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Data literacy for students in nonquantitative fields is important as statistics become the grammar of research and how the world's decisions are made. Statistics courses are typically offered by mathematics or statistics departments or by social and natural sciences such as economics, political science, psychology, and biology. Here we discuss how to construct a statistics course for students in nonquantitative fields, with a goal of integrating statistical material with students' substantive interests, using student-focused teaching methods and technology to increase student involvement. We demonstrate this kind of hybrid course with the example of an introductory applied statistics class, taught at both the University of Toronto's Anne Tanenbaum Center for Jewish Studies and the United States Naval Academy. [ABSTRACT FROM AUTHOR]

Background: Data literacy (DL) is vital for teachers, as it enables them to build on data and improve teaching and learning. Therefore, developing DL among pre‐service teachers is critical. Objectives: The purpose of this study is threefold: to evaluate whether a feedback visualisation of peer assessment‐based teaching approach (FVPA‐based teaching approach) can (1) promote pre‐service teachers' DL; (2) enhance their learning motivation; and (3) improve their cognitive load. Methods: The research was conducted based on a pre‐test‐post‐test control group quasi‐experimental design. With 20 participants in the experimental group and 21 in the control group, a total of 41 pre‐service teachers were included in the study. The pre‐service teachers in the experimental group adopted the FVPA‐based teaching approach, and those in the control group adopted the traditional peer assessment‐based learning approach. Results and Conclusions: The experimental group participants outperformed the control group participants in DL, learning motivation, and cognitive load. FVPA was conducive to helping pre‐service teachers critically interpret data, understand their teaching and learning issues, and improve self‐reflection. The findings indicate a reciprocal relationship between learning motivation and DL; improving the learning motivation of pre‐service teachers could promote their DL. Implications: This study contributes to current knowledge by providing empirical evidence on the benefits of an FVPA‐based teaching approach in improving pre‐service teachers' DL, motivation, and cognitive load. The study findings, limitations, and prospects for future research are discussed. Lay Description: What is already known about this topic?: Teacher data literacy has emerged as a crucial competency for the forthcoming generation of educators, gaining attention from education institutions globally.Peer assessment, a well‐established practice in pre‐service teacher education, has proven effective in enhancing the learning experience for disadvantaged students by fostering motivation and improving academic performance.Despite the acknowledged benefits of feedback visualisation in facilitating self‐reflection, motivation, and academic performance, creating meaningful visualisations remains a challenge for educators and learners. What this paper adds?: This study introduces feedback visualisation in peer assessment to enhance comprehension of feedback information among pre‐service teachers.Feedback visualisation augments the benefits derived from peer assessment, refining data literacy, improving learning motivation, and alleviating cognitive load among pre‐service educators.The analysis of data directly relevant to pre‐service teachers and the provision of timely feedback can motivate critical analysis and utilisation of data, facilitating the identification of pedagogical and learning challenges.The study explores intricate interactions among learning motivation, data literacy, and cognitive load within the context of peer assessment visual feedback. The implications of study findings for practitioners: We encourage teacher educators to implement peer assessment instruction grounded in visual feedback for a comprehensive understanding of learning outcomes, identification of pedagogical challenges, determination of causes, and evidence‐informed instructional decision‐making.Teacher educator should diversify strategies to augment the motivation of pre‐service teachers, cultivating their proactive engagement in data analysis, interpretation, and application to enhance overall data literacy.We recommend that teacher educators provide rich visual feedback or present complex data in a visually accessible format for pre‐service teachers who need additional assistance to mitigate cognitive load and facilitate successful learning outcomes. [ABSTRACT FROM AUTHOR]

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]

Pendulum paintings are popular in modern art and social media as a simple way to create beautiful patterns. This article shows how pendulum paintings can be used to connect art with math and science in classrooms of any level. Students use the painting process to collect data. They also use other "messy data" that may be hard to measure or see to make mathematical models that can predict how long the pendulum would swing. Real-world measurements and models used by scientists and engineers often involve messy data from complex systems. The activity can be easily adapted to and differentiated across multiple grade and ability levels. The activity helps students to analyze what they see, to consider variables that may be hidden, and to perceive math as a useful tool in science. The goal of the exercise is not to necessarily find the right answer, but to ask the right questions to make a testable mathematical model. [ABSTRACT FROM AUTHOR]

The Titanic disaster is a topic that continues to fascinate. As the importance of analytics continues to grow in industry, data literacy skills have become increasingly important in business education. This project allows students to use the passenger data from the Titanic to build their data literacy skills using an engaging, experiential topic. The project requires students to extract, transform, describe, analyze, and draw conclusions regarding the factors that impacted survival on the Titanic. The project can be deployed using various application software and tools. We describe how the assignment can be completed using Excel, Tableau, and Python using the Pandas library. [ABSTRACT FROM AUTHOR]

Much has been written on the skills and capabilities of a new generation raised with social media, smartphones, and immediate access to data and information via the Internet. Today's college students grew up using the Internet, where they both generate and consume data. But do incoming college students have the skills necessary to thrive in a digital world that requires the ability to generate, analyze, and share insights from data? This paper presents a study, performed at two small New England institutions each with a business focus, which examines the digital skill sets of first-year college students in relation to the skills they have developed before entering college. The authors also consider whether there is a "digital divide" among first-year college students in relation to their previous technology skills. When applying the Databilities framework for evaluating data literacy competencies, results show that teaching data literacy skills to first-year college students will be critical to their academic success as future information technology workers. [ABSTRACT FROM AUTHOR]

Textbooks offer instructors an opportunity to promote data visualization and statistical literacy throughout the sociology curriculum. In this study, we examined 463 data visualizations from 27 textbooks for Introduction to Sociology, Social Problems, and intermediate elective courses to illuminate the range of data visualizations and their use of statistical data and numerical variables. We find that textbooks rely on a narrow range of data visualizations (e.g., bar/column charts and tables), statistical data (e.g., percentages), and numerical variables (e.g., age). Introduction to Sociology textbooks used more data visualizations, and Social Problems textbooks incorporated more types of statistics. In contrast, intermediate-level textbooks presented more measures of central tendency. Overall, our results suggest textbooks could better integrate a broader range of figures, statistical data, and numerical variables. We conclude with resources for integrating data visualization in instruction. [ABSTRACT FROM AUTHOR]

It is expected that public policy should be based on evidence rather than on any ideology, or any pre-decided goal. Public policymaking should ideally be 'evidence-based.' Data and information constitute evidence. Data needs to be processed and analysed to be used as evidence. To process and analyse data, we need data literacy. The Covid-19 crisis revealed the importance of data and data literacy for sound policy decisions. In the post-pandemic world, data literacy will be an essential skill for policymakers and civil servants. Data literacy should be an essential part of the relevant undergraduate and post-graduate degree curriculums, but also of the in-service training programmes for civil servants. [ABSTRACT FROM AUTHOR]

Data literacy for students in nonquantitative fields is important as statistics become the grammar of research and how the world’s decisions are made. Statistics courses are typically offered by mathematics or statistics departments or by social and natural sciences such as economics, political science, psychology, and biology. Here we discuss how to construct a statistics course for students in nonquantitative fields, with a goal of integrating statistical material with students’ substantive interests, using student-focused teaching methods and technology to increase student involvement. We demonstrate this kind of hybrid course with the example of an introductory applied statistics class, taught at both the University of Toronto’s Anne Tanenbaum Center for Jewish Studies and the United States Naval Academy.

Despite receiving increased attention from researchers in mathematics education, there is still no comprehensive understanding of the current level of data literacy in the teaching and learning of mathematics. To address this gap, this study pre­sents a review of 247 papers selected from the Sco­pus database between 2009 and 2024. The research aims to explore the following: (i) The overall vol­ume, geographic distribution, and development tra­jectory in the literature on data literacy in mathe­matics. (ii) The researchers and research collabora­tions that have had the greatest influence on the literature on data literacy in mathematics. (iii) The sources that have had the greatest influence on the literature on data literacy in mathematics. (iv) The most important topics in the literature on data literacy in mathematics. It was discovered that the number of publications involving data literacy in mathematics increased from 2016 to 2023. Authors from the Netherlands are the most active in the literature on data literacy in mathematics. The Teacher College Record had the highest number of citations. Lastly, the most important topics addressed in the literature on data literacy in mathematics were data use, data literacy, and data-based decision-making. This study has implications not only for mathematics education researchers but also for other stakeholders in the education sector, including school principals, policymakers, and mathematics teachers.

Technological innovations in the audit process can transform organizations and individuals. Government organizations have developed digital-based audit services. This research focuses on auditor competencies that can be used by government organizations in the digital era. This research explains how to research auditor competency in government organizations in the digitalization era, investigates the focus and criticism of the literature on auditor competency in government organizations in the digitalization era, and develops future research related to auditor competency in government organizations in the digitalization era . A structured literature review was conducted for governmentations in English regarding auditor competency in digitally based government organizations. The results reveal that the number of governmentations related to auditor competency in digital-based government organizations has experienced growth over the last ten years. However, the assessment of auditor competency indicators automatically does not imply all of the factors that influence an auditor's competency, because the assessment of auditor competency indicators can be influenced by organizational culture, and organizational and environmental factors. Guidance on competency becomes dynamic because accounting information continues to change accompanied by technological development. So, assessing auditor competency is an important component for improving audit quality. There is no evidence that states which competency is the most dominant, although information technology competency and problem solving skills or analysis and data literacy are competencies that are often studied by researchers. This research has limitations, namely that future research directions come from recent research. So, it is possible that the picture is still far from complete because no one yet knows the speed of technological evolution. This article focuses on aspects of auditor competency assessment that can be used by government organizations, which emphasize technological developments

AbstractThe proficient handling of data can undoubtedly be regarded as a key skill for the future. However, the need for data competencies is not limited to traditional professions in the information technology environment but is rather necessary across industries and work fields. Consequently, there is a call to integrate such Data Literacy and Data Science competencies into higher education teaching across the breadth of study programs. This study, descriptive in nature, sheds light for the first time on the status quo of this integration. For this purpose, a “Data Science Dictionary” has been developed, that structurally maps a corresponding curriculum of the German Informatics Society (GI). Using quantitative content analysis, more than 13,950 distinct modules from three German study programs (Business Administration, Business & Information Systems Engineering, Computer Science) at two different types of universities are examined. As a result of this comparative study, concise “teaching portfolios” are compared between disciplines, whereby the prevalence, proportion, and depth of the competencies integrated into the study programs become transparent. Thus, this approach can provide a basis for discourse on the future integration of data competencies into, for example, business study curricula; furthermore, it can track the resulting progress in a longitudinal study. Supplementary materials for this article are available online.

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]

As a form of synthetic media built on the Internet's extensive visual datasets with evolving machine learning techniques, deepfakes raise the specter of new types of informational harms and possibilities for image-based abuse. There are calls for three types of defensive response: regulation, technical controls, and improved digital or media literacy. Each is problematic by itself. This article asks what kind of literacy can address deepfake harms, proposing an artificial intelligence (AI) and data literacy framework to explore the potential for social learning with deepfakes and identify sites and methods for intervening in their cultures of production. The article applies contextual qualitative content analysis to explore the most popular GitHub repositories and YouTube accounts teaching "how to deepfake." The analysis shows that these sites contribute to socializing AI and establishing cultures of social learning, offering potential sites of intervention and pointing to new methods for addressing AI and data harms. [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]

This article presents a matrix of competence descriptors aimed at machine translation-oriented data literacy teaching. This competence matrix constitutes the didactics-facing side of the DataLitMT project, which develops learning resources for teaching relevant components of data literacy in their translation-specific form of professional machine translation (MT) literacy to BA and MA students in translation and specialised communication. After highlighting the increasing relevance of both professional MT literacy and data literacy in the context of Translation Studies and professional translation, the article presents and discusses a professional MT literacy framework and an MT-specific data literacy framework, which serve to structure the two frames of reference relevant to this article. Then, the article provides a detailed discussion of the competence matrix developed based on the two frameworks sketched previously. This discussion is intended to show how the individual dimensions and sub-dimensions of data literacy were linked to relevant (sub-)dimensions of professional MT literacy and translated into corresponding competence descriptors. To conclude, the article presents an example of a learning resource for MT-oriented data literacy teaching developed based on the descriptors of the competence matrix. [ABSTRACT FROM AUTHOR]

Nowadays, large amounts of data are produced in every field, and these data have become a strategic asset for individuals, businesses, states, and societies. Data literacy involves the skills of using these data effectively. The first aim of the current study is to determine in which years and in which periods more research has been conducted on education and data literacy, reveal the trends in this field over time, and understand what is at the forefront in education and data literacy by analyzing changes in research trends. The second aim of this study is to understand which topics have a wider impact in the field by examining the content of the most cited studies, and to evaluate the potential for collaboration by analyzing the collaborative tendencies and main topic focuses of authors and organizations. This study adopted a science mapping method to examine the relationship in the field of education and data literacy and reveal a general condition of the research on this subject. Data for this study were collected through the Web of Science (WoS) database. As a result of the analysis, an apparent increase in research on education and data literacy since 2011 was examined, and a trend that peaked in 2020 but rose again in 2022 was identified. It was shown that technology and big data-oriented studies have gained importance in thematic evolution. This analysis provides a valuable resource for understanding the current state in the field of education and data literacy and identifying strategies for potential areas of future development. [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]

In the rapidly evolving landscape of modern healthcare, nurses must proficiently navigate data utilization and grasp the principles of data science. Despite this urgency, nursing stakeholders currently do not fully understand the extent of data literacy or data science literacy they need to acquire. This paper aims to elucidate the distinctions between data literacy and data science literacy, offering insights into strategies for nurturing these competencies within nursing education, research, and practice. Through a state-of-the-art review of 22 articles and six healthcare industry resources, we identified a notable absence of comprehensive frameworks and assessment tools, highlighting key areas for future development. [ABSTRACT FROM AUTHOR]

Digitalization in healthcare and the increasing availability of data demand data literacy competences of nurses and other healthcare professionals including technical, ethical and communication skills. The international Spring School 2023 "Information in Healthcare - From Date to Knowledge" aimed at these competences covering interoperability, data protection and security, data analytics and ethical issues. These topics were embedded in the overall case of data-driven quality improvement for diabetes patients in a region. The curriculum includes an online preparation-phase and a five-days attendance week, incorporating problem-based and group work approaches. According to the student's evaluation, the awareness of the importance of the topics was raised and theoretical as well as practical application skills were improved. The Spring School enhanced data literacy competences, critical thinking, problem-solving, interprofessional und intercultural skills among healthcare professionals. Such course offering can contribute to meeting the increasing challenges of digitalization in healthcare. [ABSTRACT FROM AUTHOR]

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The development of critical data and artificial intelligence (AI) literacy has become a key focus in current discussions in Higher Education, thus it is necessary to develop and advance capacity building, reflectiveness and awareness across disciplines to critically address the possibilities and challenges presented by data and AI. In this paper, through an integrative use of the literature and the review of case studies and best practices in authentic and real world design, we propose a model that develops and enables critical data and AI literacies grounded in citizenship, civic responsibilities, and human centred values, rethinking how we develop knowledge and understanding in our disciplines, and also, in the value of our disciplines to society. The principles of data justice, which acknowledges the growing influence of data, its gathering, and use in society, promoting shared perspectives on how societal problems should be comprehended and addressed.These can provide a useful framework for authentic and real-world assessment design, bridging professional and discipline related knowledge with critical data and AI understanding in alignment with civic and citizenship literacies to examine the challenges we face by the impact of data AI on our societies and democracies. Our exploratory approach examines the relationship between authentic and real-world assessment design and critical data and AI literacy, using data justice as a catalyst for reflection and action to promote a deeper understanding of data and AI ethics through assessment practices that enable educators and students to confidently navigate the complex world of data and AI.

Throughout COVID-19, health officials have relied on data visualizations to communicate urgent messages about the spread of the virus and preventative measures. Relatively few efforts have employed participatory engagement with communities who have experienced a disproportionate burden of COVID-19 illness to shape these communications. Sociologist W.E.B. Du Bois viewed data visualization as an approach to changing the way people think about themselves. This paper describes a community-engaged approach to data literacy skill-building with bilingual Latina learners in an adult English program in Northern California, Bay Area. The curriculum combines data visualization activities with language instruction and preventive health themes. Early work on COVID-19 in 2020-21 emphasized improving health knowledge and message interpretation but later shifted to a critical data literacy perspective, focusing on myth-busting, improving risk messaging in their own social networks, and supporting learners to see the power of their own experiences in data story-telling processes. This pedagogical approach, guided by Charles Brigg's idea of communicative justice priorities, locates adult learners' data visualization work as part of a broader effort to be included in the perspectives that shape knowledge production in today's healthcare system. This approach can be used to examine disparities in information access in linguistically minoritized communities and guide future education interventions.

Zusammenfassung Im Kontext des digitalen Wandels und der damit einhergehenden Datafizierung vieler Lebensbereiche gewinnt der kompetente und kritisch-reflektierte Umgang mit Daten und deren Nutzung an Relevanz (Data Literacy). Vor diesem Hintergrund wird im Beitrag die Thematisierung von Daten und datenbezogenen Anwendungen in Angeboten der Erwachsenenbildung untersucht. Es wurden 137 Programme von Volkshochschulen aus den Jahren 2010 bis 2021 quantitativ und qualitativ hinsichtlich der Verbreitung und der inhaltlichen Ausrichtung datenbezogener Angebote analysiert. Angebote, die potenziell Data Literacy fördern, sind in fast allen Programmen enthalten und verzeichnen eine Zunahme im betrachteten Zeitraum. Die Inhaltsanalyse zeigt, dass viele Angebote Datenschutz und -sicherheit und nur wenige Angebote die aktive Verarbeitung von Daten fokussieren.

Ubiquitous datafication surrounds agriculture stakeholders with data as the raw material for additional value creation, resilience and innovation. Technical and soft competencies of data literacy (DL) are not comprehensively addressed in formal secondary education as an interdisciplinary and general concept. Aiming to assess DL structure set for the agricultural sector and the presence of open data (OD) in formal secondary education, we offer the methodological approach for landscaping 27 DL competencies over six areas of DL adapting the ODI Data Skills Framework for qualitative thematic analysis and quantifying DL structure. Three sources of data: undergraduate (BS) and graduate (MS) course descriptions (561), interviews (32) and course materials (8) offered to students of the University of Zagreb were used to contextualize the approach. At the MS level 47% of courses show DL with prevalent technical DL competencies. Limited DL was found at the BS level (17%) where soft and technical DL competence areas were balanced. Preliminary education guidelines regarding DL in the agricultural sector are given. Further development and use of our methodological approach can ensure core DL competencies and balance soft and entrepreneurship DL in formal secondary education for agricultural sector, but can also be extended to other domains.