key support area for research data services in libraries. This article describes our efforts with supporting API access through the development of an online Scholarly API Cookbook. The Cookbook contains code recipes (i.e., tutorials) for getting started with 10 different scholarly APIs, including for example, Scopus, World Bank, and PubMed. API tutorials are available in Python, Bash, Matlab, and Mathematica. A tutorial for interacting with library catalog data programmatically via Z39.50 is also included, as traditional library catalog metadata is rarely available via an API. In addition to describing the Scholarly API Cookbook content, we discuss our experiences building a student research data services programming team, challenges we encountered, and ideas to improve the Cookbook. The University of Alabama Libraries Scholarly API Cookbook is freely available and hosted on GitHub. All code within the API Cookbook is licensed with the permissive MIT license, and as a result, users are free to reuse and adapt the code in their teaching and research.

Social research methodologists have postulated that the transparency of survey procedures and data processing is mandatory for assessing the Total Survey Error. Recent analyses of data from cross-national surveys have demonstrated an increase in the quality of documentation reports over time and significant differences in documentation quality between the projects. This research note replicates previous results with an extended set of documentation-related quality indicators describing the degree of completeness of information at the consecutive steps of the survey cycle. It also extends earlier findings by indicating no significant relationship between the quality of the survey documentation and the quality of the survey itself. We analysed a meta-data set of survey characteristics, studying all available up-to-date methodological reports of 1,145 national surveys from four large-scale multi-wave projects: the European Quality of Life Survey (2003-2016), European Social Survey (2002-2018), European Values Study (1981-2017), and International Social Survey Programme (1985-2018).