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

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.

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).

Longstanding inequities exist in community colleges across the United States. To address these inequities, California Community Colleges educators have engaged in a variety of practices including the writing of equity plans and participation in equity data inquiry. However, there is an urgent need for greater focus on racial equity and for more faculty involvement in equity work at the classroom level. This paper presents a teacher case study exploring Classroom Equity Data Inquiry (CEDI), a tool for faculty professional learning focused on equitable student outcomes. In CEDI, professors examine their disaggregated classroom data, reflect on their class equity gaps, and pursue relevant professional development. They implement targeted interventions and then assess those interventions. This paper describes the author's sustained CEDI utilizing six years of equity data in her English as a Second Language classes at a small northern California community college. First, it provides a definition and rationale for CEDI. Second, it details the author's CEDI process and challenges. Third, it shares the author's changes in thinking and practice including high impact interventions the author implemented to reduce equity gaps for men of color in her classes. Fourth, the article describes positive qualitative student data and increased success and retention rates for Hispanic and multi-race males following the interventions. The article concludes that CEDI requires training, support, and time, but that the approach merits further research. More research is needed on CEDI methods and their possible impact on racial equity in the classroom.

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

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

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

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

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

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.

While the co-production of knowledge through community-engaged research is intended to be a reciprocally beneficial process, academic institutions have often devalued community expertise by treating community organizations as subjects rather than co-creators of knowledge. Drawing from Black Feminist Epistemology, this ethnographic study examines how one community-based organization, Los Angeles Community Action Network (LA CAN), partners with academic researchers, including their discourse around partnerships and how they challenged power dynamics between community and their university partners. This paper discusses key themes from their partnerships, including centering community members' expertise through their lived experience and forming long-term mutual relationships rooted in abolition and the Black Radical Tradition. Drawing on an analysis of LA CAN's organizing and research processes with academic partners, we discuss how the centering of community expertise and forming relationships with academics aligned on these values can help to challenge the traditional power dynamics in community-university partnerships, resulting in different ways of knowing or what LA CAN referred to as "the real data set."

This paper investigates changes in the promissory visions articulated in education reforms around the world. We use structural topic modeling to inductively analyze the content of 9,268 reforms from 215 countries and territories during the period 1970-2018 using the World Education Reform Database. Our findings reveal a decline in traditional management-focused reforms and a rise in reforms related to data and information. We also find an expanding commitment to educational access and inclusion, but reforms framed explicitly in 'rights' language diminish. We argue that the rise of data-centric reforms and the retreat from rights-based approaches may both reflect and contribute to a broader erosion of the liberal world order.

The pressure towards digital education is felt everywhere including in places with extreme digital divides. Resource-constrained educational environments are particularly threatened by datification manifest in the dominant business models of surveillance capitalism as there is less room in such contexts to refuse the 'free' offerings from big tech companies; it is these very contexts which are most vulnerable. Yet educators within such environments are not mere pawns of circumstance. While the realities of their structural constraints may be invisible or obfuscated, educators are driven by their own 'concerns', which in this case pertain to the needs of diverse students in very challenging circumstances as well as to their personal aversion to being monitored. This paper reports on findings from focus groups in a mixture of institutionsin South African education. Archer's theoretical framework provides a lens to show how, despite very little choice, educators critically reflect on their circumstances expressing discomfort and unease.

Text and data mining activities -- that is, the automated processing of digital materials to uncover new knowledge -- have become more frequent in all areas of scientific research. Because they require a massive use of copyrighted work, there are evident conflicts with copyright legislation. Countries at the forefront of research and development have begun to address this issue. This paper presents the basic aspects of legislation applicable to text and data mining activities. It offers a detailed comparative analysis of the norms of the main jurisdictions that have regulated them to date, highlighting in each case the positive and negative aspects. An adequate knowledge of these laws is not only important for researchers but also important for the academic librarians who provide advice and support in these matters.

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

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

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

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

Purpose: Upon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making. Design/methodology/approach: This paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment. Findings: Four main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions. Originality/value: Three notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

The purpose of this paper is to explore 10-year trends in the use of Assistive Technology (AT). Using 2006 and 2015 Behavioral Risk Factor Surveillance System (BRFSS) data, univariate analysis were performed, and the rate of change was calculated. In both 2006 and 2015 data, a higher per cent of Blacks were using AT compared to Whites and Hispanics (p < 0.001); however, the rate of increase indicate that Blacks had the lowest per cent change (23.9%) compared to Whites (26.8%) and Hispanics (27.3%). Results indicated that AT use is statistically more prevalent in women, individuals who are not married (e.g. divorce, widowed, separated), the veteran population, lower educational attainment levels (e.g. some high school or less), lower income levels, the ageing population, unemployed (e.g. out of work, retired, unable to work) and individuals with limited functioning because of a disability. Similarly, individuals who have healthcare coverage, and individuals with concerns with medical costs used AT more. Even though most factors yielded higher utilisation rates when comparing 2006 to 2015 data, the rate of change had varying 'speeds' of improvement.

This work reports on the implementation of a self-contained data-literacy exercise designed for use in undergraduate classes to help students practice data literacy skills such as interpreting and evaluating evidence and assessing arguments based on data. The exercises use already developed data-visualizations to test and develop students' ability to evaluate arguments based on data presented visually. Moreover, the exercises are designed to teach students positive lessons from evaluating flawed examples of data usage. We show that repeated use of these exercises has the potential to help students develop tools they can use across multiple contexts when evaluating data. Student feedback and panel survey data show that students like to learn using these tools and they report increased comfort levels in working with data. The paper argues that instructional tools like these data visualization exercises are a quick and effective tool for teaching data literacy skills and that they have significant utility in non-research-methods courses.

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.

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.

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.)

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

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

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.

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

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

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

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

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.

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.

Science includes the fundamental attributes of durability and uncertainty; hence, we teach about the "tentative yet durable" nature of science. Public discourse can be different, where one hears both confidence about "settled science" and doubts about "just theories." The latter observation gives rise to the possibility that emphasis on learning the tentative nature of science offers some people the actionable option of declining to accept canonical science. Our paper reports the findings from initial and replication exploratory studies involving about 500 preservice, elementary/middle school teacher education students at a large Midwestern public university. Using a survey method that included opportunities for student comments, the study tested hypotheses about confidence in the veracity, durability, tentativeness, and trustworthiness of science. We found that most students embrace noncontroversial science as correct, and that almost all embraced the tentative nature of science regardless of what they thought about controversial topics. However, when asked about the trustworthiness of science, many students were not willing to say that they trust scientific knowledge. Even students strongly supportive of science, including controversial science, responded similarly. And why did they say that science is not trustworthy? The explanation echoed by many students was that scientific knowledge is tentative. Our paper concludes with implications for instruction and research. Our findings suggest that it would be prudent for science educators to increase instructional focus on the relationship between data and evidence that leads to the durability of scientific knowledge. Future research needs to thoroughly investigate the public interpretation of what we teach about the nature and characteristics of science, and for the implications it might have on how scientific knowledge is or is not incorporated in the development and implementation of public policy.

Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community's work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning -- to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner's context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn't been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED -- natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges.

Despite increasing pressure for policy and practice to adopt a more evidence-based approach, transferring evidence into use remains a stubborn challenge. This is largely due to a number of researcher-derived and user-derived barriers at play within institutions, organisations and systems that constrain active engagement with evidence. This paper contributes to current debates on evidence-use by suggesting that embedded evaluation approaches might overcome such barriers, through the creation of social capital that can be drawn upon by embedded evaluators to: (i) build trust, confidence and understanding around evaluation and evidence, on the part of local practitioners and policy makers; (ii) develop a co-productive evidence infrastructure that might draw together diverse stakeholders, as well as encourage user-engagement with varied forms of evaluation data (both numerical data and rich narrative accounts), to capture the richness of the unfolding story of complex educational initiatives. To illustrate this, this paper presents six reflective vignettes that ponder the barriers, and potential responses sought, within a two-year exploratory case study that aimed to actively engage local education-related stakeholders, leading a borough-wide place-based initiative, with evidence. Data reported within these vignettes were collected through a series of semi-structured interviews, supplemented by an embedded evaluator's field notes and analysed thematically. Although more research is needed, this paper concludes that the embedded evaluation model might have potential to respond to the diverse challenges associated with evidence-use, by positioning the evaluator in a relationally elastic way that might enable them to embed evidence-into-use pathways within policy and practice both actively and iteratively.

Whilst learning analytics is still nascent in most African higher education institutions, many African higher education institutions use learning platforms and analytic services from providers "outside" of the African continent. A critical consideration of the protection of data privacy on the African continent and its implications for learning analytics in African higher education is therefore needed. In this paper, we map the current state of legal and regulatory environments and frameworks on privacy to establish their implications for learning analytics. This scoping review of privacy regulations in 32 African countries, complemented by 15 scholarly papers, revealed that there are numerous national and regional legislation and regulatory frameworks, providing clear pointers pertaining to (student) data privacy to governments, higher education institutions and researchers. As such, the findings of this research have implications for African higher education to ensure not only legal compliance but also to oversee and safeguard student data privacy as part of their fiduciary duty. This research provides crucial insights regarding the importance of context for thinking about the expansion and institutional adoption of learning analytics.

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).

Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using "hints" and "Wikipedia-based explanations." Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in Korbit, a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

The application of learning analytics (LA) to research and practice in higher education is expanding. Researchers and practitioners are using LA to provide an evidentiary basis across higher education to investigate student learning, to drive institutional quality improvement strategies, to determine at-risk behaviours and develop intervention strategies, to measure attrition more effectively and to improve curriculum design and evaluation in both on-campus and e-learning settings. This paper is a case study report of the novel application of LA to programme curriculum review from a major cross-institutional project in Hong Kong. The paper describes the rationale for the project, the conceptual model that led the approach and the development of a software tool that allowed the automation of statistical analyses specifically relevant to programme review. In addition, the paper addresses a major challenge that the project faced in relation to data governance. The paper concludes by proposing the potential benefits of LA for programme curriculum review.

Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example, background information on eye-tracking and possible applications of eye-tracking in statistics education is provided. Teachers indicated that our eye-tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students.

Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric distortions,' which can substantially impact models' predictions. Even if distortions are well known in psychometrics, their existence and nature is obscure to many researchers outside this field. In this paper, we introduce distortions to the ABM community. Initially, we show where distortions come from and how to observe them in real-world data. We then show how they can strongly impact predictions, qualitative comparison with data and the problem they pose for validation of models. We conclude our analysis by discussing how researchers may mitigate this problem and highlight possible future modelling trends that will address this problem.

A linked data approach provides new opportunities for annotating, interlinking, sharing and enriching massive open online educational resources. However, it can be difficult for non-expert users to build and utilize the educational linked data in educational settings. Thus, flexible and user-friendly ways to represent, interlink, visualize and utilize the educational linked data become increasingly important. This paper proposes a linked data-based knowledge navigation system (LDKNS) for improving the teaching effectiveness. In the system, learning resource ontology was implemented to represent learning contents, and linked data visualization technologies were used in a formal curriculum structure. It presents a use case of building the educational linked data and creating interactive knowledge visualizations to support teachers and students in art design education. A contrast experiment was conducted to evaluate the effectiveness of the proposed system. The experiment involved 115 second year undergraduate students divided into experimental and control groups. It was found that there was a significant increase in the motivation of students who used the LDKNS. It was also found that students' achievement in the experimental group performs better. Furthermore, the results of a survey on cognitive load revealed that using the system can decrease their cognitive load. Thus, we believe that art design education supplemented with the LDKNS yields a significant learning advantage for students by improving learning performance.

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.

This paper describes the pedagogical experiences and lessons learned from teaching a service-learning graduate course in data governance at a medium-sized university in the Northeast. Just-in-time knowledge delivery and hands-on experiences in the form of service-learning projects played a critical role in knowledge acquisition and retention for students and a deeper understanding of the "messiness" and challenges of crafting and implementing data governance in a real-world environment. The rewards and struggles of students and the instructor provide insights for teaching abstract concepts in interdisciplinary fields in a hands-on environment. Service-learning courses in analytics are evolving. The lessons learned from this experience can be extended to other graduate analytics courses.