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This report details the investigation of practices of instructors who use quantitative data to teach undergraduate courses within the Social Sciences. The study was undertaken by employees of the University of California, Santa Barbara (UCSB) Library, who participated in this research project with 19 other colleges and universities across the U.S. under the direction of Ithaka S+R. Ithaka S+R is a New York-based research organization, which, among other goals, seeks to develop strategies, services, and products to meet evolving academic trends to support faculty and students.

This report details the investigation of practices of instructors who use quantitative data to teach undergraduate courses within the Social Sciences. The study was undertaken by employees of the University of California, Santa Barbara (UCSB) Library, who participated in this research project with 19 other colleges and universities across the U.S. under the direction of Ithaka S+R. Ithaka S+R is a New York-based research organization, which, among other goals, seeks to develop strategies, services, and products to meet evolving academic trends to support faculty and students.

This paper outlines an example of Special Collections materials being used to support master's-level students' data and digital skills development. It evidences positive student learning outcomes through an unconventional interdepartmental collaboration between a digital media teaching team and Special Collections staff, which combined digital practices and tangible Special Collections material. This paper outlines the readings used for the course under review, the assignments used to evaluate students, student experiences of those assignments, and staff observations of student success and the logistics of course delivery. Findings show that in completing their assignments students (a) negotiated uncertainty; (b) increased understandings of data and digital methods; (c) embraced the lifeworlds of data; and (d) blurred the boundaries of analog and digital. More generally, this work finds that Special Collections material can be effectively used to support data and digital skills development, as well as help students gain broader understandings of digital contexts and media. Value is, however, dependent upon embracing uncertainty: something that universities tend to avoid. But, as is shown, staff and students alike can thrive in states of bewilderment. [ABSTRACT FROM AUTHOR]

The phenomenon of the data deluge is a reality and the volume of data produced by people and companies is much greater than what can be handled and analyzed. Data play a crucial role in guiding the efficient utilization of technological resources for companies, aiding them in product and service management. Moreover, individuals who have become adept data producers and consumers are increasingly orienting their lives toward data. To address this evolving trend, there is a growing imperative to educate Computing professionals. These professionals are required to design technology solutions that facilitate the synergy between individuals and data, a phenomenon known as Human-Data Interaction (HDI). This paper introduces a suggested minimum syllabus for HDI courses, with the aim of addressing the key themes associated with the interaction between individuals and data. The complexity and depth of HDI topics justify a dedicated course, preventing the risk of essential content being fragmented or inadequately covered if dispersed across different courses.

and datafication within societies over the last years. In this context, the concept of data literacy has also gained in importance, as it is seen as one of the skills that citizens require in order to navigate and participate in society. However, despite the relevance of the concept, it is yet to be discussed as to the ways to promote it at the institutional level. In order to perform such a task, we conducted a review that combines approaches from a mapping review with that of a traditional narrative review, making a series of policy recommendations towards a data literate society. Focusing on the period between 2015 and 2021, we analysed a corpus of 39 empirical and theoretical studies. Findings confirm the scattered and interdisciplinary nature of research on data literacy, yet paving the way for a series of recommendations to promote the former.

This paper introduces a study aimed at understanding informal learning to develop open data literacy. Based on the open datasets and reports published by the Open Knowledge Foundation (OKF) on the globally organised event Open Data Day 2021, we extracted 20 records and applied to them quantitative and thematic analysis. Convergent and diversified patterns of engagement with the Open Data Day across the five continents were observed. On this basis, some formal and particularly, informal learning activities arise as a collaborative and participatory work of data communities seeking to work with open data. Along the several experiences there is concern about the technical skills to search, find and use Open Data, including data storytelling abilities. However, this set of abilities was not connected with a set of critical and holistic skills, required to promote some of the goals expressed by the ODDs. Overall, integrating formal and informal learning into a situated context should be considered to promote open data literacy. Dathatons nel mondo: Potenziale per lo sviluppo di una alfabetizzazione ai dati (aperti). Questo documento introduce uno studio volto a comprendere l’apprendimento informale per sviluppare l’alfabetizzazione dei dati aperti. Sulla base dei set di dati aperti e dei rapporti pubblicati dalla Open Knowledge Foundation (OKF) sull’evento Open Data Day 2021 organizzato a livello globale, abbiamo estratto 20 record e applicato loro analisi quantitative e tematiche. Sono stati osservati modelli di impegno convergenti e diversificati con l’Open Data Day nei cinque continenti. Su queste basi, alcune attività di apprendimento formale e in particolare informale nascono come lavoro collaborativo e partecipativo delle comunità di dati che cercano di lavorare con i dati aperti. Lungo le diverse esperienze c’è preoccupazione per le capacità tecniche per cercare, trovare e utilizzare gli Open Data, comprese le capacità di data storytelling. Tuttavia, questo insieme di abilità non era connesso con un insieme di abilità critiche e olistiche, necessarie per promuovere alcuni degli obiettivi espressi dagli ODD. Nel complesso, l’integrazione dell’apprendimento formale e informale in un contesto situato dovrebbe essere presa in considerazione per promuovere l’alfabetizzazione dei dati aperti.

Digital data has consolidated as the fuel of technological development. The commercialisation of Large Language Models and Generative Artificial Intelligence has led to the massive adoption of Artificial Intelligence applications by the general public, bringing data literacy to a prominent position in the public debate. In this data-driven economy and society, academia and industry have to collaborate to meet the needs of a data-literate workforce (León et. al., 2020). Moreover, data literacy is also a powerful enabler of civic engagement, as it empowers individuals and communities to keep governments transparent and accountable, tackle local issues, and navigate their own data ecosystems. In this regard, it is also essential support for the wide exploitation of open data and open government resources. Nonetheless, the data-driven practices have led to critical situations, injustice, and concern in several areas of human activity, from the public to the private sector. The growing availability of data and the increasing incentives to use it, in fact, have brought about a rising concern known as data literacy. This concern is strongly driven by the fact that many individuals lack the necessary skills to effectively utilise the available data, as well as the necessary competencies to make ethical and responsible use of such data. This special issue contains papers supporting this notion, emphasising that data literacy encompasses not only the ability to work with data but also the ability to understand its use and value within different contexts, including areas like citizen science and digital civic engagement. While there has been an increase in research on data literacy in recent years, much of the existing research is confined to specific disciplinary areas such as research data, and there is still a relative neglect of the civic and citizen context (Yousef, Walker, & León-Urrutia, 2021). Simultaneously, data has increasingly become a part of citizens' lives through the presence of algorithms, machine learning, and the potential for artificial intelligence. Hence, this special issue has aimed to gather papers that explore the possibilities of comprehensive multidisciplinary research on the societal implications of data literacy, its significance, and how it can be effectively fostered. In fact, the concept of data literacy, as the educational activity aimed at developing understanding and skills relating to such dimensions of our societies, spots the contextual and diversified nature of data practices in response to or reaction to metrics, quantification, and algorithms. The research on the topic has highlighted the existence of practices of participation linked to data appropriation to express cultural diversity, civic empowerment, and hence social and economic innovation. Instead, another strand of research is particularly focused on uncovering algorithmic bias, unstructured data usage, and the search for data justice. From these diversified strands of research, there emerges a clear need to embrace interdisciplinary discussion and collaboration in order to explore and expand the concept and practice of data literacy. Specifically, teachers and educators at all levels of the schooling system, including higher education and lifelong learning, are called to transform their practice through the introduction of data literacy as a contextualised and complex perspective on an emerging technological revolution in contemporary society. To this aim, the focus of this special issue was to gather research that deals with data literacy as an emerging topic, and an area of knowledge and practice that requires reflection, discussion and transformative action. To that end, we highlight the relevance of building understanding on the basis of empirical research as a perspective on a complex and emerging social problem. Specifically, we called for contributions referring to research dealing with conceptual competence frameworks or models, applying constructs, and/or catering to practical cases showing the benefits of different data literacy experiences for different target groups across lifelong learning. We collected four relevant research articles, which bring to the fore the polysemy embedded in the concept of data literacy, as well as the multiplicity of practices it can generate. Data literacy is not a crucial competence only in Higher education contexts, it is more and more important to promote the introduction of these competences at an earlier stage

Objective: Data literacy is the ability to describe, evaluate, use, share, and cite data. It is increasingly important for researchers and college students, including in the field of economics. This study explores the prevalence of data literacy competencies in economics articles. Data literacy competencies displayed in journal articles demonstrate what researchers value and provide opportunities to teach students, helping librarians shape data services and instruction.Methods: Based on close reading of economics and data literacy literature, the author developed a protocol of terms relating to data literacy. A stratified random sample of 100 articles was selected from ten top economics journals. Adobe Acrobat’s index search function was used to conduct automated content analysis coding, with additional manual checking for accuracy and data sharing and sources.Results: The economics research articles in the study sample showed strong coverage of terms relating to describing, evaluating, and using data. Sharing and citing data were identified as areas for improvement as only 36% of articles shared data and 40% included terms related to citation. The analysis verifies previous research about the prevalence of commercial data use in business research and adds insight on frequently used open data sources. Conclusions: There are clear data literacy strengths within economics. Librarians have the skills to partner with economics instructors to reinforce strengths and improve gaps to prepare more data literate students.

This paper is a position paper that is not based on formal research. Its purpose is to propose a Data Literacy Framework for support in the process of the bridging the gender gap in Western Balkans among women entrepreneurs. To succeed in today’s data-driven economy, women entrepreneurs need data skills and digital skills to unlock opportunities and grow businesses. The focus of any successful business in modern economies is how to equip women with suitable data and digital literacy so they can pursue careers in the digital economy, to contribute to the digital transformation of the economy and the public sector, especially in the Western Balkans developing countries. The proposed Data Literacy Framework should be used as an instrument to devise public policy measures for providing education and training opportunities, design customised data literacy upskilling for women entrepreneurs, implement career guidance services, promote the role of the women in the digital economy, and provide support for more effective deployment of various concepts of the digital society.

Data Literacy is a germane competence for anyone or institution desirous to scale the challenges of the 21st century and be globally recognized. Data literacy is the ability of individual to identify, work with, analyze and communicate data effectively. It is perceived that there is a poor data literacy competence in higher institutions in developing countries. Researchers in the 21st century, for example, ought to possess skills and competencies to communicate through affiliated institutional email addresses; be active on platforms such as Google scholar and research gate; and, possibly own personal websites to drive teaching and learning. This paper, through qualitative analysis of existing literature, attempts to examine the interplay between data literacy and higher education in the 21st century. Data Literacy is relevant not only to individuals but also to higher education. The phenomenon data literacy is relevant to institutions of higher learning because institutions of higher learning are acknowledged centers of human resources training for national and sustainable development. The paper will be of significance to governments, policy makers, and administrators of higher institution in particular. Furthermore, the paper will be of benefit to scholars as data literacy continues to shape frontiers of teaching and learning across the globe in the 21st century.

This research article explores the impact of new literacy skills, including data literacy, technological literacy, and human literacy, on regional economic development in the creative economy. The study, conducted in Indonesia, finds that these literacy skills are important for the growth of businesses in the creative economy and can contribute to regional economic development. However, there is a need for government policies to increase literacy among creative economy actors. The article suggests that future research should focus on the development of these literacy skills in the post-pandemic period. [Extracted from the article]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University 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.)

This research addresses the growing social importance of data from an educational perspective through data literacy (DL), seeking to integrate it into the broader information literacy (Infolit) movement. For this purpose, a systematic review was carried out of the papers in the main collection of the Web of Science that contain both concepts (DL and Infolit) and that were indexed up until March 2023. External aspects, such as the growth of the research and the identity, nationality, professional scope, and productivity of the authors, were taken into account. In addition, internal aspects, such as context (theory, frameworks, definitions, models, and related disciplines), objectives, methodology, results, conclusions, and recommendations, were analyzed to obtain a detailed perspective of the scientific research process adopted. A synchronic and diachronic analysis of the corpus of selected articles is offered, focusing on the aforementioned aspects. The researchers' consensus on the urgency of addressing data training both generally and specifically in the different disciplines, languages, environments, and levels is evident. The emergent, multisectoral, and interdisciplinary nature of data literacy as part of Infolit, which is being applied in the education of students at different levels, viz. professionals and citizens, is noted, although the training limitations of students and many professionals are evident. Consequently, it is imperative to include DL in curricula and training programs to contribute to the acquisition and development of these competencies in different areas. To this end, the joint work of teachers, librarians, researchers, and other professionals is imperative. There is a need to deepen the theoretical, practical, and applied fields, as well as to reach a common definition, form a basic model of DL competencies within Infolit, and create submodels that take into consideration the idiosyncrasies of each area of application. [ABSTRACT FROM AUTHOR]

With the emergence and development of the digital economy, digital society, and digital government, data culture has also emerged. Library data culture includes five aspects, namely library culture of data-related skills and attitudes, library culture of data sharing, library culture of data use and reuse, li- brary data ethics and governance culture, and library-specific data culture. Taking representative academic libraries at home and abroad as examples, the path of library data culture construction is analyzed from the perspectives of strategic planning and best practices, including data-driven research, data-driven services, data-driven management, and data-driven decision-making. The construction of library data culture faces risks such as data security and intellectual property, as well as challenges in digital technology development and application, capacity building and talent cultivation, and external competition. Libraries can sustainably carry out data culture construction by establishing a data mindset, strengthening capacity building, enhancing talent cultivation, prudently addressing process risks, and actively embracing external challenges. [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]

Many universities have wellness programs to promote overall health and well-being. Using students' own personal data as part of improving their own wellness would seem to be a natural fit given that most university students are already data and information literate. In this work, we aim to show how the interplay between health literacy and data literacy can be used and taught together. The method we use is the development and delivery of the FLOURISH module, an accredited, online-only but extra-curricular course that delivers practical tips in the areas that impact students' everyday wellness including sleep, nutrition, work habits, procrastination, relationships with others, physical activity, positive psychology, critical thinking, and more. For most of these topics, students gather personal data related to the topic and submit an analysis of their data for assessment thus demonstrating how students can use their personal data for their benefit. More than 350 students have taken the module and an analysis of the use of online resources, as well as feedback on the module experience, are presented. The contributions of this article are to further endorse the need for health literacy and digital literacy for students, and we demonstrate that these can be taught together making each literacy more appealing to the digital natives of Generation Z who make up the majority of students. The implications for public health research and practice are that two student literacies, health and digital, are not independent and for our students, they should be taught together. [ABSTRACT FROM AUTHOR]

Universities are increasingly interested in providing courses that equip students with data science skills and engage experiential learning, particularly in the social sciences. However, these courses can be costly to implement and time-consuming for instructors to develop. This article describes an integrative learning model for teaching computational social science skills to undergraduate students. There are three elements to the model: content delivery through collaborative learning, skill development in an applied lab setting, and student mentorship. I apply this model to an experiential course where undergraduate students learn to create and conduct a national public opinion poll. The model addresses the need for university classes that equip undergraduate students with computational social science skills, and provides a pathway for training student researchers as teaching assistants for future courses. This model builds the research capacity of faculty, graduate assistants, and undergraduates, invests in data science by providing an infrastructure for the collection of large scale data over time, and integrates experiences inside and outside of the classroom. This model is replicable at other institutions and will be of benefit to programs seeking to implement best practices and maximize learning effectiveness through research integration. [ABSTRACT FROM AUTHOR]

Is public engagement with science deliberative and evidence‐based? The public is often perceived as underprepared to use data and susceptible to partisan and emotional manipulation. Consequently, educational efforts focus on the ability to identify reliable information. We posit that effective engagement with science goes beyond this and hinges on data literacy. We leveraged the unique circumstances of COVID‐19, where diverse people inundated with pandemic‐related data representations in the media needed to make consequential decisions, to examine whether people use data and what factors affect such use. In a survey of a representative Israeli adult sample, participants reported their information habits and beliefs before and during COVID‐19. On being presented with graphs and datasets, they answered data literacy and COVID‐19‐related functional reasoning assessments (e.g., would you travel abroad?). Data literacy distinguished those who incorporated data from those who did not. Yet, participants incorporated moral, social, and economic considerations at all data literacy levels, suggesting that people may be deliberative even when they do not attend to quantified data. Moreover, participants' trust in science and data interpretation competence were key factors mediating the relationship between self‐efficacy in data interpretation skills and the incorporation of data in reasoning. The findings extend beyond COVID‐19 to a broader understanding of the factors influencing public engagement with quantitative representations. Rather than focusing solely on remediating data interpretation, we suggest that educative efforts work on multiple fronts and that cultivating trust in science is key to a broader, more deliberative engagement with science. [ABSTRACT FROM AUTHOR]

This research delves into the interplay between three pivotal literacies in the digital age-information literacy, data literacy, and privacy literacy--and the receptivity towards the adoption of emerging technology within communities, with a specific focus on the chatbot ChatGPT. Data was gathered through online surveys conducted among adults residing in a four-county region in northern Texas during a two-week period in late 2022, yielding 130 valid responses. The results of regression analysis indicate a positive association between the inclination to utilize ChatGPT for enhancing one's community and proficiency in information literacy and privacy literacy. However, an unexpected observation emerges as data literacy skills do not exhibit a significant relationship with this inclination, despite ChatGPT's standing as a data science innovation. Moreover, age, gender, educational attainment, and internet usage patterns are identified as influential factors in these associations. These findings hold substantial importance in comprehending the intricate dynamics of how diverse literacies and individual and community-related variables mutually shape each other's development. [ABSTRACT FROM AUTHOR]

This study aims to confirm the effect of industrial education 4.0 on data, technology, and human literacy among postgraduate students in Indonesia. The research adopted covariance-based structural equation modeling by employing AMOS 25 to examine the relationship between variables. The model estimation was performed using confirmatory factor analysis as a standard model for measuring industrial education 4.0 and involved structural model analysis to confirm the hypotheses. The population in this research paper was 2.958 postgraduate students in 32 study programs, while the sample was 312 respondents, which was determined using the proportional random sampling technique. This study tested the three hypotheses proposed, showing that industrial education 4.0 has a positive and significant effect on data literacy, technological literacy, and human literacy among postgraduate students in Indonesia. This paper raises the need for developing industrial education 4.0 on these literacies. [ABSTRACT FROM AUTHOR]

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Data literacy has become a much sought after skill by organizations as the importance of data driven decision making continues to rise in importance. This study explores the teaching of Tableau as a supplement to Excel to enhance the data literacy skills of students. Twenty-eight students taking an introductory course in Business Analytics and Information Systems completed a series of five tasks in both Excel and Tableau and answered a two question survey on their perceptions of the relative ease of use and ability to produce correct and visually pleasing results for Tableau versus Excel. An assessment of the results showed that Tableau submissions produced higher scores in terms of correctness and more professionally formatted visualizations. Student survey responses also supported a significant preference for Tableau over Excel in terms of both ease of use and ability to produce correct results. [ABSTRACT FROM AUTHOR]

Digital data play an increasingly important role in how we understand the present and the past. The challenges inherent in understanding and using digital data are as intellectually demanding as any other archaeological research endeavor. For these reasons, data management cannot be regarded as a simple compliance or technical issue. For data to be meaningfully preserved and used in intellectually rigorous ways, they need to be integrated fully into all aspects of archaeological practice, including ethics, teaching, and publishing. In this review, we highlight some of the significant and multifaceted challenges involved in managing data, including documentation, training, methodology, data modeling, trust, and ethical concerns. We then focus on the importance of building data literacy broadly among archaeologists so that we can manage and communicate the data our discipline creates. This involves more than learning to use a new tool or finding a data manager for one's excavation or survey. Long-term, responsible stewardship of data requires understanding the workflows and human roles in data management. Putting effort now into thoughtful data management and broad data-literacy training means we will be able to make the most of the “bigger” data that archaeologists now produce. An important aspect of this reorientation will be to look beyond the boundaries of our own research projects and information systems. Future research, teaching, and public engagement needs will also compel us to explore how our data articulates with wider contexts—within and beyond our discipline.

Objective: The aim of this study is to explore how data literacy can influence the research and publications productivity of Kyambogo University academic staff.Methods: The study employed a literature review to collect detailed information. It observed lessons, and studied patterns of the phenomenon to explore data literacy initiatives that can be used by Kyambogo University academic staff to improve their research publications productivity and also to mitigate the accrued challenges. Results: The paper explored eight initiatives through which data literacy skills could enhance the research productivity of Kyambogo University academic staff. These were awareness and promoting freedom of using open data, engaging students in data literacy activities, pedagogical reflection, datafication of single and joint academic staff publications, visualization of data, storytelling, ethical use of data, and preservation of research data. Conclusions: While this paper relies on the context of the Kyambogo University academic staff, the authors posit that these data literacy skills can be embraced by universities in developing economies; especially those struggling with poor research and publications productivity. The paper further identifies areas where universities in developing economies, in conjunction with their libraries, can improve the academic staff pedagogy and compliance to eScience through polishing their data literacies.

Data literacy is a vital life-skill that is becoming increasingly necessary in today's society. As such, public data literacy education must become a substantive component in teaching, requiring an appropriate pedagogical model. This paper aims to present a new definition for data literacy, as well as conceptual framework for public data literacy education, drawing from tenets of design-based education and synthesized principles of data literacy education from literature. The resulting framework is termed the Data Design Cycle, and emphasizes collaboration, communication, and the development of problem-solving skills. The model is highly adaptable to different educational environments and learning styles, making it easily integrable into a variety of educational settings, including traditional classroom-based learning, online learning, and experiential learning programs. Furthermore, by promoting the principles of public teaching of data literacy and a learner-centred approach, the Data Design Cycle ensures that data literacy education is accessible to heterogeneous learners from diverse educational and socioeconomical settings.

Data-driven school improvement has been proposed to improve and support educational practices, and more studies are emerging describing data-driven practices in schools and the effects of data-driven interventions. This paper reports on a study that has taken place within a national program where 15 schools from 6 different municipalities and organizations are working at classroom, school and municipality levels to improve educational practices using data-driven methods. The study aimed at understanding what educational problems teachers, principals and administrative staff in the project aimed to address through the utilization of data-driven methods and the challenges they face in doing so. Using a mixed-methods design, we identified four thematic areas that reflect the focused problem areas of the participants in the project, namely didactics, democracy, assessment and planning, and mental health. All development groups identified problems that can be solved with data-driven methods. Along with this, we also identified five challenges faced by the participants: time and resources, competence, ethics, digital systems and common language. We conclude that the main challenge faced by the participants is data literacy, and that professional development is needed to support effective and successful data-driven practices in schools. [ABSTRACT FROM AUTHOR]

The current study used a randomized control trial to evaluate a decision‐making model on special education preservice candidates' instructional decision‐making and self‐reported confidence ratings when analyzing graphed student data. Thirty‐two special education preservice candidates viewed authentic curriculum‐based measurement (CBM) graphs and selected an instructional decision based on the data. Half of the participants received a decision‐making model (i.e., flowchart) to guide instructional decision‐making and all participants were asked to rate their level of confidence analyzing graphs pre‐ and poststudy. Statistically significant and large differences in the accuracy of instructional decisions and confidence ratings were observed for the participants who used the decision‐making model relative to the control group. The results of the study suggest that a simple, cost‐effective intervention that does not require training from an expert can have a positive impact on teachers' data‐based decision‐making. Practitioner Points: A decision‐making model increased preservice teacher candidates' accuracy in making data‐based decisions when analyzing curriculum‐based measurement progress monitoring graphs.Teacher candidates that used the decision‐making model reported increased levels of confidence compared to teacher candidates that did not use the decision‐making model.Decision‐making models increase teacher candidates' accurate data‐based decision‐making and do not require extensive training or expertise to use. [ABSTRACT FROM AUTHOR]

Today's world is characterized by the extensive production of data in different scenarios that everyday citizens need to understand for their informed participation in society. With the increase in the availability of data in a society defined by the industrious production of data, the educational system needs to think of possibilities to bring young children closer to the world of data science. This paper presents a nontraditional data exploration experience with an 8‐year‐old participant helped by a data visualization tool. A task‐based interview was conducted while the participant explored a carbon dioxide emission dataset. This paper studied how the participant interrogates the data, draws inferences and exhibits dispositions. At the end, some reflections are presented when introducing the exploration of nontraditional data in teaching. [ABSTRACT FROM AUTHOR]

As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re‐conceptualizing how we teach undergraduate statistics and data science courses for majors and non‐majors alike. In this paper, we focus on three crucial components for this re‐conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re‐conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses. [ABSTRACT FROM AUTHOR]

The growing role of research data and the rise of open science has also led to a demand for data literacy, which also serves managing, critically sharing and reusing research, business and citizen data. Focusing on these areas, this paper presents the characteristics of information literacy and data literacy, including their critical versions approaches. [ABSTRACT FROM AUTHOR]

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