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This paper provides an overview of the design and motivation for creating the Social Cognition Parallax Interview Corpus (SCOPIC), an open-ended, accessible corpus that balances the need for language-specific annotation with typologically-calibrated markup. SCOPIC provides richly annotated data, focusing on functional categories relevant to social cognition, the social and psychological facts that place people and others within an interconnected social context and allow people to interact with one another. By "parallax corpus" we mean "broadly comparable formulations resulting from a comparable task", to avoid the implications of "parallel corpus" that there will be exact semantic equivalence across languages. We describe the data structure of the corpus and the language functions being annotated, and provide an example of a typological analysis using recursive partitioning, a modern statistical technique. The current paper should be seen as the introductory chapter of an open-ended special issue of LDC whose goal is to make available both the original corpus, the evolving annotated versions, and analyses coming from them, so that any investigator can examine the corpus with their own questions in mind. A range of new papers, linked to the evolving corpus, will be added to this special issue over time.

This paper considers the role of independent research in helping an education and training system "know itself", illustrating this through the experience of the Centre for Educational Sociology (CES) of which David Raffe was a member and director. It highlights the tensions in the research-policy relationship arising from the different "normative worlds" of researchers and policy makers, and their priorities at each stage of the policy cycle. It shows how such tensions have been evident in the research-policy relationship in Scotland pre- and post-devolution and points out the importance of the plural provision of research support and funding. The paper argues the need for system-wide, longitudinal data to enable a country to "know" its education and training system and analyse the impact of government policies and wider societal change. It points out that Scotland no longer collects such data and so cannot interrogate and understand its education and training system. The failure of policy makers to learn from the experience of their own country's past is noted; the paper concludes by reaffirming the need for research to challenge policy and policy-makers, especially in a system such as Scotland with a tendency to complacency.

This paper analyses how three European countries produce and use data within a specific educational policy field, that of school leaving and graduation. It compares how stakeholders in England, Finland and the Netherlands know what happens to the leavers from schools and universities. Through gathering evidence about the methodological underpinnings of the school leavers' and graduates' information systems (SLGIS) and whose data needs they aim to satisfy, this research provides insight into the discrepancies of data production. Moreover, as stakeholders from the policy and the institutional level are represented to a different extent when SLGIS are set up, the utility of the resulting data in their work is of differing degrees. The paper models the problems of how SLGIS are set up and utilised, thus suggesting some of the possible solutions. Using this particular example, the paper highlights the discrepancies of data production and utilisation in terms of the focus, the timing, the methodology and sampling as well as the processes of dissemination. To provide a wider relevance, the paper outlines key issues around the utility of research evidence.

Purpose: When school leaders advance strategic plans focused on improving educational equity through data-driven decision making, how do policies-as-practiced unfold in the daily work of science teachers? The paper aims to discuss this issue. Design/methodology/approach: This ethnographic study examines how data-centric accountability and improvement efforts surface as practices for 36 science teachers in three secondary schools. For two years, researchers were embedded in schools alongside teachers moving through daily classroom practice, meetings with colleagues and leaders, data-centric meetings, and professional development days. Findings: Bundled initiatives created consequences for science educators including missed opportunities to capitalize on student-generated ideas, to foster science sensemaking, and to pursue meaningful and equitable science learning. Problematic policy-practice intersections arose, in part, because of school leaders' framing of district and school initiatives in ways that undermined equity in science education. Practical implications: From the perspective of science education, this paper raises an alarming problem for equitable science teaching. Lessons learned from missteps seen in this study have practical implications for others attempting similar work. The paper suggests alternatives for supporting meaningful and equitable science education. Originality/value: Seeing leaders' framing of policy initiatives, their bundling of performance goals, equity and accountability efforts, and their instructional coaching activities from the point of view of teachers affords unique insight into how leadership activities mediate policies in schools.

Data papers, such as those published by Ecological Research, encourage the retrieval and archiving of valuable unpublished, undigitized ecological observational data. However, scientists remain hesitant to submit their data to such forums. In this perspective paper, we describe lessons learned from the Long‐Term Ecological Research, the Global Biodiversity Information Facility and marine biological databases and discuss how data sharing and publication are both powerful and important for ecological research. Our aim is to encourage readers to submit their unpublished, undigitized ecological observational data then the data may be archived, published and used by other researchers to advance knowledge in the field of ecology. Coupling data sharing and syntheses with the development of innovative informatics would allow ecology to enter the realm of big science and provide seeds for a new and robust agenda of future ecological studies. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper reports on a system and related experiments finalised to improve both the performance and quality of formative and summative assessments in specific data science courses. The system is developed to automatically grade assignments composed of R commands commented with short sentences written in natural language. In our opinion, the use of the system can: (1) shorten the correction times and reduce the possibility of errors; and (2) support the students while solving the exercises assigned during the course through automated feedback. To investigate these aims, an ad-hoc experiment was conducted in three courses containing the specific topic of statistical analysis of health data. Our evaluation demonstrated that automated grading has an acceptable correlation with human grading. Furthermore, the students who used the tool did not report usability issues, and those that used it for more than half of the exercises obtained (on average) higher grades in the exam. Finally, the use of the system reduced the correction time and assisted the professor in identifying correction errors.

In this article the authors respond to a comment on their article "An Analysis of the Labor Market for Uber's Driver-Partners in the United States," in the 2017 issue of the journal. Topics include the criticism based on the use of outdated data and selective reporting of findings, and the invitation language makes no mention of a connection between the survey and Uber Technologies.

Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data--characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining the assumptions, key concepts, merits, and caveats of three commonly used analytical approaches to mining massive amounts of text data: topic models, network text analysis, and sentiment analysis. Next, to ensure the veracity of using big data in education policy research, I debunk three methodological misconceptions. This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data in education policy.

Special education, a discipline that aims to provide specialized instruction to meet the unique needs of each child with a disability, has turned 40 years old in the United States. Ever since the passage of the Education for All Handicapped Children Act (P.L. 94-142) in 1975, every state has been directed to provide a free and appropriate education for all students with disabilities (Gallagher, 2000; Rothstein, 1995). The focus of this paper is to revisit the foundations of the special education movement in the United States to show how special education has progressed since 1975. The current Race to the Top movement impacts school districts across the nation, creating great concern about what this means for students with disabilities and how it affects their struggle to succeed within the public school domain. In fact, after 40 years, (P.L. 94-142) known as the Individuals with Disabilities Education Act, IDEA, is the current high stakes standards and assessment climate taking the "special" out of special education?

In this short thought-piece, I attempt to capture the type of freewheeling discussions I had with our late colleague, Mika Seppälä, a research mathematician from Helsinki. Mika, not being a psychometrician or learning scientist, was blissfully free from the design constraints that experts sometimes ingest, unwittingly. I also draw on delightful conversations with the German research mathematician, Heinz-Otto Peitgen, a polyglot whose work includes advances in medical imaging and explorations in fractal geometry for K-12 students. Together, they taught me to reconsider foundational assumptions about learning, how to describe it, and how to grow it. Accordingly, I use this set of papers as a prompt for examining assumptions that numerical precision ensures scientific insight, that linear models best capture growth in learning, and that relaxing a fixation with time (exemplified by the reification of pre- and post-testing) might open up new topologies for describing, predicting, and promoting learning in its myriad manifestations.

The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50-60%. At the college level in the US only 30% of students graduate from 2-year colleges in 3 years or less and approximately 50% graduate from 4-year colleges in 5 years or less. A basic challenge in delivering global education, therefore, is improving student success. By student success we mean improving retention, completion and graduation rates. In this paper we describe a Student Success System (S3) that provides a holistic, analytical view of student academic progress. The core of S3 is a flexible predictive modelling engine that uses machine intelligence and statistical techniques to identify at-risk students pre-emptively. S3 also provides a set of advanced data visualisations for reaching diagnostic insights and a case management tool for managing interventions. S3's open modular architecture will also allow integration and plug-ins with both open and proprietary software. Powered by learning analytics, S3 is intended as an "end-to-end solution" for identifying at-risk students, understanding why they are at risk, designing interventions to mitigate that risk and finally closing the feedback look by tracking the efficacy of the applied intervention. (Contains 1 note.) [This paper was published in the ALT-C 2012 Conference Proceedings.]

The growing popularity of data visualization due to increased amounts of data and easier-to-use software tools creates an information literacy skill gap for students. Students in an Information Technology Management graduate course were exposed to data visualization not only through their textbook reading but also through a data visualization assignment. Through a review of current literature and practice, student feedback, and instructor experience, the data visualization assignment has been updated multiple times in an effort to best serve the students' learning needs. This paper provides a brief literature review on why data visualization is growing and why students should learn about it and then outlines the data visualization assignment through each iteration. Lessons learned from student feedback on a student survey and suggestions for future assignments are also included.

In the quest to improve measured educational outcomes national governments across the The Organisation for Economic Co-operation and Development (OECD) and beyond have instituted large-scale assessment (LSA) policies in their public schools. Controversy almost universally follows the implementation of such testing, related to such topics as: a) the uncertain quality of the tests themselves as psychometrics measures; b) the uses to which the data can and should be put; c) the unintended consequences of test-preparation activities and resulting score inflation; and d) the effects of high-stakes tests on students. Debates of this nature naturally involve and impact the attitudes and opinions of teachers related to their collection and use of these data. This paper examines the impact of these attitudes using both the qualitative and quantitative data from a large-scale research study on Canadian provincial assessment. Data were collected from nation-wide teacher surveys as well as interviews with teachers, administrators and district-level staff. Results show that teacher attitudes about these assessments are strongly correlated to classroom-level instructional change. Three attitudinal factors have significant effects on teaching (to) the provincial curricula, yet none significantly affects the use of less constructive instructional strategies also known as "teaching to the test." Specifically, the belief that large-scale assessment data have more appropriate uses and the belief that these data could lead to school improvement were significant factors in facilitating change. The implications of these findings are profound in that large-scale assessment policy cannot succeed even by its own standards without more buy in from teaching professionals.

Purpose: This paper presents application solutions related to the use of Fourth Industrial Revolution technologies in industry. The aim of the paper is including in a discussion about new trends in the development of information systems supporting monitoring, control and diagnostics of production processes. The objective was realised using the example of continuous production in steelworks. Design/methodology/approach: The aim of the paper was realized in theoretical and practical part of the paper. The empirical part of the was the form of a case study – approach to mathematical model for predicting the enthalpy of foundry ladles. Findings: The conclusion is simply: each industrial branch has to use own transformation path towards Industry 4.0. Practical implications: Prepared approaches - based on analysis of trends in data processing control system in the continues production - can be used to build algorithm of predictive model in its system. Research limitations/implications: The authors are aware of the limitations resulting from the excessive generality of the model, but they ensure that they will continue research on the implementation of Industry 4.0 technology in the metallurgical (metal) industry, with particular emphasis on the continuous course of manufacturing processes. Originality/value: The value of the paper are prepared approaches to mathematical model for predicting the enthalpy of foundry ladles. [ABSTRACT FROM AUTHOR]

The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%) to address the problems caused by missingness. Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. All these 4 papers were published in the past four years. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials; some papers provided great detail about the missing data, while others made no reference to it. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore what design aspects can support data visualization literacy within science museums. Design/methodology/approach: The qualitative study thematically analyzes video data of 11 visitor groups as they engage with reading and writing of data visualization through a science museum exhibition that features real-time and uncurated data. Findings: Findings present how the design aspects of the exhibit led to identifying single data records, data patterns, mismeasurements and distribution rate. Research limitations/implications: The findings preface how to study data visualization literacy learning in short museum interactions. Practical implications Practically, the findings point toward design implications for facilitating data visualization literacy in museum exhibits. Originality/value: The originality of the study lays in the way the exhibit supports engagement with data visualization literacy with uncurated data records.

Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve Bayes methods were used and compared. The performance of machine learning was compared to human experts in terms of flagging records to be excluded from equating. The application of machine learning seemed to be successful, although the data only consisted of a couple of thousand records. Recommendations in using machine learning are provided.

Many studies have reported on the influence of teachers' conceptions of variability on different aspects of their professional knowledge for teaching statistics and their classroom practices. However, research on these kind of conceptions is still scarce, particularly in Latin American countries like Venezuela. In an effort to help fill this gap, a qualitative study was conducted that aimed to characterize the different ways in which Venezuelan in-service secondary school mathematics teachers conceptualize variability. For that purpose, a survey instrument was developed and administered to 27 teachers working at the metropolitan area of Caracas. This paper focuses on the participants' answers to two items in which interpretation of histograms was necessary. It was found that about a third of the participants exhibited a sophisticated recognition of variability (e.g., gave answers connecting both middles and extremes), whereas about half of them exhibited misconceptions of variability, such as acknowledging variability from the viewpoint of idiosyncratic ideas, or the degree of symmetry (or lack thereof) of a histogram. Moreover, it was also found that about two-thirds of the participants were unable to correctly match real-life contexts to their corresponding histograms, while about two-fifths were unable to correctly determine the accuracy or inaccuracy of descriptions of the variability in a histogram. The author discusses possible reasons for the obtained results, in order to identify relevant implications for teacher education in the area of statistics.

This paper describes a short module for introducing data science to senior school students or other data-science beginners. The design focuses on "data moves." Students use CODAP to do their work.

The data sets used in statistics education have changed over time, from mathematically "well-behaved" ones that facilitated computation, to more context-rich sources and now, with the increasing influence of data science practices, to "found" data, often from open data sites. As data sources change, it is important for educators to take a fresh look at the ways we engage students in thinking about the processes that generated the data they encounter. The use of already collected data requires particular attention because many of the decisions that went into the processes of obtaining the data are hidden. Students need to learn to ask "Who, When, How, Where, and Why?" data were collected and to wonder if the data really measure what needs to be measured. Our advocacy in this paper is to deepen the educational treatment of data production to better reflect the current and future practice of statistics and data science.

This paper addresses the entanglements of the earth's becoming in a multi site cross hemisphere study of the infinite moments of young children's world making, recorded in multimodal videos, still images, and children's productions. The postqualitative project was informed by new materialist and posthuman theorising. The researchers conducted their research at seven different early learning centres in NSW, Queensland, Victoria, and Finland. At the end of the project the researchers came together to make sense of the data across all sites and realised that their postqualitative data was BIG and that their 'little big data' was generated in the context of the increasing impact of 'big Big Data.' We discuss our approaches to posthuman and new materialist big data in order to create a conversation between the two and to highlight the alternative methodological processes we developed to make sense of large bodies of postqualitative data.

As many sciences move to be more data-intensive, some science librarians are offering more research data services and perform research data management roles. Job analyses provide insight and context to the tasks employees actually do versus what their job descriptions depict or employers assume. Two separate job analyses studies investigated the roles and responsibilities of data services librarians and research integrity officers among the top 10 private and top 10 public higher education institutions. The focus of these interviews was research data management support roles. Comparing these two groups' responses indicates that the role-based responsibilities for research data services are not always clear within institutions and are predominantly placed on individual researchers or research teams, but science librarians may provide some solutions to address this gap. This paper presents a model for the potential roles of science librarians in research data management.

The increasing datafication of teachers' work and schooling practices as evidenced through various metrics of student testing and school improvement measures have continued to grow unabated across many OECD Countries. Such practices have been fuelled by global competition for league tables such as the Programme for International Student Assessment (PISA) with 'big data' having a major impact on how teachers are expected to gather, analyse and report data. For schools in Australia, the turn of the millennium has heralded the 'high stakes' data trend and the roll-out of standardised testing. This paper reports on an Australian study's findings, against such a backdrop, to explore the impact and challenges of an overly data-rich environment for educators, teachers and system leaders and what might be the enabling conditions to move towards a more research-rich teaching profession. The findings suggest that perhaps standards and data are not the enemy for teachers, rather it is standardisation and the datafication of students that creates an amplification of the effects of institutional rankings and league tables. Enabling conditions are offered to adopt a more comprehensive and inclusive view of what counts as research and who conducts research, key to enabling a mature teaching profession.

The paper considers digital tutoring platforms as commercial products in the shadow education sector. In particular, it focuses on relatively new platforms which purportedly act as tutors themselves: web-based learning software providing "intelligent tutoring systems.' The question is not how effective and beneficial these platforms are for learning success. Following science and technology studies, the focus is on product design and its role as an actor in a network of relations. Autoethnographic interaction with a paradigmatic platform is used to show a variety of inscriptions and translations. The classifications, standardizations and quantitative calculations that emerge here are analyzed as practices for generating data, which in turn stabilize the platform as a specific ontological configuration. The data give the impression of being personal. On closer examination, however, they are the product of manifold relations between different actors in the network.

This paper analyses the reliability and accuracy of the relationships between migration and employment status when estimated using a linked data set. The analysis will be carried out using a new source, the "Labour and Geographical Mobility Statistics," which is provided by the Spanish Statistical Office. This statistic is constructed by an exact matching procedure, linking the "Labour Force Survey" with the Official "Population Register." The findings reveal that in order to accurately study geographic labour mobility, timing and geographical coherence between the two data sets must exist. If this is not the case, causality relations between the labour market and geographical mobility cannot be properly analysed. Although our analysis refers to the microdata quality and internal coherence of this new Spanish statistic, our conclusion can be extended to any linked data set for studying geographic labour mobility, including the population censuses mainly based on administrative data.

The on-farm research network concept enables a group of farmers to test new agricultural management practices under local conditions with support from local researchers or agronomists. Different on-farm trials based on the same experimental design are conducted over several years and sites to test the effectiveness of different innovative management practices aimed at increasing crop productivity and profitability. As a larger amount of historical trial data are being accumulated, data of all the trials require analyses and summarization. Summaries of on-farm trials are usually presented to farmers as individual field reports, which are not optimal for the dissemination of results and decision making. A more practical communication method is needed to enhance result communication and decision making. R Shiny is a new rapidly developing technology for turning R data analyses into interactive web applications. For the first time for on-farm research networks, we developed and launched an interactive web tool called ISOFAST using R Shiny. ISOFAST simultaneously reports all trial results about the same management practice to simplify interpretation of multi-site and multi-year summaries. We used a random-effects model to synthetize treatment differences at both the individual trial and network levels and generate new knowledge for farmers and agronomists. The friendly interface enables users to explore trial summaries, access model outputs, and perform economic analysis at their fingertips. This paper describes a case-study to illustrate how to use the tool and make agronomic management decisions based on the on-farm trial data. We also provided technical details and guidance for developing a similar interactive visualization tool customized for on-farm research network. ISOFAST is currently available at https://analytics.iasoybeans.com/cool-apps/ISOFAST/.

Given that our current educational context has required many educators to utilize online instruction in some capacity over the past year, the purpose of this paper is to describe how literacy specialists and teachers can use digital tools to remotely engage in data-based decision-making within a collaborative professional learning community. Digital tools enable teachers to engage in meaningful asynchronous and synchronous collaborative activities online. Drawing upon our experience as literacy teacher educators, we describe our process of using digital tools to teach literacy specialists and educators how to take part in and facilitate professional learning communities (PLC) engaging in the cycle of data based decision making.

The Euler problem on the stability of a thin elastic rod under compressing forces is widely known. In a number of papers this problem is generalized to finding the shape of such a rod under the simultaneous action of a compressing force and a torsional moment. The shape is determined by solving a complex nonlinear boundary value problem by numerical methods. In this paper, an approach providing a full analytical solution of the problem for a broad range of external conditions is discussed. [ABSTRACT FROM AUTHOR]

This paper establishes that system-generated data profiles are influencing the work of principals in three Queensland state schools. Drawing upon Foucault's notions of governance, as well as research emphasising performative cultures and the importance placed upon numbers and data in education, this paper uses the tale of the Emperor's map as a metaphor to explore the way principals' work is being influenced by specific sets of data compiled by the department. These data profiles are representative of external accountabilities and high stakes testing regimes, as seen in systems that have adopted neoliberal policies which attempt to quantify the work being undertaken in schools. The paper demonstrates that principals are being constructed in part by discourses from a system that emphasises these system-generated performance data as a driver for school improvement.

Purpose: This paper argues that in a well-organised school with strong leadership and vision coupled with a concerted effort to improve the teaching performance of each teacher, student achievement can be enhanced. The purpose of this paper is to demonstrate that while macro-effect sizes such as "whole of school" metrics are useful for school leaders in their professional development roles, there are important micro-conditions that can be uncovered in a more detailed analysis of student achievement data. Design/methodology/approach: Evidence of student achievement in a variety of standardised and non-standardised assessment tasks was subjected to examination in a "post hoc," case study design. The assessment tasks were the South Australian Spelling Test Waddington Reading Test, a school-wide diagnostic writing task, teacher running records and National Assessment Program for Literacy and Numeracy. Performance in selected classrooms was compared on these tests utilising a variety of parametric quantitative statistics. Findings: School-based reform initiatives require external criteria on which to base decision making. Without such criteria based on data and the capacity to interpret it, interactions in the school culture have unanticipated consequences that have the potential to neutralise school improvement strategies. Further, findings suggest that fewer but sharper and quicker data collection tools are more valuable in such teacher decision making, but these require expertise to produce and interpret them. Research limitations/implications: This paper provides insights from one school, but the reported data are embedded in a sustained five year school reform programme. Practical implications: This paper documents a whole school organisational reform model devised by a school head and leadership team to improve student academic performance. The paper offers a process for developing a data-based school reform strategy for professional development to enhance both student achievement and school outcomes. Social implications: The paper outlines a model for school reform that is focused on all students increasing their academic outcomes. By emphasising collaborative teacher work based on research-justified teaching approaches, the model shows that social inequalities can be reversed. Originality/value: The paper outlines a whole of school reform model focused through a combination of distributed leadership, data-driven decision making, within a context of a coaching, mentoring and feedback regime for teachers. Together this model is an application of theoretical ideas to school reform.

Advanced by powerful venture philanthropies, educational technology companies, and the US Department of Education, a growing movement to apply "big data" through "learning analytics" to create "personalized learning" is currently underway in K-12 education in the United States. While scholars have offered various critiques of the corporate school reform agenda, the role of personalized learning technology in the corporatization of public education has not been extensively examined. Through a content analysis of United States Department of Education reports, personalized learning advocacy white papers, and published research monographs, this paper details how big data and adaptive learning systems are functioning to redefine educational policy, teaching, and learning in ways that transfer educational decisions from public school classrooms and teachers to private corporate spaces and authorities. The analysis shows that all three types of documents position education within a reductive set of economic rationalities that emphasize human capital development, the expansion of data-driven instruction and decision-making, and a narrow conception of learning as the acquisition of discrete skills and behavior modification detached from broader social contexts and culturally relevant forms of knowledge and inquiry. The paper concludes by drawing out the contradictions inherent to personalized learning technology and corporatization of schooling. It argues that these contradictions necessitate a broad rethinking of the value and purpose of new educational technology.

This paper focuses on the importance of equality, diversity, and inclusion (EDI) in the aquaculture industry, with a particular emphasis on the sector in Scotland. Aquaculture is a particularly important industry when it comes to EDI, given its potential to address Sustainable Development Goals on gender equality and diversity. The paper highlights the increasing attention being paid to EDI in many areas and the significant benefits to businesses that adopt EDI policies, including improved reputation, increased innovation, and greater profitability. This paper draws on a survey of EDI in Scottish aquaculture, a workshop and interviews with industry experts to suggest concrete actions that could improve EDI in the sector. A key priority is the collection and publication of workforce data in Scottish aquaculture as well as industry champions who raise awarenes and promote EDI, and supporting cross-sector organizations who provide EDI training. We conclude by calling for more research to support the development of EDI in Scottish aquaculture, which will contribute to future resilience and fairness as well as a dynamic, relevant, and accessible industry. [ABSTRACT FROM AUTHOR]

Any tool, apparatus, machine, appliance, implant, reagent for in vitro usage, software, material, or other like or related item that the producer intends to be used, either alone or in combination, for medical purposes is considered a medical device. A thorough review was conducted for the purpose of revealing the medical device data and medical device industry in the current paper. Some of the data incorporated in this paper has been taken from Government Websites, Press Releases, Media Reports, Deloitte Report, Union Budget 2022-23 and dully citation was mentioned. [ABSTRACT FROM AUTHOR]

Geospatial data describe objects and things with relation to geographic space often with location coordinate in a spatial referenced system. Rural roads are geospatial entities which can be captured and stored using geographic information system techniques. Therefore, a geographic information system is an essential tool to be placed on comprehending the information of spatial and non-spatial data over space and time. Data required for this paper include high resolution satellite imageries (QuickBird, SPOTS, IKONOS), Landsat (EOI Hyperion, DEM); local, state, and international boundaries; all Edges of transport routes connecting all settlements in the state, settlement data, stream network data, and terrain data. Roads associated attributes include location of potholes, bumps, drainages, drainage direction, and last date of road repaired, highest point, lowest point, mean elevation, maximum slope, average slope, road tears and wears which is expressed as roads condition. Road geometry data involve length of each road edge, width, and referential measurement. Data on nature of surfacing such as tar, asphalt, concrete, and laterite. Other data on roads are name, type, classification, and Geotagged pictures and video of all roads in Adamawa state. The field survey involves trailing the whole length of the roads from a referenced baseline at a vehicle speed using GPS Waypoint Navigators, handheld GPSs, and RoadLab application in iPad. These devices were used in collecting data on roads roughness index expressed as good, bad, excellent; visual assessment of road conditions and drainages were carried out during the field survey. When navigating the roads records taking of roads data, geotagged pictures, videos, and coordinates of event areas were captured. However, the use of RoadLab in assessing road conditions was only limited to Trunk a, b, and c roads across the state since they are the most tarred roads in regional road classification. Therefore, rigorous physical/visual surveys and assessment on all other rural roads were conducted. The result of the research indicate that trunk b, c and feeder roads are in bad shape and geospatial database of all road network in Adamawa State was developed. [ABSTRACT FROM AUTHOR]

Building on work which has shown the role of digital technologies in reframing environmental relations, this paper explores ethnographically how environmental data is reconfiguring the concept of place. The paper takes as its focus an action-research project within a UK based, citizen-oriented initiative called Newtown Energy Futures, in which we sought to enfold climate and energy data into a social-justice informed attempt at climate action. By exploring how the project used data as an invitation for citizens to engage with and participate in local infrastructural and environmental dynamics, the paper sheds light on how environmental data came to participate in the making of place and in doing so raised questions about how to rebuild the socio-material relations through which 'a sense of place' might be reproduced. As climate and energy data increasingly demand that places become enrolled into environmental projects our findings suggest that data enables place to emerge as a 'socio-technical potentiality' an observation that has implications both for both engagement with, and the study of data and place. In practical terms, we suggest that this refiguration of place has the effect of creating hopeful trajectories for change, whilst also posing difficult questions about the limits of participation in a data-infused form of place-based politics. [ABSTRACT FROM AUTHOR]

Relating electoral laws to electoral integrity has long been a focus of academic research. Using the strategic-relational approach, as outlined in this special issue, it is possible to better understand how electoral laws shape the voter experience and electoral outcomes. This paper contributes to this understanding by looking at Voter Identification (ID) laws. With no consolidated dataset of voter ID laws existing outside the U.S.A. it is difficult to answer research questions put forward in this special issue, especially the second. This paper begins to address this shortfall by presenting the Comparative Voter ID Law (CVIL) index. Which has collected data on 246 individual electoral jurisdictions. Data presented show how voter ID laws are distributed globally, regionally, by regime type and level of democracy. The second part of the analysis goes on to describe voter ID laws by whether a jurisdiction has compulsory ID laws, how many different types of ID are accepted and the minimum number of ID documents that must be shown. Thirdly, other variables within the dataset are described. The CVIL will provide opportunities to understand how Voter ID laws are part of institutional design, are used by actors, shape the voter experience and electoral out-comes. [ABSTRACT FROM AUTHOR]

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research. [ABSTRACT FROM AUTHOR]

Educational outcomes across multiple countries show large gaps between children's grade level curricular expectations and the actual learning levels of children. This paper discusses findings from an early grades literacy study and attempts to explore factors that potentially improve the English language learning results of Grades 3 and 4 learners in Bonsaaso, Ghana. The study was conducted in five schools, and the teachers were randomly selected into treatment and control groups. This is a quasi-randomized study. The objective of the study is to understand the components and processes that improve learning. The treatment included teacher training, constant feedback using real-time data and supervision, discussions of student-learning outcomes, materials development, corrective feedback to students and lesson plans. Overall, the percentage of the learners able to read as a result of this instructional treatment almost doubled compared to the control group; 34% of the students in the treatment group were classified as able to read at the end of the study compared to 18% in the control group. However, majority of the students were unable to read simple sentences. The classroom observations describe some of the classroom management and teaching techniques that could enhance learner performance.

There is a debate on students' low science achievement in the United States, particularly among low income, African American students, and Latino students. An important part of the education community's response to low achievement generally and in science specifically has been the implementation of high stakes accountability policies. Because of accountability's emphasis on educator data use, much research has examined different facets of it throughout educational organizations, but research has not focused on the extent to which data use might be content-specific. The purpose of this paper, then, was to investigate the data use practices of science teachers. Drawing from a broader study of science teachers in grades 5-8 across six school districts, this study reports results from teacher surveys and interviews. The findings indicate that while there were examples of science-specific data use, most of the science teachers used data in ways consistent with previous content-agnostic research. Implications for future research, policy, and practice are discussed.

Introduction: In the big data age, we have to deal with a tremendous amount of information, which can be collected from various types of sources. For information search systems such as Web search engines or online digital libraries, the collection of documents becomes larger and larger. For some queries, an information search system needs to retrieve a large number of documents. On the other hand, very often people are only willing to visit no more than a few top-ranked documents. Therefore, how to develop an information search system with desirable efficiency and effectiveness is a research problem. Method: In this paper, we focus on the data fusion approach to information search, in which each component search model contributes a result and all the results are combined by a fusion algorithm. Through empirical study, we are able to find a feasible combination method that balances effectiveness and efficiency in the context of data fusion. Analysis: It is a multi-optimisation problem that aims to balance effectiveness and efficiency. To support this, we need to understand how these two factors affect each other and to what extent. Results. Using some groups of historical runs from TREC to carry out the experiment, we find that using much less information (e.g., less than 10% of the documents in the experiment), good efficiency is achievable with marginal loss on effectiveness. Conclusions: We consider that the findings from our experiment are informative and this can be used as a guideline for providing more efficient search service in the big data environment.