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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).

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 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.

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).

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.

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.

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.

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.

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.

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.

This position paper uses the concept of "hidden curriculum" as a heuristic device to analyze everyday data-related practices in formal education. Grounded in a careful reading of the theoretical literature, this paper argues that the everyday data-related practices of contemporary education can be approached as functional forms of data literacy education: deeds with unintentional educational consequences for students' relationships with data and datafication. More precisely, this paper suggests that everyday data-related practices represent data as cognitive authority and naturalize the routines of all-pervading data collection. These routines lead to what is here referred to as "data (il)literacy" -- an uncritical, one-dimensional understanding of data and datafication. Since functional data (il)literacy education takes place subconsciously, it can be conceptualized as a form of hidden curriculum, an idea that refers to lessons taught and learned but not consciously intended to be so.

Data-informed decision-making in teachers' practice, now recommended by different teacher inquiry models and policy documents, implies deep practice change for many teachers. However, not much is known about how teachers perceive the different steps that analytics-informed teacher inquiry entails. This paper presents the results of a study into developing an Analytics Model for Teacher Inquiry (AMTI), which was then used to understand how teachers (N=10) construe the steps in the model and to explore the possible constraints as well as incentives for TLA-informed teacher practices (Teaching and Learning Analytics). In the final iteration experts (N=7) and teacher-researchers (N=2) tested and evaluated the developed model. Their feedback was used to improve the model and provide example cases with insights into possible scenarios for TLA-informed analyses of teaching.

Understanding data structures is fundamental for mastering algorithms. In order to solve problems and tasks, students must be able to choose the most appropriate data structure in which the data is stored and that helps in the process of the solution. Of course, there is no single correct solution, but in many cases, it is an important step to find the most efficient data structure to solve the task. In this paper, I will present the most common data representations and data structures that can help get to know them, to master them, and then to reuse them for solving different tasks.

The paper investigates how the order of numerical data in word problems on division of a whole into unequal parts affects achievement and reasoning of 14-16-year old pupils. The variable was altered in two word problems, in one of which also the context was changed (psychological variable) and in the latter "if-clause" is or is not used (linguistic variable). 182 pupils were involved in the experiment. The solutions were analysed quantitatively using Item Response Theory as well as qualitatively. The data suggest that pupils' success is affected by the order of numerical data in the statement in an unfamiliar context. The presence of "if-clause" in the statement was studied in a two-level problem. The order of numerical data played its role in case of formulations without "if". The results of the experiment are of interest for mathematics education as well as for construction of tests. The paper is an extended version of the paper by Novotná (2018).

The usage metrics for open educational resources (OER) are often either hidden behind an authentication system or shared intermittently in static, aggregated format at the repository level. This paper discusses the first year of University of Michigan's project to share its OER usage data dynamically, publicly, to synthesize it across different levels within the repository hierarchies, and to aggregate in a method inclusive of content hosted on third-party platforms. The authors analyze their user research with a target audience of faculty authors, multimedia specialists, librarians, and communications specialists. Next, they explore a stratified technical design that allows the dynamic sharing of metrics down to the level of individual resources. The authors conclude that this framework enables sustainable feedback to OER creators, helps to build positive relationships with creators of OER, and allows the institution to move toward sharing OER on a larger scale. [This paper was presented at the OpenCourseWare Consortium Global Conference (Ljubljana, Slovenia, April 23-25, 2014).]

Currently "Big Data" is an emerging field that presents several Information Technology challenges regarding the capture, storage search, structure, and visualization of this data. The real challenge for organizations is to find ways to extract value from it and provide better services to their clients. The data generated in academic and other institutions is vast and complex. Libraries face new challenges as they seek to determine their role in the handling of Big Data within their organization and use it to develop services. Thus, in most organizations, libraries will not have the knowledge to build new services unaided. Furthermore, libraries have always been information handlers and technology adopters; therefore, Big Data technologies will certainly affect their context. The purpose of this paper is to explore all these issues through a systematic literature review, unveiling the theories that underpin the paper's argument. It attempts to answer several research questions, such as how librarians are involved in the Big Data era? And what are the future research developments of Big Data within the library context? The study considered only papers published between 2012 and 2018 in English and presents the collected literature by grouping them according to the type of library each paper refers to. Thus, it identifies new and evolving roles in the context of all types of libraries. In addition, the study presents several interesting tables, which aim to help librarians locate relevant articles that will inform their practice and guide service development for users of large and complex datasets.

This paper presents findings from interviews with US Research Integrity Officers (RIOs) on their overall responsibilities as well as perspectives on Data Management Plans (DMPs). DMPs are formal documents describing the roles and activities for managing data during and after research. DMPs are now a required research criterion by many funding agencies globally. A purposive sample of Research Integrity Officers (RIOs) from the top ten US private and public universities were recruited for interviews using an open-ended questionnaire related to their job duties and perspectives on data management plan implementation and evaluation. Responses from 12 participants were transcribed, anonymized, and coded in NVivo. RIO backgrounds, duties, and perspectives varied. The mode number of staff/ faculty people dedicated to the RIO role at these institutions was a halftime appointment. All RIOs had some responsibilities related to Authorship, Publication, and Inventorship and Integrity and Information with 11 participants also responsible for offering some Responsible Conduct of Research (RCR) training. Most RIOs assumed that Principle Investigators are responsible for DMP compliance during sponsored projects as well as the long-term data management after a project ends. None of the twelve participants has received any Research Data Management training. Given the sea change in research practices, RIOs should have more training as data-intensive research emerges and DMPs become commonplace.

When writing about transformation in higher education (HE) in South Africa, it is quite popular to mention the fall of apartheid, and perhaps also 1994, as a starting point for significant change. I, myself, have made this mistake (see Bernard, 2015). However, the recent #FeesMustFall protests highlighted that many approaches to transformation have been superficial at best, and extremely problematic at worst (Luckett & Naicker, 2019; Luckett, 2019). This is because they have done little to acknowledge the legacies that colonial modes of thinking have had, and continue to have, on the everyday lived experiences of students in spaces that still feel alienating to them. In April 2020, when the doors of South African universities closed to all, and during a swift and mass migration away from university campuses to Virtual Learning Environments (VLEs), I was presented with the opportunity to critically reflect on the impact that increased use of VLEs can have on the transformation agenda in the HE sector. My approach takes up Tumubweinee and Luescher's (2019, p. 2) argument that many initiatives aimed at transformation in HE have failed, because they do not pay sufficient attention to the where of transformation. Thus, like Tumubweinee and Luescher, I locate my reflection on VLEs in the postmodern, sociopolitical understandings of 'space' evident in the work of Lefebvre (1991), but more specifically his notions of conceived and abstract space. In doing so, issues of identity and coloniality are brought to the fore. My approach is critical in that it "implies possibilities, and possibilities as yet unfulfilled" (Lefebvre, 2002, pp.18-19).

In recent decades, higher education institutions around the world have come to depend on complex digital infrastructures. In addition to registration, financial, and other operations platforms, digital classroom tools with built-in learning analytics capacities underpin many course delivery options. Taken together, these intersecting digital systems collect vast amounts of data from students, staff, and faculty. Educators' work environments--and knowledge about their work environments--have been shifted by this rise in pervasive datafication. In this paper, we overview the ways faculty in a variety of institutional status positions and geographic locales understand this shift and make sense of the datafied infrastructures of their institutions. We present findings from a comparative case study (CCS) of university educators in six countries, examining participants' knowledge, practices, experiences, and perspectives in relation to datafication, while tracing patterns across contexts. We draw on individual, systemic, and historical axes of comparison to demonstrate that in spite of structural barriers to educator data literacy, professionals teaching in higher education do have strong and informed ethical and pedagogical perspectives on datafication that warrant greater attention. Our study suggests a distinction between the understandings educators have of data processes, or technical specifics of datafication on campuses, and their understanding of big picture data paradigms and ethical implications. Educators were found to be far more knowledgeable and comfortable in paradigm discussions than they were in process ones, partly due to structural barriers that limit their involvement at the process level.

Relying on the nation's first judicial big data research base for people's courts in Southeast University, Southeast University Law School has set up a training direction for graduate students in legal big data and artificial intelligence, and explored the "three-dimensional, small-scale, wide-ranging, and large-scale ecology." The concept of "meta-integration" of legal postgraduate talent training will build a "four-in-one" legal big data and artificial intelligence postgraduate talent training system, so that the leading position of legal big data and artificial intelligence in leading disciplines will be gradually established, and the training effect of leading talents will be established. In addition, the establishment of a teaching feedback mechanism can also provide important guidance for how to improve artificial intelligence in legal education. This paper applies the data mining technology to the legal education in colleges and universities and comprehensively expounds the basic theories and methods of data mining.

This paper provides an example of a global data sharing project with an educational transformation agenda. This agenda shaped both the design of the shared dataset and the experience of sharing the common dataset to support multiple perspective inquiry and enable integrative and critically reflexive research-to-practice dialogue. The shared dataset contains multiple disciplinary instances of "design review conversations"--digital videos of conversations between those who give and those who receive feedback, guidance, critique, or mentoring during a design review event. International design researchers were invited to apply their expertise to the dataset and participate face-to-face in a multi-day symposium for catalyzing conversations connecting design thinking, teaching, and learning. In this paper we describe this data sharing project, highlighting (1) the principles that guided the design of the shared dataset; (2) challenges encountered with implementing the project; and (3) the approach for broadening the dataset experience from sharing to enabling learning partnerships. We conclude with insights from this experience for improving methodologies for global data sharing and designing data sharing for educational transformation.

This paper addresses questions of data sharing from the perspective of a former NSF program officer. A brief comparison of policy and research perspectives is made to highlight different values in these two communities. Data sharing is framed as one means to support dialog between researchers and those involved in policy. Other uses of data sharing to those involved in policy are then outlined; it is seen that the role of data sharing is growing in a policy landscape in which program evaluation is becoming increasingly important. Despite the desire of Federal agencies to support data sharing, there are legal, regulatory, and ethical constraints and tensions; these are briefly outlined. To help navigate these tensions, data sharing is framed in terms of three value propositions: help tell stories, create usable ontologies, and support networks. Each of these value propositions is discussed and several heuristics are proposed that may help emerging data sharing efforts have more value for those who serve engineering education in a policy role. The paper concludes by exploring the larger question of data at scale, the need to connect data across scales, and the philosophical issues that underlie any future attempts to share data.

In the last fifty years, the remarkable development of digital technology, that has permeated practically all social, economic, and cultural, political, and technological realms is producing several phenomena that have a direct impact in education. In this paper, I discuss first, the fact that more and more we refer to digital technology as just 'technology", as if the rest of the many organizational, symbolic, artefactual, and biotechnological developments were something "natural". Second, I refer to the rise and spread of technological solutionism in education and a growing discourse that sees every new digital technology as the arrival of the Promised Land, as the panacea to solve the problems of education. I go on analysing the collateral effects of this discourse in the educational practice, with an especial reference to persuasive technologies and Big Data. The article concludes with the request and the need for researchers, practitioners, and education policy makers to avoid the temptation to solve a deeply "wicked" problem such as education with simple solutions.

Mathematical and statistical thinking is involved across the breadth of people's home and work life and leisure activities. This paper reports on an aspect of a project that aimed to develop preservice teacher awareness of the mathematical and statistical thinking required across the breadth of primary teachers' professional role. This thinking is conceptualised as the mathematics and statistics embedded in each of the curriculum learning areas, in data literacy, and administration and management tasks. Mentor meetings indicated pre-service teachers who were completing a one-year graduate diploma initially had a limited awareness of the extent of this thinking. Through focus group discussions across the year participating preservice teachers' commentary showed an increased awareness and appreciation of the breadth of contexts where teachers might encounter mathematics and statistics thinking beyond mathematics lessons. Given awareness is fundamental to learning and subsequent action we posit that developing this awareness during teacher education is important.

Digital technology has provided users with new connections that have reset our understanding of social architectures. As a reaction to Artificial Intelligence (AI) and Big Data, the educational field has rearranged its structure to consider human and non-human stakeholders and their actions on digital platforms. In light of this increasingly complex scenario, this proposal aims to present definitions and discussions about AI and Big Data from the academic field or published by international organizations. The study of AI and Big Data goes beyond the search for mere computational power and instead focuses upon less difficult (yet perhaps more complex) areas of the study social impacts in Education. This research suggests an analysis of education through 21st century skills and the impact of AI development in the age of platforms, undergoing three methodological considerations: research, application and evaluation. To accomplish the research, we relied upon systematic reviews, bibliographic research and quality analyses conducted within case studies to compose a position paper that sheds light on how AI and Big Data work and on what level they can be applied in the field of education. Our goal is to offer a triangular analysis under a multimodal approach to better understand the interface between education and new technological prospects, taking into consideration qualitative and quantitative procedures.

In this paper, we introduce Notice, Wonder, Feel, Act, and Reimagine (NWFAR) to promote social justice in data science (DS) education. NWFAR draws on intersectional feminist DS to scaffold critical perspectives towards systems of power and oppression and attend to students' experiences in designs for learning. NWFAR adds three practices that are typically not emphasized in learning designs for DS: feel--engaging emotions and the physical body; act--challenging, inspiring, or informing others towards change; and reimagine--envisioning how data, data methods, and data technologies could pursue different problems, solutions, and perspectives. We illustrate NWFAR through two design-based research projects from prior empirical work. Through these two examples, we demonstrate what thinking with NWFAR could look like in practice and highlight future possibilities for learning. We conclude with a discussion that focuses on the reimagining dimension, in which we highlight social-justice oriented theories

Missing data patterns are the combinations in which the variables with missing values occur. Exploring these patterns in multivariate data can be very useful but there are few specialized tools. The current paper presents a plot that includes relevant information for visualizing these patterns. The plot is also dynamic-interactive; so, selecting elements in it permits the highlighting of those that are more relevant according to certain criteria. An example, based on college data, is used for the purposes of illustrating the capabilities of the plot.

In this paper, we argue that in order to bind Teach For All's universalizing statement of problems and solutions to the specificities and the special conditions of member programs' local contexts, what is needed is a shared set of discursive practices, a way of bringing together the commonalities found in each country while separating the noise of particular politics and histories. That common set of discursive practices is shaped around the notion of data. This paper is structured as follows: First, we contextualize Teach for All by (briefly) juxtaposing the universal and specific elements of the network, including the organization's mission, target population, its recruits (and recruiting tactics), vision, and its production of a particular kind of teacher. Then, we present the two competing, yet complementary, logics of data that are at play in Teach for All--the use of data itself and the notion of data speak--along with their underlying assumptions. We conclude by questioning the logics of this set of discursive practices, and outline our skepticism regarding how data is mobilized to produce particular subjectivities and objects.

Open Data is the name given to datasets which have been generated by international organisations, governments, NGOs and academic researchers, and made freely available online and openly-licensed. These datasets can be used by educators as Open Educational Resources (OER) to support different teaching and learning activities, allowing students to gain experience working with the same raw data researchers and policy-makers generate and use. In this way, educators can facilitate students to understand how information is generated, processed, analysed and interpreted. This paper offers an initial exploration of ways in which the use of Open Data can be key in the development of transversal skills (including digital and data literacies, alongside skills for critical thinking, research, teamwork, and global citizenship), enhancing students' abilities to understand and select information sources, to work with, curate, analyse and interpret data, and to conduct and evaluate research. This paper also presents results of an exploratory survey that can guide further research into Open Data-led learning activities. Our goal is to support educators in empowering students to engage, critically and collaboratively, as 21st century global citizens.

This paper reports on the perceptions and experiences of primary school teachers of the challenges they faced and the prospects of using data from the Annual National Assessments (ANAs). While the majority stated that information from the ANAs can assist teachers to improve learning, responses on the use of the ANAs in the classroom were mixed, with most reporting that teachers did not know how to use ANA results to improve learning, and that no plans were in place at their schools for the use of ANA data. A significant proportion also indicated that they received little or no support from the school district on how to use ANA results. These findings were consistent across the school quintiles as well as the foundation and intermediate phases. Given the potential value of the ANAs, the paper highlights two initiatives aimed at enhancing the meaningful use of ANA results to improve learning and teaching in schools.

With the increasing presence of 'datafied' educational settings across Australia, critical components of teachers' educational practice and work have been quantified. Digital data collected through teachers' labour in and around the classroom links to educational practice and the commercial datafication of teachers' online persona. Often described as 'sleepwalking' towards surveillance, this paper argues that corporate marketing strategies induce teachers in a state of 'commercial and computational comatose'. It challenges the concept of 'sleepwalking' by introducing an emergent way of thinking, through the metaphor eMorpheus, to stress broader tensions concerning teachers' working conditions, rights, and employment. Drawing on in-depth interview data generated via the Apps in Australian Classrooms Project, the findings highlight how the new forms of leadership are emerging in educational settings in response through consideration of the eMorpheus metaphor.

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge's lifecycle. [ABSTRACT FROM AUTHOR]

Over the decades, it is evident that exceptional learners have been excluded from participating in international assessments such as OECD's PISA (Programme for International Student Assessment) due to their disabilities. Drawing on the interdisciplinary theories and perspectives of educational assessment, measurement, and early childhood special education, the paper discusses the potential benefits young children with special needs may gain from the International Early Learning and Child Well-being Study (IELS), as well as considering caveats and challenges accompanying the use of IELS for these young special education populations. In particular, it raises a range of questions about what and how to collect, validly interpret, and use the IELS data to enhance early learning and development of exceptional learners in participating countries. Finally, the paper discusses accommodations that promote inclusionary assessment practices and level the playing field for young children with special needs.

How data are collected and how they are analyzed is typically described in the literature, but how the data are encoded is often not described in detail. In this paper, we discuss how data typically gathered in PER are encoded and how the choice of encoding plays a role in data analysis. We describe the kinds of data that are found when using short answer, multiple choice, Likert-scale, ranking task, and free response questions in terms of nominal, ordinal, interval, and ratio data. We discuss the mathematical operations that are available for each kind of data and how this affects ways that similarity and difference between student responses can be determined, a topic we discuss in terms of measures of distances and correlation. Finally, we use several papers from the literature to discuss ways in which data have been encoded and analyzed, with examples of normalized gain, factor analysis, model analysis, cluster analysis, and the investigation of epistemological agreement. We highlight both strengths and weaknesses of the data encoding approaches used in these studies. Our goal is not a comprehensive review, but one that is illustrative and can help researchers understand their own and each other's work more deeply.

In today's online data-driven world, people constantly shed data and deposit digital footprints. When individuals access health services, governments and health providers collect and store large volumes of health information about people that can later be retrieved, linked and analysed for research purposes. This can lead to new discoveries in medicine and healthcare. In addition, when securely stored and de-identified, the privacy risks are minimal and manageable. In many jurisdictions, ethics committees routinely waive the requirement for researchers to obtain consent from data subjects before using and linking these datasets in an effort to balance respect for individuals with research efficiency. In this paper, I explore the ethical justification for using routinely collected health data for research without consent. I conclude that, not only is this morally justified but also that data subjects have a moral obligation to contribute their data to such research, which would obviate the need for ethics committees to consider consent waivers. In justifying this argument, I look to the duty of easy rescue, distributive justice and draw analogies with vaccination ethics.

The aim of this paper is to introduce a software that is appropriate for the generalizability theory for not only balanced but also unbalanced data sets. Because it is possible to have unbalanced data sets while conducting a study, the researchers have devised an easy solution, other than deleting data, to balance the design to cope with this situation. Thus, the software G String V will be introduced. First, the generalizability theory will be reviewed, followed by a description of the unbalanced synthetic data that was used to conduct the analysis using the software. Explanations are provided for installing the software, preparation of the data, and the step-by-step data analysis. Moreover, the interpretation of the data is also explained. Finally, the limitations of the software are shared.

This paper discusses some of the recurrent issues which the authors have noticed in educational research. These might be concerned with the nature of social science research, such as its representation of the reality of student learning, the role for replication studies, and the tolerance for disagreements. There are also issues related to the researching process, including the scope of prior research that should be reviewed, the purpose of triangulation, the need to be data-sensitive, the value in the complementarity of quantitative and qualitative information, relationships between theory and data, researchers' statistical literacy levels, and over-dependence on software-generated results. The fluid multi-cultural context in which education and pedagogy are situated has also meant that accessibility to knowledge is language-mediated and social reality, ever-evolving.