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The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work.

Computer assisted assessment is becoming more and more common through further and higher education. There is some debate about how easy it will be to migrate current assessment practice to a computer enhanced format and how items which are currently re-used for formative purposes may be adapted to be presented online. This paper proposes an evaluatory framework to assess and enhance the practicability of large-scale CAA migration for existing items and assessments. The framework can also be used as a tool for exposing compromises between delivery mechanism and validity--exposing the limits of validity of modified paper based assessments and highlighting the crucial areas for transformative assessments. (Contains 1 note, 5 tables, and 1 figure.)

High Speed 2 (HS2) will be the largest programme of historic environment investigation and recording works ever undertaken in the UK. It is certain that the creation of HS2's historic environment physical and digital archive (High Speed Two Ltd. 2023) is an integral part of the lasting legacy of the programme, which presents an unprecedented opportunity for significant knowledge creation. HS2 historic environment works that have been undertaken for Phase One of HS2 between London to the West Midlands has resulted in a substantial digital archive, including Geographic Information Systems (GIS) data. This data paper highlights the GIS spatial datasets generated from the HS2 Phase One historic environment fieldwork programme. It explains the technical components of the datasets which are deposited with the Archaeology Data Service (ADS).

In this commentary, the authors discuss the implications of A. S. Green, E. Rafaeli, N. Bolger, P. E. Shrout, and H. T. Reis's (2006) diary studies with respect to memory. Researchers must take 2 issues into account when determining whether paper-and-pencil or handheld electronic diaries gather more trustworthy data. The first issue is a matter of prospective memory, and the second is a matter of reconstructive memory. The authors review the research on these issues and conclude that regardless of the type of diary researchers use, several factors can conspire to produce prompt--but inaccurate--data.

For the interval 1962-2022 y, during the continuos publication last 60 years, in Metalurgija Journal Authors from 40 countries from Mexico to China (all 5 continent ) have publish. The goal of the Article is give List of Papers published in this interval, 199 issues or 238 numbers, 2721 scientific and technical, and 287 contributions (total 3008 papers ) of Authors whose investigation results and ideas have been examined and found on the pages of this Journal. Thanks for all.

The paper argues that learning analytics as a research field can benefit from a theory-informed shared language to describe sensemaking of learning and teaching data. To make the case for such shared language, first, I critically review prominent sensemaking theories to then demonstrate how studies in learning analytics do not use coherent descriptions of sensemaking, eclectically combining the paradigms that have underlying differences. I then propose a conceptualization of sensemaking that overcomes the differences between these theories and explains how the concepts of "activity system," the "definition of the situation" and "affordances" can be used to capture individual differences in sensemaking. The paper concludes with a preliminary framework and examples demonstrating its utility in raising new theoretical questions, informing design principles and providing shared language for researchers in learning analytics.

This study examines data from the California EMS Information System (CEMSIS) to identify factors that influence prehospital time for EMS events related to motor vehicle collisions (MVCs). While only 19 percent of the United States population resides in rural areas, over half of all traffic fatalities involve rural motor vehicle collisions. Rural and urban MVCs result in similar injury severities, however relative inaccessibility of trauma centers and prehospital EMS time (activation, response, and transport time) likely contribute to the generally higher mortality rate in rural areas. For the present study, 24 CEMSIS data variables were requested, many of which involved missing data, which severely restricted the potential analysis of the impact of EMS response times. However, the findings did show that average overall EMS time (including response, scene and transport time) were approximately twice as long for collisions in rural zip codes compared with urban zip codes. Several limitations influence the interpretation of these results. Data on prehospital EMS times is missing for much of the state—even for zip codes with records of EMS events, data is likely incomplete. In addition, zip code level location data is insufficient for adequate study of the effects of the built environment and road network on prehospital time. Furthermore, according to the National EMS Information System (NEMSIS) User Manual, the national dataset suffers from selection and information bias, which are likely also present in the CEMSIS data. Although the present study cannot analyze the effect of longer prehospital times on patient outcome, other research has found that longer prehospital times may negatively impact patient health. Recommendations for reducing time from injury to appropriate medical care in rural areas include improving cell phone coverage, compliance of rural 911 center with FCC wireless, use of GPS technology, and integration of automatic vehicle location and computer aided navigation technologies into all computer-aided dispatch systems. In addition, CEMSIS should improve the coverage of their dataset and ensure that all EMS activities are recorded. To expand the type of analyses that can be conducted using CEMSIS data, EMS records must include fields that allow them to be linked to hospital and police datasets. When such data becomes available, research must be conducted to determine whether prehospital time is significantly related to patient outcome following motor vehicle collisions.

This project interrogates the United States' national fixation on the answer to the question: Who are you? In this article, it is posed that identity documentation practices arising out of the Chinese Exclusion Act era cast identity as an empirical and immutable phenomenon, specifically in response to the racialization of American-born Chinese settlers as duplicitous, through the mechanisms that information is collected, the actual information itself, and the cross-references or connections created between cases. Through tracing this lineage, racialized identification data is identified and theorized as part of hegemonic data regimes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has fast-tracked the development of digital applications and inspired everyone to adapt to the technologies to curb the spread of outbreak. As this crisis intensifies, the rapid usage of digital devices and apps has echoed the serious concerns about civil liberties, privacy, and data protection. Considering the situation, this research aimed to explore the internet using habits of the youths of West Bengal, a state in eastern India, during COVID-19. Besides, the paper explored their experiences of using various digital applications, the fundamental digital literacy and how safely they protect data from breaches. Thus, the paper presents the results by conducting an online survey among the youths in West Bengal. The result, from 215 participants, highlighted that the increased use of these digital applications has not matched the demand for digital privacy literacy among the young generation of the state. While this pandemic has raised their concerns over digital privacy and data protection, yet they do not undertake any strong protection mechanisms to safeguard them digitally. Besides, this paper suggests suitable plans to raise awareness among this generation and form a healthy digital citizenship with a proper regulatory framework as it is the need of the hour.

Examines the characteristics of rejected manuscripts submitted to the "African Journal of Library, Archives and Information Science." Most of the papers were rejected because they contributed nothing new to knowledge (65.5%), used unreliable data (13.1%) and lacked focus (13.1%). There were no remarkable differences with regard to status and affiliations between authors of rejected and accepted papers. (Author/AEF)

Employment and training services offered through Extension are part of and continue a long tradition of policy-focused employment and job training. This paper chronicles the successes of UT Extension's work as a third-party partner in the delivery of workforce development programming geared toward individuals receiving Supplemental Nutrition Assistance Program (SNAP) benefits. The paper begins with an overview of the federal program and a discussion of how Tennessee forged a state-level partnership for the delivery of workforce services. Data showing program success including number of participants served, supportive services offered, and economic impact are highlighted. Finally, lessons learned are outlined.

In today's data-sharing paradigm, personal data has become a valuable resource that intensifies the risk of unauthorized access and data breach. Increased data mining techniques used to analyze big data have posed significant risks to data security and privacy. Consequently, data breaches are a significant threat to individual privacy. Privacy is a multifaceted concept covering many areas, including the right to access, erasure, and rectify personal data. This paper explores the legal aspects of privacy harm and how they transform into legal action. Privacy harm is the negative impact to an individual as a result of the unauthorized release, gathering, distillation, or expropriation of personal information. Privacy Enhancing Technologies (PETs) emerged as a solution to address data privacy issues and minimize the risk of privacy harm. It is essential to implement privacy enhancement mechanisms to protect Personally Identifiable Information (PII) from unlawful use or access. FIPPs (Fair Information Practice Principles), based on the 1973 Code of Fair Information Practice (CFIP), and the Organization for Economic Cooperation and Development (OECD), are a collection of widely accepted, influential US codes that agencies use when evaluating information systems, processes, programs, and activities affecting individual privacy. Regulatory compliance places a responsibility on organizations to follow best practices to ensure the protection of individual data privacy rights. This paper will focus on FIPPs, relevance to US state privacy laws, their influence on OECD, and reference to the EU General Data Processing Regulation. (GDPR).

An increasingly complex and demanding research landscape has seen university libraries rapidly evolve their services. While research data management, bibliometrics, and research impact services have predominantly featured in the literature to date, the full scope of support libraries are currently providing to their institutions is unknown. This paper aims to present an up-to-date view of the scope and extent of research support services by university libraries across Australia and Aotearoa New Zealand. A coding process analyzed content data from university library websites. Eleven research support areas were identified. Service delivery is split between synchronous and asynchronous modes. This paper describes a lived experience of an evidence-based library and information practice approach to improving research support services at two Australian university libraries, and while it highlights continued maturation of research support services, more research is needed to better understand influences on service development.

This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.

Purpose: In today's digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students' data awareness. Design/methodology/approach: The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students' awareness and understanding of and reflection on data practices of data-driven digital artefacts. Findings: The study's findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Originality/value: Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

Chinese students studying abroad have been increasing rapidly in the past decades and become a significant financial contribution to receiving countries. Accordingly, understanding their enrollment choice is essential to facilitate college marketing and admission strategies. Though the decision process is believed to be different from domestic students, empirical analysis of Chinese students' enrollment choices is still lacking. This paper fills the void by examining the influential factors of Chinese students' enrollment choice with novel student-level data. We find that in addition to factors domestic students typically consider, such as financial aid and academic quality, Chinese students particularly emphasize college ranking, reputation, and location in their decision process. Furthermore, unlike domestic students who usually prefer colleges with proximity to home, Chinese students' location preference is linked to job prosperity. We also find that the impact of the factors varies for students from different regions of China, which can be attributable to uneven economic development within the country.

One way to achieve equitable design is to directly include users who will be impacted the most in the planning and facilitation of a project. Common financial, logistical, and/or temporal constraints reveal that direct inclusion of the people most impacted is not always possible. If this barrier arises, one promising alternative is the creation and use of personas. Using a vignette and case study qualitative methodological approach, three researchers at a large urban university in the Pacific Northwest detail personas and journey mapping as an equitable design practice during a LMS migration on a rapid development timeline. This paper details how personas were created using empirical data, how journey mapping impacted various teams, and how centering equity better prepared staff to support instructors throughout the migration while addressing the student learning impact.

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing. [ABSTRACT FROM AUTHOR]

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Dyslexia is the most prevalent disorder in the world that causes difficulties with reading, writing, and spelling. Pupils with dyslexia show trouble with their cognitive skills. Various interventions were already introduced for their treatment but dyslexia is still a trending disorder. The available interventions utilized for these pupils' learning open the research area for the current state-of-art of learning interventions for pupils with dyslexia. The results of this Systematic Literature Review show the trending interventions, sensory approaches utilized, and difficulties for pupils with dyslexia learning. Papers published over a period of 5 years were reviewed and their data was collected using a rigid systematic process. Based on the gathered data, several analyses were conducted. The search shows that nowadays, technological-based interventions are trending specifically apps and games, in parallel haptics technology is in its very initial stage. The most predominant sensory approaches were visual and auditory, followed by kinesthetic and tactile, mainly intervening with non-technological and technological interventions. There are still many open issues and research opportunities in the field of learning interventions for pupils with dyslexia, as most researchers utilized the visual and auditory approaches for the feedback and guidance of these pupils, while they lack to utilize the kinesthetic and tactile.

In the digital era like today the growth of data in the database is very rapid, all things related to technology have a large contribution to data growth as well as social media, financial technology and scientific data. Therefore, topics such as big data and data mining are topics that are often discussed. Data mining is a method of extracting information through from big data to produce an information pattern or data anomaly

In using software to write a scientific, technical, or other scholarly document, authors have essentially two options. They can either write it in a 'what you see is what you get' (WYSIWYG) editor such as a word processor, or write it in a text editor using a markup language such as HTML, LATEX, Markdown, or AsciiDoc. This paper gives an overview of the latter approach, focusing on both the non-visual accessibility of the writing process, and that of the documents produced. Currently popular markup languages and established tools associated with them are introduced. Support for mathematical notation is considered. In addition, domain-specific programming languages for constructing various types of diagrams can be well integrated into the document production process. These languages offer interesting potential to facilitate the non-visual creation of graphical content, while raising insufficiently explored research questions. The flexibility with which documents written in current markup languages can be converted to different output formats is emphasized. These formats include HTML, EPUB, and PDF, as well as file formats used by contemporary word processors. Such conversion facilities can serve as means of enhancing the accessibility of a document both for the author (during the editing and proofreading process) and for those among the document's recipients who use assistive technologies, such as screen readers and screen magnifiers. Current developments associated with markup languages and the accessibility of scientific or technical documents are described. The paper concludes with general commentary, together with a summary of opportunities for further research and software development.

Abstrak Setiap orang berhak atas perlindungan diri pribadi, keluarga, kehormatan, martabat, dan harta benda yang di bawah kekuasaannya, serta berhak atas rasa aman dan perlindungan dari ancaman ketakutan untuk berbuat atau tidak berbuat sesuatu yang merupakan hak asasi. Inilah rumusan dari Pasal 28 G Ayat (1) dari Undang-Undang Dasar, yang dinyatakan Perubahannya yang Kedua dalam suatu Ketetapan Majelis Permusyawaratan Rakyat Republik Indonesia. Dalam rumusan ketentuan ini orang dapat menyuling semua asas yang mengatur perlindungan data dalam Sistem Hukum Pancasila.Di samping rumusan ketentuan di atas, sejumlah rumusan kaedah hukum lainnya berisi asas-asas yang sama dapat juga ditemukan dalam beberapa Undang-Undang yang berlaku dalam sistem hukum Indonesia. Antara lain, UU No. 8 tahun 2011 tentang ITE. Penulis artikel ini berpendapat bahwa seluruh rumusan ketentuan dimaksud adalah bentuk-bentuk perwujudan diri dari jiwa bangsa (Volksgeist) tempat orang dapat menemukan asas-asas dan kaidah yang mengatur perlindungan data dalam Sistem Hukum Pancasila. Satu dari asas hukum yang melindungi data pribadi dapat digunakan untuk memecahkan skandal terkini, yaitu Panamapapers. Abstract Every person has the rights to get protection on his personal/privacy, family, honor, dignity and properties under his power, and has rights to feel secure and to get protection from fear of any threat in order to do or not to do something related to their fundamental rights. This is the formulation of the Article 28 G (1) of the Indonesian Basic Act, mentioned in its Second Amendment mentioned in the Decision of the People's Consultative Assembly of the Republic of Indonesia. In this stipulation one could distil all the legal principles governing data protection in the Pancasila Legal System. Apart from the stipulation mentioned above, some further formulation of legal rules contained similar principles may also be found in several Acts in the Indonesian legal system. Among those Acts, is the Act number 8 of 2011 concerning Information and Electronic Transaction. This writer argues that all of the stipulations are forms of manifestation of the spirit of the Indonesian people (Volksgeist) in which one could find rules and principles governing data protection in the Pancasila Legal System. One of the principles of Law that protect the personal data could be used in order to solve the rescent scandal called Panamapapers.

The present study proposes a Fuzzy Data Envelopment Analysis (FDEA) approach for analyzing the performance of 8 selected paper mills of India. The proposed approach named FDEA considers the use of fuzzy weights in the objective function and makes use of alpha cut to decide the fuzzy interval. The efficiency of paper mills is evaluated based on 3 input parameters (raw material, energy consumption, and water consumption) and 4 output parameters (paper production, Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Greenhouse Gas (GHG) emissions). Further, this paper also analyzes the effect of negative outputs, like BOD, COD, and GHG on the efficiency of paper mills. The study indicates that FDEA can be used efficiently for evaluating the performance of a particular sector under similar conditions. [ABSTRACT FROM AUTHOR]

Faculty have a great deal of control over their lectures, lecture notes, and slides. This article discusses a coming wave of recording devices and other classroom technologies--this time wielded by the students--which will test this control and force serious conversations about how to best help students learn, what it means to own an idea, and what is meant when talking about developing a community of learners on campus. The harbinger of this wave is the Livescribe Pulse smart pen, created by an MIT engineer and initially aimed directly at the college student market. The smart pen points a tiny camera at specially marked paper, captures what is written, and converts the writing to PDF files and plain text in what is being called paper-based computing. The pen comes with microphones that capture audio and software that synchronizes it with the written notes. A student can replay an entire lecture at a later time, either by interacting with the written notes or through a computer. The pen's software also makes it easy to share recorded class sessions with other students at the Livescribe website or through Facebook. (Contains 4 endnotes.)

The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work. [ABSTRACT FROM AUTHOR]

In advanced missions, handling thin and soft membranes is difficult for robots. This task is composed of several sub-tasks, including turning and removing a sheet from a pile of papers on a table, folding paper, and sticking two pieces of paper together. In this study, a hand robot turns and removes a sheet from a pile of papers, and ensures that only one sheet is grasped. To perform this task, two robotic fingers compress the piled papers in the normal direction of the table with a specific force as they close. We therefore incorporated position-based force control into our system; for the force controller, we adopted stiffness control that calculates position modification proportionally to the difference between the target force and an external force measured by the tactile sensor. With this controller, the robot was able to remove a sheet from a pile of papers and ensure that only one sheet was removed. Using this discrimination, the maximum-minimum shearing forces are better classified than with normal force distribution deviation. [ABSTRACT FROM AUTHOR]

This paper offers a methodological framework to research data practices in education critically. Data practices are understood in the generic sense of the word here, i.e., as the actions, performances, and the resulting consequences, of introducing data-producing technologies in everyday educational situations. The paper first distinguishes between data infrastructures, datafication and data points as three distinct, yet interrelated, phenomena. In order to investigate their concrete doings and specificities, the paper proposes a topological methodology that allows disentangling the relational nature and interwovenness of data practices. Based on this methodology, the paper proceeds with outlining a methodical toolbox that can be employed in studying data practices. Starting from nascent work on digital education platforms as a worked example, the toolbox allows researchers to investigate data practices as consisting of four unique topological dimensions: the Interface of a data practice, its actual Usage, its concrete Design, and its Ecological embeddedness - IUDE.

Purpose: This paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across representations and how those relations change diachronically. To develop an appropriate modeling approach, the paper critically examines Max Boisot's I-Space model, a conceptual model for understanding the interplay among knowledge assets used by a population. I-Space maps information in three dimensions (abstraction, codification and diffusion). It is not directly adoptable for case study methodology due to three fundamental disjunctures: in theory, methodology and unit of analysis. However, it can be adapted for qualitative research by substituting analogues for abstraction, codification and diffusion. Design/methodology/approach: Using an example from early-stage technology entrepreneurship, this paper first reviews network, flow and matrix models used to systematically visualize case study data. It then presents Boisot's I-Space model and critiques it from the perspective of qualitative workplace studies. Finally, it adapts the model using measures that have been used in qualitative case studies. Findings: This paper notes three limitations of the I-Space model when applied to empirical cases of workplace learning. Its theory of information does not account well for how people use representations synchronically for learning. It is a conceptual framework, and the tentative attempts to use it for mapping representations have been used in workshops, not for systematically collected data. It does not adequately bound a case for analysis. Thus, it can be applied analogically but not directly for mapping representations in qualitative case studies. Practical implications: This paper identifies a possible way to develop I-Space for strategically mapping representations in qualitative case studies, using measures analogous to the I-Space axes to reflect observable behavior. Originality/value: In providing a methodological critique for one model of knowledge management, this paper also develops criteria for appropriate modeling of meaningful artifacts in the context of qualitative studies of workplaces.

The Internet of Things (IoT) has pervaded practically all aspects of our lives. In this exploratory study, we survey its applications in the field of education. It is evident that technology in general, and, in particular IoT, has been increasingly altering the educational landscape. The goal of this paper is to review the academic literature on IoT applications in education to provide an understanding of the transformation that is underway. Using topic modeling and keyword co-occurrence analysis techniques, we identified five dominant clusters of research. Our findings demonstrate that IoT research in education has mainly focused on the technical aspects; however, the social aspects remain largely unexplored. In addition to providing an overview of IoT research on education, this paper offers suggestions for future research.

Robust indicators are important for identifying disadvantaged pupils in education, and for ensuring that they are rightly receiving relevant state-funded assistance. This paper compares the quality and completeness of data from England on student eligibility for free school meals (FSM) based on an administrative census, with more all-encompassing household income measures, from a smaller sample of young people. The first measure comes from the National Pupil Database (NPD), and the second from Next Steps (NS). The two datasets are linked at the individual student level. In this restricted group, FSM data is more complete (97%) than household income (47%). The bias created by missing data on income in NS calls into question its more general usefulness for analysts. FSM cannot be read neatly from income, such as referring to an income below a certain level, and vice versa. Many reportedly low-income children are not listed as FSM-eligible. However, the two values are linked, while each also provides unique information. Both measures predict attainment at school, to some extent. The paper concludes that FSM is the more practical measure at present, but also considers how access to limited income data could be made more widespread while maintaining individual data rights.

This paper interrogates whether technical democracy is well-suited to contend with possible technical controversies, including the increasing prevalence of EdTech in public schooling. Drawing across Habermasian 'deliberative democracy' that seeks consensus-derived resolution, as well as more agonistic approaches that embrace conflict as the means for pursuing pluralistic values, we use this paper to problematise technical democracy for its potential to produce agonistic and pluralistic im/possibilities. Drawing on case studies developed from our previous research into digital platforms and infrastructures, we consider what an alternative to technical democracy might offer towards realising democratic ideals through what we call technical agonism. Using these thinking tools, we argue that a move towards technical agonism would centre dissension and scepticism as fundamental features of liberal democratic institutions, while also providing opportunities for schooling to be reimagined in ways that are more democratic and ethical.

This paper examines the concept of open-source data interoperability in the United States' K-12 education domain, specifically addressing the implications of interoperability for data justice. The term 'data justice' is a relatively recent coinage; the framing and meaning of this term are still evolving, and it has not yet been applied in the educational domain. Building on the nascent research and theorizing in the fields of both data justice and interoperability in educational contexts, this paper provides an overview of the current state of this intersection. Additionally, the authors draw on their direct experiences implementing interoperability initiatives in several U.S. states in order to build a foundational understanding of the risks and opportunities for data justice in the realm of interoperability and data standards in education. The paper concludes with a call for more research to be completed on this complex sociotechnical topic.

In the recent Australian 2021 census, the socio-technical construct of algorithmically driven decision-making processes made LGBTQI+ data as a category of diversity, inclusion and belonging an absent presence. In this paper, we position the notion of 'data justice' in relation to the entrenchment of inequalities and exclusion of LGBTQI+ lives and consider the implications of LGBTQI+ data being missing in action. As we look at the notion of 'data justice', we consider five critical socio-technical imaginaries with different kinds of data to think through the implications of technical democracy, data justice and post-automation. Finally, we consider the imaginary of citizenship when LGBTQI+ data is habitually missing in action from systematic power integrated into forms of governance. This paper positions 'data' not as a static 'object or process' but as a dynamic ecology that carries with it a multi-faceted set of coded meanings requiring constant review and reconsideration.

The launch of new tools in late 2022 heralded significant growth in attention to the impacts of generative AI (GenAI) in education. Claims of the potential impact on education are contested, but there are clear risks of inappropriate use particularly where GenAI aligns poorly with learning aims. In response, in mid-2023, the Australian Federal Government held an inquiry, calling for public submissions. This inquiry offers a lens onto the policy framing of GenAI in education and provides the object of investigation for this paper. We use the inquiry submissions, extracting structured claims from each. This extraction is provided as an open data set for further research, while this paper focuses on our analysis of the policy recommendations made.

The paper introduces a novel approach to university rankings that considers a university's contribution to sustainable development. It addresses the usual controversies surrounding the construction of rankings using composite indicators. The conventional approach typically involves normalizing sub-indicators and applying subjective weights for aggregation, which raises concerns about the reliability of the rankings. In response to this issue, we propose an alternative method based on Data Envelopment Analysis (DEA) that utilizes flexible weights. Our approach is applied to the data from the UI-GreenMetric World University Ranking. We initially employ a general Benefit of the Doubt DEA model and subsequently enhance its discrimination power by computing super-efficiency. In the third model, we impose weight restrictions on sub-indicators. The results of our analysis offer valuable insights for all stakeholders, as illustrated by the implications derived for Czech universities included in the sample. Furthermore, we compare the results of universities in various European countries, establishing a connection between rankings and the fulfillment of Sustainable Development Goals (SDG) within individual countries. This research contributes to a more comprehensive understanding of the relationship between university performance, sustainability, and the associated implications for policy and benchmarking.

School exclusion reduction in Scotland--and especially in the city of Glasgow--has received substantial media and policy attention in recent years. In London in particular, multiple governmental agencies have explicitly expressed a desire to replicate the exclusion reduction which recently occurred in Glasgow, often citing the connection between school exclusion and violence as a key motivating factor. In this paper, after presenting the statistical trends in school exclusions in Scotland, England, Glasgow and London, we mobilise original interview data to (1) explain how school exclusion reduction occurred so rapidly in Glasgow between 2007 and 2019, and (2) explore whether a similar reduction in exclusions could occur in contemporary London. We apply a theoretical framework to these issues which derives from Peters' work on policy coordination, allowing us to compare the conditions in Glasgow and London for well-coordinated pan-city exclusion reduction. Building on previous research which has contrasted national school exclusion policies in Scotland and England, we conclude that policy conditions surrounding school exclusion in the two cities differ substantially. There are substantial barriers to significant exclusion reduction in London, relating to both city- and national-level factors. There barriers include competition between different agencies working in relevant policy spaces; the fragmentation of the city's education system; the need for better incentivisation of inclusion by Ofsted and the Department for Education; and particular challenges to reframing the issue of school exclusion in London.

Purpose: In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches - media literacy, the German "(politische) Bildung" and Freirean "critical pedagogy" - and empirical analyses of online educational resources about datafication. Design/methodology/approach: The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings: The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value: The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.

Internet users are becoming ignorant with their data and the transparency of information due to the nature of high-speed internet today. Regrettably, internet users are deceived by engineering tactics performed by highly trained people, namely cybercriminals. Thus, in order to combat phishing attacks, internet users should be educated on security concerns, the influence of social engineering and anti-phishing knowledge. This paper presents a literature review of phishing, a type of cyber fraud, covering the types of phishing, the implications and governance. This study benefits the public to mitigate phishing attacks and increase phishing awareness.

This paper reports on the perceptions of Spanish academic libraries regarding Open Science (OS). OS is irrupting into academia and academic librarians need to support researchers. On the other side, researchers need to be ready to change their scientific behaviour in relation to publications and research data. We conducted a focus group with 8 academic librarians. We also sent a survey to (N = 67) academic libraries, obtaining a response rate of 71.6%. In the survey, we asked for drivers and constraints for OS services as well as for any training taking place. Our results show that facilitators are the system relationships (SD = 4.74) and internal promotion of systems relationships (SD = 4.54). In relation to the level of knowledge of OS, both researchers (SD = 3.27) and librarians have a high level in terms of the OA area (SD = 4.15) but little development of the rest of the components of OS. On the other hand, in relation to training librarians, results indicated that OA (SD = 4.79), Open Data (SD = 4.79) and new evaluation models (SD = 4.79) should be part of the training for researchers. The results of the focus group reinforce some of the indicators mentioned. We conclude that academic libraries may train researchers in OS through the acquisition of new skills and trainers-training and with the strategic support of the university. We argue that academic incentives and a change in research accreditation are also needed to shift researchers' perceptions in relation to OS.