Purpose This study explores how youth establish personal relevance with data within a data-art inquiry program. To be specific, this study aims to use the Personal Data Relevance (PDR) framework, adapted from Priniski et al.’s (2018) personally meaningful learning framework, to examine how youth’s engagement with data topics, data sets and data products contributes to meaningful data science learning. Design/methodology/approach The PDR framework is the main framework for understanding personal data relevance in this study. The authors implemented a data-art inquiry program with 16 high school participants in a rural high school setting in East Tennessee. The data included youth-generated data visualizations and transcripts from group interviews, and the analysis involved a qualitative approach combining deductive and inductive coding. Findings Youth’s personal connections with data are through three key dimensions of the PDR framework: Personal Data Association, Personal Data Usefulness and Personal Data Identification. The findings reveal that participants were most engaged with data topics reflecting personal experiences, that they were able to develop situational interest in data in the program, and that their final data visualizations provided a medium for expressing social values and identity. Research limitations/implications This study’s findings may be context-specific due to the structured sequential nature of the data-art inquiry program. Future research could explore the PDR framework’s applicability in varied instructional designs and investigate interactions among the PDR dimensions more deeply. Practical implications Educators designing data science curricula should explicitly incorporate opportunities for youth to select personally relevant data topics, actively engage in data set exploration and reflect on the social implications of their data products to enhance data engagement and data science learning. Social implications Encouraging youth to find personal relevance in data can foster deeper engagement with data-based societal issues, which can promote informed and active participation in public discourse through data literacy. Originality/value This study introduces the PDR framework, providing a structured approach for analyzing and designing data science programs that emphasize youth’s personally meaningful connections with data. This study contributes uniquely to the field by explicitly linking personal relevance to interdisciplinary data-art inquiry contexts.

This systematic review examines data science education in U.S. informal learning environments through analysis of 20 studies. Our analysis reveals the landscape of this emerging field. Our findings highlight three critical dimensions shaping informal DSE: the methodological and theoretical diversity of the field; the interplay of people, practices, and places; and emerging design principles and pedagogical approaches. Effective programs integrate technical skills with critical perspectives, connect to personally meaningful contexts, and position learners as knowledge producers rather than consumers. We identify several promising approaches, including critical data literacies, personal data exploration, data storytelling, embodied learning, and youth positioning as data agents. Yet, implementation challenges persist: literacy barriers, data complexity, equity gaps between intentions and practice, and limited assessment frameworks constrain the field's ability to scale these innovations. Despite intentional efforts, notable gaps remain in rural, early childhood, and disability contexts. Critical approaches examining power and representation show promise for marginalized communities. Beyond technical recommendations, we argue for reconceptualizing data literacy toward collective sovereignty and assessment frameworks valuing transformative outcomes. Informal learning environments can serve not merely as preparation for existing data systems but as spaces for imagining and enacting more just alternatives that challenge power structures in data science education.

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from μ=3.9 (Challenge phase) to μ=4.6 (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education.

Driven by the digital age, the chemical and chemical engineering industry is gradually transforming towards green, refined and intelligent development, thus posing new demands on talents' data literacy, innovative thinking ,and interdisciplinary capabilities. Taking the Applied Chemistry major as an example, this paper explores the paths and methods of promoting educational reform and construction through digital and intelligent technologies. First, in the term of teaching modes, personalized and interactive teaching is realized through intelligent platforms and virtual simulation technologies. Second, in the practical system, a new paradigm of "virtual simulation - simulation calculation - physical verification" is constructed. Third, in the course content, new technological elements such as Python, chemoinformatics and artificial intelligence are integrated. Fourth, in the evaluation system, a multi-dimensional and full-process evaluation mechanism is built based on learning data analysis. Fifth, in the faculty construction, teachers are encouraged to transform into "mentors, designers and analysts". Sixth, in professional management, a data-driven decision-making mechanism is established. In conclusion, digital and intelligent technologies will drive the Applied Chemistry major to shift from cultivating traditional "chemists" to nurturing "composite talents in chemistry and data", providing important support for building a high-quality education system.

Building Artificial Intelligence and Data Literacy Across the Health Care Research Workforce in the Mayo Clinic Center for Clinical and Translational Science: Why It Matters and What We Are Doing About It.

Making data literacy accessible: A pilot study of academic library and community collaboration for citizen data literacy

Cross-sectional investigation on factors and improvement strategies for data literacy among pre-service teachers

Theory and practice in corporate finance education are often disconnected. Prior research on Problem-Based Learning (PBL) in finance holds some potential, but the evidence for the influence of experiential, data-driven tasks on students’ conceptual understanding and data literacy remains under investigated. The study adopted Kolb’s Experiential Learning Theory and analyzed a PBL project using 59 accounting students representing equity analysts at a Malaysian public university to implement the Efficient Market Hypothesis (EMH) on actual Bursa Malaysia event data. The study is qualitative in design. Descriptive statistics were used to summarise student endorsement of the project, and written reflections collected over five months in 2024 were analysed thematically. Two research questions guided the analysis: (1) how the project shapes students’ understanding of EMH and related corporate finance concepts; and (2) how it influences students’ data literacy, including their use of spreadsheets to analyse and interpret investment outcomes. Results demonstrate strong support for the intervention, with 91.5% of students recommending the project for future cohorts. Six themes were identified: authentic relevance, improved analytical skills and data-handling, theory-to-practice transfer, increased self-confidence and decision-making, stronger communication and collaboration, and perceived deficits in instructional scaffolding. Results show that a systematic interaction with real market data deepens conceptual understanding and enhances the competence of the quantitative evidence used, making visible the design features that can be improved. The study introduces an EMH-based PBL model for corporate finance modules and proposes design protocols for finance educators desiring to cultivate both technical proficiency and workplace skills through experiential techniques.

This study investigates the optimization of the educational evaluation system to support data-driven decision-making at MTs Negeri 1 Rejang Lebong. Using a qualitative case study approach, data were gathered through interviews, observations, and document analysis involving school leaders and teaching staff. The findings reveal that the integration of the CIPP evaluation model and Total Quality Management (TQM) principles enhances the quality and effectiveness of managerial decision-making. However, challenges such as limited data literacy and lack of structured reporting persist. The study recommends improving evaluator capacity, promoting data competence among educators, and utilizing digital tools to strengthen institutional decision-making processes.

Facing drastic changes in technological and knowledge landscapes, the agencies responsible for publishing and transmitting the data, information and knowledge of cultural heritage need to cope with them. The recent rapid spread of generative artificial intelligence into our daily lives has intensified the need for such responses. Anticipating knowledge landscape transformed by generative artificial intelligence technology, I suggest several tasks required in constructing archaeological digital archiving: establishing a system of digital data literacy; enhancing mechanisms for data evaluation and reuse; building an international data-sharing system; and developing archives into platforms for digital archaeology.

This article explores the growing need to understand what skills are required to navigate rapid technological change driven by digitalisation, datafication, and AI – both within organisations and education, and in citizens’ everyday lives. Through a narrative literature review of digital, data, and AI literacy, we analyse their defining components, synthesising these approaches into an integrated framework. Our findings position digital literacy as a foundational concept essential for understanding and engaging with both data and AI literacy. While digital literacy equips individuals with basic technological skills, data literacy and AI literacy require more specialised knowledge. Data literacy involves using data for decision-making and problem-solving, whereas AI literacy extends to understanding AI systems, their functions, and their social, ethical, and legal implications. The article identifies three interrelated dimensions across all literacies: technical, critical, and communicative-cognitive. While technical and critical dimensions are well-documented, the communicative-cognitive dimension, essential for interacting with and cognitively relating to technological resources, remains less explored. We argue that educational programs must prioritise technical, critical thinking, and communicative-cognitive skills to cultivate comprehensive digital, data, and AI literacy. Finally, we raise critical questions about how public sector officials practise these literacies and how they can be integrated into education and training across diverse organisations.

The consumer packaged goods and retail industries face unprecedented disruption as agile, digitally-native competitors capture significant market share through superior data utilization capabilities while established enterprises struggle with legacy infrastructure constraints. This article examines the strategic imperative of modern data stack adoption through a comprehensive analysis of two industry-leading transformations: a North American home improvement retailer's migration from on-premises data warehouse to cloud-native architecture, and a global beverage company's unified data platform spanning its North American bottling ecosystem. The article demonstrates that data modernization transcends mere technological upgrade, representing a fundamental strategic reorientation that transforms data from an operational byproduct into a competitive foundation. Through a detailed case study, the article is supported by contemporary research on cloud computing architectures, data integration methodologies, and organizational transformation frameworks. This work reveals critical success factors, including unified data architectures that eliminate fragmentation, secure ecosystem-level collaboration platforms that extend beyond organizational boundaries, and cultural transformation initiatives that cultivate data literacy and evidence-based decision-making capabilities. The article establishes that the primary value proposition of modernization lies not in cost reduction but in risk mitigation, eliminating opportunity costs, operational disruptions, and competitive disadvantages imposed by legacy constraints while enabling advanced artificial intelligence and machine learning applications impossible within traditional infrastructure paradigms. Findings emphasize that successful transformation requires comprehensive approaches addressing technology, processes, people, and organizational culture simultaneously, with modular architectures enabling incremental implementation while ecosystem-thinking facilitates collaborative value creation across multi-stakeholder networks. This framework provides strategic guidance for CPG and retail organizations navigating the transition from reactive, infrastructure-constrained operations toward agile, data-driven enterprises capable of sustained competitive relevance in increasingly volatile markets.

This article presents an Indigenous research methodology developed in the context of a case study to better understand data literacy within our local Indigenous Australian community. For too long Indigenous peoples have been the subjects, rather than the producers, of data-focused research. By adopting this approach in our case study, we identified practical guidelines for conducting best-practice Indigenous research in our own local community. Researchers must build genuine relationships with Indigenous Peoples and communities, understand community priorities and cultural protocols, maintain good Indigenous data governance and include Indigenous Peoples at all stages of research. This article also considers the positionality of the Aboriginal researchers in the study’s design, conduct and ethical considerations. Adopting these guidelines allowed for insights to be collected and interpreted through an Indigenous lens and ensured that the case study met the needs of and benefitted our local Indigenous Australian community.

This study, grounded in Carlson and Johnston’s Data Information Literacy (DIL) framework, assessed the data literacy skills of Sangguniang Kabataan (SK) leaders in selected local government units in Misamis Oriental across ten dimensions spanning data identification and acquisition; clarification, analysis, integration, and verification; and visualization, communication, evaluation, and ethicization. Employing a descriptive research design, the study was conducted in Balingasag, Misamis Oriental from June 2025 - October 2025. Data were collected from 73 currently serving SK officials aged 18–24 through purposive sampling using a structured Likert-scale questionnaire, and analyzed using descriptive statistics (mean and standard deviation). Overall, SK leaders reported high mean scores in data identification (M = 3.52) and data ethicization (M = 3.58), and in integrating data into community initiatives, but low mean scores in advanced data acquisition (M = 2.45) and only moderate skills in statistical analysis and data visualization. These findings indicate that while youth leaders value data and ethical practices, gaps remain in technical and applied competencies. The study highlights the need for targeted capacity-building programs, mentorship opportunities, improved access to digital tools, and continuous ethical reinforcement to strengthen data literacy and support evidence-based decision-making, participatory governance, accountability, and the effectiveness of youth-oriented community programs.

The increasing use of Artificial Intelligence (AI) in education presents new challenges, particularly in evaluating the credibility of AI-generated content. This study explores the attitudes and evaluative skills of 279 in-service primary and secondary teachers in assessing AI-generated information on urban heatwaves, as a climate-related issue. Using a quantitative pre-and-post design, data were collected through a digital questionnaire administered before and after a targeted training intervention. The instrument, informed by the CRAAP test, assessed teachers’ ability to critically appraise AI-generated content. Findings revealed initial concerns about students’ reliance on AI and difficulties in evaluating the validity and reliability of AI-generated data. Post-training results demonstrated marked improvements in participants’ evaluative competencies across all CRAAP dimensions, particularly in assessing the currency and relevance of AI-generated content. These outcomes underscore the effectiveness of targeted training and reinforce the ongoing need to strengthen digital and data literacy among educators.

Data literacy is an increasingly important skill in our data-driven world, and librarians and other information professionals can play a key role in creating a data literate population due to data literacy’s close association with information literacy. However, the definition of data literacy and the attention paid to certain competencies varies greatly between fields: what librarians and statisticians mean by “data literacy” is not the same thing. A scoping review of data literacy articles within the field of statistics education reveals the landscape of data literacy education in statistics, giving librarians and other information professionals a map for coordinating their data literacy work with disciplinary faculty. The areas of data discovery, evaluating and ensuring the quality of data and its sources, and reproducibility are closely examined. These areas are defined and valued inconsistently amongst information professionals and statisticians, but their close associations to traditional library services creates an ideal opportunity for libraries and data archives to contribute to data literacy education.

In a data-driven world, the ability to critically assess empirical data is a core aspect of scientific literacy and a major goal of physics education. Yet, students often fall back on heuristics that conflict with statistical reasoning. A common example is the “the more measurements, the better the quality of the data” (MMB) heuristic, which naively equates quantity with quality, ignoring variance. According to conceptual change theory, such heuristics or misconceptions coexist with formal scientific knowledge and are automatically triggered during reasoning, resulting in conflicts and requiring active suppression. Inhibitory control, an executive function that overrides automatic responses, may be the central key to resolving these conflicts; however, its role in procedural domains, such as data literacy, is understudied. This study examines whether and how inhibitory control facilitates students to overcome the MMB heuristic in different representational formats (tabular vs pictorial). Using a modified Stroop paradigm, undergraduate physics students ( N = 3 0 ) evaluated data quality presented in tabular (numerical tables) and pictorial (dot plots) formats across the congruent, incongruent, and neutral stimulus types. Results showed significant accuracy reductions in incongruent trials compared to both neutral and congruent trials across both formats, indicating significant Stroop effects. For the response times, we found a significant Stroop effect (longer response time in the incongruent than congruent trials) in the pictorial format, while a similar but non-significant trend in the tabular format. These findings first indicated the involvement of inhibitory control when the heuristic activation occurred in both formats. However, the pictorial format may evoke stronger or more persistent heuristic conflict, or the potential higher cognitive processing demands in the tabular format may mask the subtle RT-based Stroop effects. These findings extend the conceptual change research by identifying inhibitory control as crucial for resolving heuristic interference in procedural reasoning, and they stress the need to consider how representational formats modulate both heuristic activation and cognitive processing demands when designing instruction in physics.

The use of Pedagogical Digital Literacy (PDL) enhances the teaching of modern educators, with the integration of digital competency in Sindh, Pakistan. In developing countries, digital competency is often ineffective. Understanding the gap, the current study identifies the impact of PDL core competencies on the pre-service teachers’ training efficiency in Karachi, Pakistan. The four key competencies are such as, Information & Data Literacy, Communication & Collaboration, Digital Content Creation, and Safety & Well Being. The study used a cross-sectional survey research design, where, based on a simple random sampling technique, 200 prospective teachers were selected for the distribution of the adapted research questionnaire on a five-point Likert scale. The findings indicate that these four core digital competencies significantly predict the Pedagogical Digital Literacy among the prospective teachers in the universities of Sindh, Pakistan. Contrary to that, the influence varies, the communication & collaboration strongly predict PDL of the prospective teachers with p value < 0.05, and Beta at 0.727, and digital content creation significant but slightly weaker predictor to PDL of the prospective teachers with Beta 0.285 and p value < 0.001. These results are evident to provide directives for the teacher education to reform the curricula in context to digital communication, collaboration skills, and competencies, developing a more effective teaching model for the future teachers of the education sector in Sindh, Pakistan. Indicating that future researchers are suggested to work on digital content creation, contextual barriers the access to digital resources, training in self-efficacy to pedagogical approaches, and sustainable development to address the resource-constrained situations in the higher education classrooms and practices.

“Data cultures in human resources management in SMBs in North-Rhine Westphalia” is the title of the author's current project, which is researching the opportunities and challenges of digital transformation in small and medium-sized companies. For this purpose, interviews were conducted with human resources managers. In light of findings from work and organizational sociology, the following basic thesis of this article emerges: Human resources management and employees in SMBs alike will produce completely new data cultures during digital transformation, which they will have to “manage”. Future skills of (digitalized) workers include data literacy, which includes dealing with data (including personalized data) as well as a new perspective on errors, the ability to recognize algorithmic (pre-) decisions, the willingness to take on one's own leadership responsibility. and the critical and reflexive handling of data in general – not just one's own.

This study aims to analyze the fundamental concepts, principles, and implementation challenges of educational planning through a systematic literature review. Employing qualitative methods with a library research design, this study synthesized 25 academic sources, including journals, textbooks, and policy documents published within the last decade. The analysis reveals that educational planning encompasses systematic processes of needs analysis, goal formulation, priority setting, implementation, and evaluation. Three major approaches—social demand, manpower needs, and rate of return—emphasize equity, relevance, and efficiency, respectively. Four critical aspects (quantitative, qualitative, relevance, and efficiency) must be comprehensively integrated throughout the planning stages. The findings indicate substantial implementation challenges, including limited managerial capacity, insufficient data literacy, weak stakeholder participation, and resource constraints. School Work Plans (RKS/M) often remain administrative documents rather than strategic instruments for institutional improvement. Regional disparities in planning capacity further exacerbate educational inequity between urban and rural areas. Significant gaps persist between theoretical concepts and practical implementation, where planning becomes a mechanistic administrative task rather than a strategic management instrument. This study concludes that effective educational planning requires paradigm transformation toward participatory, evidence-based approaches supported by enhanced capacity building, systematic evaluation mechanisms, and synergistic coordination among central government, regional authorities, and educational institutions to ensure sustainable and equitable national education development.