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Post-secondary data literacy education is critical to students' academic and career success. However, the literature has not adequately addressed the conceptualization and assessment of data literacy for post-secondary students. In this study, we introduced a novel digital performance-based assessment for teaching and evaluating post-secondary students' data literacy skills. The purpose of this study is to validate the assessment and identify problematic items for later modifications using the argument-based approach to validation. We analyzed students' item responses and eye movements using psychometric and eye-tracking analyses to collect two types of validity evidence: internal structure and response processes. Descriptive and psychometric results showed that the nine example items measuring basic data analysis were of acceptable psychometric quality. The eye-tracking analysis of two representative items indicated that most students first attended to and processed expected item areas when the items were available. In addition, the critical item regions for task success were associated with students' highest cognitive effort. These results rejected our alternative score interpretation that the developed assessment questions evaluate students' abilities that are weakly connected to the skill of basic data analysis. Possible explanations of the findings and theoretical and pedagogical implications of our study were discussed. [ABSTRACT FROM AUTHOR]

In response to the ever-increasing spread of online disinformation and misinformation, several human–computer interaction tools to enhance data literacy have been developed. Among them, many employ elements of gamification to increase user engagement and reach out to a broader audience. However, there are no systematic criteria to analyze their relevance and impact for building fake news resilience, partly due to the lack of a common understanding of data literacy. In this paper we put forward an operationalizable definition of data literacy as a form of multidimensional critical thinking. We then survey 22 existing tools and classify them according to a framework of 10 criteria pointing to their gameful design and educational features. Through a comparative/contrastive analysis informed by a focus group, we provide a principled set of guidelines to develop more efficient human–computer interaction tools to teach how to critically think in the current media ecosystem. [ABSTRACT FROM AUTHOR]

The current study presents a systematic review of teachers' data literacy, arising from a synthesis of 83 empirical studies published between 1990 to 2021. Our review identified 95 distinct indicators across five dimensions: (a) knowledge about data, (b) skills in using data, (c) dispositions towards data use, (d) data application for various purposes, and (e) data-related behaviors. Our findings indicate that teachers' data literacy goes beyond addressing the needs of supporting student learning and includes elements such as teacher reflection, collaboration, communication, and participation in professional development. Considering these findings, future policies should acknowledge the significance of teacher dispositions and behaviors in relation to data, recognizing that they are as important as knowledge and skills acquisition. Additionally, prioritizing the provision of system-level support to foster teacher collaboration within in-school professional development programs may prove useful in enhancing teachers' data literacy. [ABSTRACT FROM AUTHOR]

Analysing how news media portray A.I. reveals what interpretative frameworks around the technology circulate in public discourses. This allows for critical reflections on the making of meaning in prevalent narratives about A.I. and its impact. While research on the public perception of datafication and automation is growing, only a few studies investigate news framing practices. The present study connects to this nascent research area by charting A.I. news frames in four internationally renowned media outlets: The New York Times, The Guardian, Wired, and Gizmodo. The main goals are to identify dominant emphasis frames in AI news reporting over the past decade, to explore whether certain A.I. frames are associated with specific data risks (surveillance, data bias, cyber-war/cyber-crime, and information disorder), and what journalists and experts contribute to the media discourse. An automated content analysis serves for inductive frame detection (N = 3098), identification of risk references (dictionary-based), and network analysis of news writers. The results show how A.I.'s ubiquity emerged rapidly in the mid-2010s, and that the news discourse became more critical over time. It is further argued that A.I. news reporting is an important factor in building critical data literacy among lay audiences. [ABSTRACT FROM AUTHOR]

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Statistical and data literacy have emerged as key competencies in the 21st-century. Science, technology, engineering, and mathematics (STEM) curricula have been adapted in various countries to place increased emphasis on proficient data use and statistical comprehension. Consequently, there is a need for an adequate education and professional training of STEM teachers. This systematic review analyzes and synthesizes research on teachers' statistical and data literacy in K-12 STEM education. A total of 42 articles were analyzed, with a specific focus on teacher variables, teacher education, and their impact on classroom practice. The findings emphasize the recent emergence of research in this domain, particularly focusing on statistical literacy, with mathematics being the dominant STEM domain explored. Most studies examined cognitive variables of both pre-service and in-service teachers, revealing relevant gaps in knowledge and skills related to statistical and data literacy. Limited attention was given to teachers' affective variables. Research on the complex relationships between teacher variables, their teaching practices, and their students' learning is scarce, necessitating further investigation. Several pedagogical approaches to prepare STEM teachers for promoting statistical and data literacy in their classrooms were identified. Positive impacts on teachers' cognitive and affective variables were observed, but longitudinal effects and transfer to classroom practice require further inquiry. As curricula worldwide increasingly emphasize statistical and data literacy, this review underscores the necessity of enhancing teachers' competencies, advocates for tailored pedagogical approaches, and emphasizes the need for further research to shape effective instruction in K-12 STEM education. [ABSTRACT FROM AUTHOR]

Since schools increasingly use digital platforms that provide educational data in digital formats, teacher data use, and data literacy have become a focus of educational research. One main challenge is whether teachers use digital data for pedagogical purposes, such as informing their teaching. We conducted a survey study with N = 1059 teachers in upper secondary schools in Switzerland to investigate teacher digital data use and related factors such as the available technologies in schools. Descriptive analysis of the survey responses indicated that although more than half of Swiss upper-secondary teachers agreed with having data technologies at their disposal, only one-third showed a clear tendency to use these technologies, and only one-quarter felt positively confident in improving teaching in this way. An in-depth multilevel modeling showed that teachers' use of digital data could be predicted by differences between schools, teachers' positive beliefs towards digital technologies (will), self-assessed data literacy (skill), and access to data technologies (tool) as well as by general factors such as frequency of using digital devices in lessons by students. Teacher characteristics, such as age and teaching experience, were minor predictors. These results show that the provision of data technologies needs to be supplemented with efforts to strengthen teacher data literacy and use in schools. [ABSTRACT FROM AUTHOR]

Wikidata is a free, multilingual, open knowledge-base that stores structured, linked data. It has grown rapidly and as of December 2022 contains over 100 million items and millions of statements, making it the largest semantic knowledge-base in existence. Changing the interaction between people and knowledge, Wikidata offers various learning opportunities, leading to new applications in sciences, technology and cultures. These learning opportunities stem in part from the ability to query this data and ask questions that were difficult to answer in the past. They also stem from the ability to visualize query results, for example on a timeline or a map, which, in turn, helps users make sense of the data and draw additional insights from it. Research on the semantic web as learning platform and on Wikidata in the context of education is almost non-existent, and we are just beginning to understand how to utilize it for educational purposes. This research investigates the Semantic Web as a learning platform, focusing on Wikidata as a prime example. To that end, a methodology of multiple case studies was adopted, demonstrating Wikidata uses by early adopters. Seven semi-structured, in-depth interviews were conducted, out of which 10 distinct projects were extracted. A thematic analysis approach was deployed, revealing eight main uses, as well as benefits and challenges to engaging with the platform. The results shed light on Wikidata's potential as a lifelong learning process, enabling opportunities for improved Data Literacy and a worldwide social impact. [ABSTRACT FROM AUTHOR]

This paper describes a randomized and controlled efficacy study conducted in high school biology classrooms across the USA. In this study, teachers implemented the use of Data Nuggets, activities designed to bring real research and data into the classroom. These materials can be embedded within the existing instructional modality of any given curriculum, thus infusing these curricula with science stories and associated datasets. Our design had teachers incorporate Data Nuggets into one of their class sections, while teaching a second class section in a business-as-usual manner. Although students in both conditions improved similarly in quantitative reasoning over the course of the study semester, we saw several key differences for students taught using the intervention as compared to those taught using only standard instruction. Students in classrooms that utilized Data Nuggets spent more time engaged in the practices of science and had greater improvement in their ability to construct scientific explanations. In addition, students using the intervention activities showed increases in both their self-efficacy in data-related tasks and their interest in STEM careers. Finally, the effects of teacher implementation on student outcomes when using Data Nuggets were assessed. [ABSTRACT FROM AUTHOR]

With the digital transformation of education, data and digital technologies are regarded as the driving forces for teaching innovation. Teachers' data literacy and digital teaching competence are becoming increasingly important for empowering students' digital capacity, ethically technology usage, and collaboration or communication skills in the classroom. Therefore, whether teachers' data literacy and digital teaching competence can empower students in the classroom needs to be explored. This study aims to reveal the relationship between teacher's information communication technology (ICT) attitude, ICT skills, data literacy, digital teaching competence and empowering students. The data were collected from an online self-assessment scale which included a total of 629 K-9 teachers who participated in this study. Using SPSS and AMOS, a model was built by using Structural Equation Models to explain and predict the relationships. The results indicated that: (a) ICT attitude had no significant impact on digital teaching competence, and ICT skills significantly predicted digital teaching competence, but neither ICT attitude nor skills had a significant direct impact on empowering students; (b) data literacy significantly predicted digital teaching competence and had a significant direct impact on empowering students; (c) digital teaching competence, as dominant mediator in ICT attitude, ICT skills and data literacy, strongly predicted empowering students. The findings provided valuable evidence for teachers, policymakers, administrators, teacher educators, and teachers to better reimagine the teachers' digital teaching competence. In the future, the teachers' digital teaching competence should become the top priority in teacher ICT training, which was the most direct influencing factor for empowering students. [ABSTRACT FROM AUTHOR]

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. [ABSTRACT FROM AUTHOR]

Summary: Background: In surgery, electronic healthcare systems offer numerous options to improve patient care. The aim of this study was to analyse the current status of digitalisation and its influence in surgery, with a special focus on surgical education and training. Methods: An individually created questionnaire was used to analyse the subjective assessment of the digitalisation processes in clinical surgery. The online questionnaire consisted of 16 questions regarding the importance and the corresponding implementation of the teaching content: big data, health apps, messenger apps, telemedicine, data protection/IT security, ethics, simulator training, economics and e‑Learning were included. The participation link was sent to members of the German Society of Surgery via the e‑Mail distribution list. Results: In total, 119 surgeons (response rate = 19.8%) took part in the survey: 18.5% of them were trainees (TR), and 81.5% had already completed specialist training (SP). Overall, 66.4% confirmed a positive influence of digitalisation on the quality of patient care. The presence of a surgical robot was confirmed by 47.9% of the participants. A further 22.0% (n = 26) of the participants confirmed the possibility of using virtual simulators. According to 79.0% of the participants, the integration of digital technologies in surgical education for basic and advanced stage surgeons should be aimed for. Data protection (1.7) and e‑Learning (1.7) were rated as the most important teaching content. The greatest discrepancy between importance and implementation was seen in the teaching content of big data (mean: 2.2–3.8). Conclusion: The results of the survey reveal the particular importance of digitalisation content for surgery, surgical education and training. At the same time, the results underline the desire for increased integration of digital competence teaching. The data also show an overall more progressive and optimistic perception of TR. In order to meet the challenges of the digital transformation, the implementation of suitable curricula, including virtual simulation-based training and blended-learning teaching concepts, should be emphasised. [ABSTRACT FROM AUTHOR]

Internationally, assessment and the use of diagnostic data are recognized as critical capabilities for teachers. This is not a recent development, with assessment recognized for some decades as playing a significant role in informing learning and learners. This paper will examine whether teachers and members of the school leadership team utilize the National Assessment Program - Literacy and Numeracy (NAPLAN) data for informing teaching and improving learning. Using a theoretical framework that draws on the conceptualisation of assessment as a social practice and Wenger's social theory of learning as a shared enterprise in a community of practice, this paper will provide evidence of the common power relationships (school leaders and teachers) that exist as part of social structures within a community of practice. The paper will present how these power relationships impede access to, use of or enable expertise in the analysis of NAPLAN data. The findings are based on an empirical study of nine case study schools across two Australian states and suggest there are inequalities of access to NAPLAN data between school leaders and teachers. The study discovered variegated access and distinct pathways of data dissemination and analysis dependent on a teacher's specified role within the school. The paper concludes with suggestions for greater stewardship from school leaders in building school cultures of data literacy and highlights the importance of collaboration between school leaders and teachers to build professional capability in this area. [ABSTRACT FROM AUTHOR]

COVID-19 has demonstrated the importance of data for scientific policy advice. Mechanisms by which data is generated, shared, and ultimately lead to policy responses are crucial for enhancing transparency and legitimacy of decisions. At the same time, the volume, complexity and volatility of data are growing. Against this background, mechanisms, actors, and problems of data-driven scientific policy advice are analysed. The study reveals role conflicts, ambiguities, and tensions in the interaction between scientific advisors and policy-makers. The assumption of a technocratic model, promoted by well-established structures and functioning processes of data-driven government, cannot be confirmed. Reality largely corresponds to the pragmatic model, in parts also the decisionist model, albeit with dysfunctional characteristics. [ABSTRACT FROM AUTHOR]

As data become more available and integrated into daily life, there has been growing interest in developing data science curricula for youth in conjunction with scientific practices and classroom technologies. However, the what and how of data science in pre-collegiate education have not yet reached consensus. This paper analyzes two prominent self-identified data science curricula, Introduction to Data Science (Gould et al. 2018) and Bootstrap: Data Science (Krishnamurthi et al. 2020), in order to ascertain what is thus far being presented to schools as data science. We highlight overlapping content and practices by the curricula while noting some key differences between the curricula and with professional practice. Moreover, we examine how lessons are structured and what kinds of data sets are used as well as introduce a measure of data set proximity. We conclude with some recommended areas for further coverage or elaboration in future iterations and future curricular efforts. [ABSTRACT FROM AUTHOR]

Data visualization for alternative narratives is a powerful adversarial tool that seeks to inquire about the existing conditions of conflicts. Nowadays, data visualization is widely used in activist communication to offer data-driven counternarratives to those with dominant power. However, a study of 64 cases shows that most visualizations are far from following the open-source ethos that data activism advocates. If visualizations for alternative narratives do not open up their processes and communicate how the meaning of data is shaped and translated visually, they risk turning into black-box devices that crystallize biased representations of conflict rather than investigating them. This paper presents a data design framework for visualizations used in alternative narratives. It is presented as a methodological tool that encourages actionable data practices that promote a more critical and reflective data culture. The framework is structured in two ways of approaching the process of working with data: from the parts of the process and from the process as a whole. The first approach presents four lenses intended to materialize aspects of the process of working with and making sense of data: Open/close, Composition, Zoom, and Sanitization. The second approach proposes a self-hacking operation of disclosing the production process of the visualizations. This paper argues that visualizations for alternative narratives ought to be open artifacts that promote the democratization of re-interpretation and critical data by disclosing the design decisions that were made on the dataset, as well as its visual, and interactive representation. [ABSTRACT FROM AUTHOR]

This paper is based on (a) a literature review focussing on the impact of learning analytics on supporting learning and teaching, (b) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics as well as (c) outlining a research agenda for closing identified research gaps. Issues and challenges facing educators linked to learning analytics and current research gaps were organised into four themes, the further development of which by the expert panel, led to six strategy and action areas. The four themes are 1. development of data literacy in all stakeholders, 2. updating of guiding principles and policies of educational data, 3. standards needed for ethical practices with data quality assurance, and 4. flexible user-centred design for a variety of users of analytics, starting with learners and ensuring that learners and learning is not harmed. The strategies and actions are outcomes of the expert panel discussion and are offered as provocations to organise and focus the researcher, policymaker and practitioner dialogs needed to make progress in the field. [ABSTRACT FROM AUTHOR]

We report on an exploratory effort to design an interest-based learning experience for high school (step) dancers to engage with concepts in mathematics and data science. We hypothesized that generating and analyzing data from their own dance movement, through motion and audio capture, would (a) enable learners to form analogies between off-line embodied experiences and new abstract concepts and (b) support motivation to learn due to perceived relevance and usefulness of data science to dance practice. Based on initial interviews to understand the specific needs and interests of the steppers, we developed some early prototypes for visual and acoustic analysis, concentrating on pose precision, tempo, and spectral characteristics (timbre) for. Teacher and student reactions to the tools demonstrated support for our hypotheses that off-line embodied cognition would help with new knowledge acquisition and that the perceived usefulness of data science would motivate learning. Several promising future directions remain to develop an interest-based and embodied data science curriculum. [ABSTRACT FROM AUTHOR]

Public trust is paramount for the well functioning of data driven healthcare activities such as digital health interventions, contact tracing or the build-up of electronic health records. As the use of personal data is the common denominator for these healthcare activities, healthcare actors have an interest to ensure privacy and anonymity of the personal data they depend on. Maintaining privacy and anonymity of personal data contribute to the trustworthiness of these healthcare activities and are associated with the public willingness to trust these activities with their personal data. An analysis of online news readership comments about the failed care.data programme in England revealed that parts of the public have a false understanding of anonymity in the context of privacy protection of personal data as used for healthcare management and medical research. Some of those commenting demanded complete anonymity of their data to be willing to trust the process of data collection and analysis. As this demand is impossible to fulfil and trust is built on a false understanding of anonymity, the inability to meet this demand risks undermining public trust. Since public concerns about anonymity and privacy of personal data appear to be increasing, a large-scale information campaign about the limits and possibilities of anonymity with respect to the various uses of personal health data is urgently needed to help the public to make better informed choices about providing personal data. [ABSTRACT FROM AUTHOR]

We present an overview of the work of the Search Studies research group, focusing on commercial search engines from a user perspective. This encompasses studying what users of these search engines get to see on the result pages, how users interact with search engines, and the effect both have on knowledge acquisition in society. Our research combines search engine data analysis, by collecting and analysing data from commercial search engines (data science), with understanding information-seeking behaviour through conducting user studies in different settings (information science), ranging from large, representative online surveys to behavioural studies in the lab employing, amongst others, eye-tracking. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of AStA Wirtschafts- und Sozialstatistisches Archiv is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Real time data associated with the Building Information Model plays a critical role in the interpretation of the built environment, which is particularly relevant as an increasing number of education facilities and institutions promote sustainable engineering practices and monitoring data available to the public. However, it is challenging for non-technical audiences to fully comprehend or use information concealed in scientific data related to the performance of structures and materials. It is especially difficult for them to connect these concepts to physical contexts and phenomena. In this paper, we present how cross-reality paradigms in Architecture, Engineering, and Construction, coupled with multimodal representation techniques, enhance data literacy in both professionals and laypeople alike. In particular, we present the design of a learning environment where cutting-edge holographic interfaces and display technologies are combined with sonified and visual data to create a more immersive environment for data analysis and exploration, empowering users with situated data awareness and new ways of understanding real-time data. [ABSTRACT FROM AUTHOR]

Data literacy has become an important part of STEM literacy and informed citizenship in our society. However, data literacy initiatives in secondary schools predominantly focus on narrow issues of developing students’ ability to work with quantitative data and fail to address students’ broader understanding both as producers and consumers of data. This study examined how secondary school students (N = 27) understand data and its relevance in everyday life. Students responded to a short pre-project survey and worked on a biology inquiry project producing poster-based science news. We inductively coded text responses and created themes that represent students’ descriptions of data. We also analysed the nature of data students used while working on their project and producing posters. Results showed that students’ understanding of data were limited to contexts of (a) experiment and survey, (b) utility and usage information, and (c) numerical charts and graphs. However, students used broad range of data in their authentic projects compared to their written description. Results provide insight about designing data literacy interventions and the importance of developing students’ broader awareness related to the nature of data in everyday life. [ABSTRACT FROM AUTHOR]

This article explores 6th-grade students’ modelling with data in generating models for selecting an Australian swimming team for the (then) forthcoming 2016 Olympics, using data on swimmers’ times at various previous events. We propose a modelling framework comprising four components: working in shared problem spaces between mathematics and statistics; interpreting and reinterpreting problem contexts and questions; interpreting, organising and operating on data in model construction; and drawing informal inferences. In studying students’ model generation, consideration is given to how they interpreted, organised, and operated on the problem data in constructing and documenting their models, and how they engaged in informal inferential reasoning. Students’ responses included applying mathematical and statistical operations and reasoning to selected variables, identifying how variation and trends in swimmers’ performances inform model construction, recognising limitations in using only one performance variable, and acknowledging uncertainty in model creation and model application due to chance variation. [ABSTRACT FROM AUTHOR]

The Thinking with Data project (TWD) expands on current notions of data literacy by (1) focusing on proportional reasoning as key to data literacy and (2) leveraging the non-mathematics disciplines to engage students in deep thinking about the context of data and the application of proportionality. A set of four 2-week, sequential modules for cross-disciplinary implementation in seventh-grade classrooms was designed and evaluated. Using a quasi-experimental approach, we found that student data literacy was increased through the focused integration of social studies, mathematics, science, and English language arts. In this article, we describe our theoretical approach to designing and implementing the modules, report on student learning gains in mathematics, and describe teacher reactions to the materials. In sum, our study provides evidence that the TWD approach has the potential to build data literacy while also allowing students to learn core discipline-based content standards. [ABSTRACT FROM AUTHOR]

Student success is becoming a shared vision for quality in higher education. Majority data in higher education have not been transformed into actionable insights for quality enhancement. Data are dispersed among stakeholders, and stakeholders' data literacy influences the effectiveness of using data for student success. However, existing studies mainly focus on students' data literacy; the analysis of other stakeholders' data literacy for student success is still few. This study aims to explore how stakeholders' data literacy contributes to student success in a holistic view. The salience model is used to identify core stakeholders. The goal-modeling language iStar is used to present how stakeholders contribute to student success. A competencies matrix of data literacy is used to discuss the specific data literacy competencies that stakeholders should focus on promoting student success. A survey is conducted to validate the goal-oriented analysis and the discussions on specific competencies of data literacy for stakeholders. The goal-oriented analysis presents the complexity of interactions and dependencies among stakeholders for student success. This study helps to raise stakeholders to be aware of the importance of their data literacy and the necessity of collaboration on exploiting vast available data for student success. [ABSTRACT FROM AUTHOR]