IASE organises yearly diverse kinds of conferences of different sizes and scopes, and sponsors other events and collaborations, in order to respond to the diverse needs of the dynamic field of statistics education. Thus, every year you can find somewhere around the world a relevant gathering with a focus on statistics education.
International Association for Statistical Education
How the level of inference in visualizations influences participants’ performance in Bayesian reasoning
We conducted a study with 2,400 participants that had to solve six Bayesian reasoning tasks in one out of eight different visualization types (no visualization, regular 2×2 table, graphical 2×2 table, unit square, implicit tree diagram, explicit tree diagram, double tree, net diagram) in a probability, proportion or frequency format. The aim of the study was to investigate whether the levels of inference have an influence on the participants’ performance in the tasks. The level of inference is characterized by the number of mental steps that are needed to arrive at the correct solution, which vary between the visualization types. The results show that the levels of inference indeed influence performance. This can be used to teach students to adaptively and flexibly use probabilistic visualizations for different types of tasks.
“How is this possible?” - Secondary PSTs’ conceptions of variability in the context of probabilistic thinking
Abstract: In the past, the consideration of (data) variability has been repeatedly characterized as a fundamental element of statistical thinking. However, motivated by recent curricular changes in Austrian secondary schools, this study situates the concept within the context of individual ideas about probabilities in random experiments. A thematic analysis of pre-service teachers’ answers to a pen-and- paper study in October 2024 gives insights into how variability is conceptualized within the interplay of patterns and deviations in the context of the empirical law of large numbers at the beginning of their tertiary stochastics training.
The integration of probability-based arguments in risk-related contexts
Probabilistic reasoning is key for decision making, especially in risk-related contexts. For example, erroneous HIV self-test results pose risks that must be evaluated when considering public approval. This requires incorporating probabilities (e.g., for not being infected even though a positive test result is given) into the decision-making process. We examine how upper secondary school students use such probability-based arguments in risk-related contexts before and after an intervention on conditional probabilities. A qualitative content analysis classifies their arguments as (i) mathematical, (ii) context- related, (iii) transitional (between mathematical and context-related), (iv) affective, or (v) based on anticipated personal experience. A central result of the analysis is that after the intervention, students used mathematical arguments more frequently than prior to the intervention on conditional probabilities.
Designing and implementing data lessons in secondary education
This study examines how secondary teachers incorporated the six-phase Data Investigation Process (Lee et al., 2022) into classroom lessons following a professional learning experience. Analysis of 13 lesson plans, interviews, and survey responses revealed that while most lessons addressed multiple phases, few supported full engagement across all six. Framing the Problem was the most consistently attended-to phase, often grounded in authentic contexts and clear investigative questions. In contrast, Explore and Visualize was less developed, frequently limited by specific learning goals constraints. Lessons that integrated technology, especially CODAP, were more likely to support deeper student exploration and reasoning. Findings underscore the need for thoughtful instructional design.
Expansion of the data moves framework to support data processing, analysis and modeling
Erickson et al. (2019) defined a data move as an action that alters a dataset’s contents, structure, or values. They also proposed a framework of six data moves from their work with CODAP. We have expanded their framework through reflection on the use of data moves by ourselves and teachers. Our framework has added new data moves, restructured the relationship of data moves, and added purposes for each data move. We will explicate our expanded framework and how it supports data processing, analysis, and modeling through exploration of a dataset involving recent adult obesity levels in Western Pacific and South-East Asian Countries. We will also discuss how teachers’ intentional use of the framework can enhance students’ understanding of data science.
The role of dotplots in statistics education practice
Statistical graphs play a crucial role in data literacy, yet pupils frequently misinterpret histograms. In education, textbooks typically progress from case-value plots to histograms but research suggests that dotplots and their variants (e.g., histodots) can support this transition. The study asked: How and how often are dotplots and their variants used in Dutch textbooks? An analysis of 19 Dutch textbooks (Grades 7–10) showed that dotplots are rare and not used to support transitions to histograms or boxplots. Variants like hatplots and histodots were absent. Hence, a gap is apparent between research on learning progressions in statistics education and the Dutch curriculum as implemented in textbooks, which teachers often closely follow. Although the reasons for this limited uptake of research remain speculative, possible explanations include a lack of exemplary large-scale or quasi-experimental studies and a shortage of exemplary, classroom-tested materials to support implementation.
Modeling equity using multiple technologies for teaching statistics with preservice teachers
Mathematical knowledge for teaching requires strong content knowledge, yet statistical content knowledge is often overlooked in elementary teacher education. Many elementary preservice teachers (PSTs) struggle with core statistical concepts like mean and median, and even those with strong content knowledge may lack effective teaching strategies. This study describes the design and implementation of a statistics lesson aimed at promoting equitable learning experiences using technologies such as GeoGebra and Padlet. Data collected included the mathematical work and reflections of 27 PSTs, video recordings of the lesson, and observers’ field notes; all were analyzed using qualitative methods. Analysis showed that technology-supported statistics learning encouraged understanding of statistical concepts for PSTs as learners, and that reflecting on pedagogical strategies used in the lesson allowed PSTs to analyze the lesson as future teachers. These findings underscore the need for teacher preparation courses that emphasize both content and pedagogy in teaching statistics.
Interactive oral assessment for statistical literacy
In the quest to distinguish fact from fiction in a world awash with misinformation and disinformation, statistical literacy is more crucial than ever. One of the key characteristics of a statistically literate citizen is their ability to communicate and justify their reaction to data-based information encountered in various contexts. An interactive oral assessment (IOA) can be a valuable and authentic way of assessing these skills. IOAs involve genuine, unscripted conversations, facilitating opportunities for an instructor to query responses to evaluate students’ understanding more fully and allowing students flexibility in demonstrating their knowledge. IOAs are also less prone to academic misconduct than their more traditional written or online counterparts. This paper presents the rationale for introducing an IOA in an introductory statistical literacy course, outlines the implementation process, and shares reflections from course instructors alongside preliminary student feedback.
Branching out data science education: Developing task and computational environment design principles for teaching data science at the high school level through an international research collaboration
Data science is rapidly emerging as a desired component of education, yet generalisable design principles for tasks and computational environments remain underdeveloped. This paper reports on a design-based research collaboration between Germany and New Zealand, which aims to develop and refine such design principles for high school data science education. Drawing on literature and prior research, we propose that the design of tasks and computational environments should integrate humanistic, algorithmic, and programming approaches. The three key design “branches” of our research are: (1) emphasising a humanistic focus through data construction and exploration; (2) using decision trees to develop algorithmic modelling concepts; (3) using computer programming for gaining insights, supporting creativity, and model tinkering. These branches are illustrated through examples from our collaborative work. Our research contributes to the development of effective educational strategies for data science education and could inform the development of professional development for high school teachers.
Project-based learning in life sciences statistics courses: Dynamics, pitfalls, and educational gains
This study explores how project-based learning (PBL) enhances statistics education for students in the two departments—biotechnology and medical laboratory sciences. Semester-long practical projects enable students to simulate real-world data analysis and develop research skills by selecting topics, building databases, reviewing literature, applying various methods using analytical software, interpreting results, and drawing conclusions. MedLab students, who take the course early in their studies, express strong motivation to take initiative and work independently throughout the project. In contrast. Biotechnology students, who are at a more advanced stage in their academic studies, rate communication with peers and lecturer very high. The study draws on two surveys: a Likert-scale questionnaire completed by 64 students to assess PBL experiences, and an open-ended survey of 27 students analyzed using natural language processing to identify sentiment and themes. Findings reveal key differences and similarities, informing how PBL can be tailored to enhance learning in STEM education.
Enhancing teachers’ TPACK for data science in STEAM education: Insights from a pilot professional development program in Cyprus
In today’s data-driven world, education systems face growing pressure to equip students with data science literacy and critical thinking skills. While STEAM education offers a natural interdisciplinary setting for fostering these competencies, teachers often lack the confidence, training, and pedagogical strategies needed to integrate data science into their practice. The EU-funded DataScEd4CiEn project responds to this challenge by designing and implementing a Professional Development (PD) program tailored mostly for STEAM educators. This paper presents an exploratory study of a pilot implementation in Cyprus, aimed at investigating how the program supports teachers in adopting data- driven, interdisciplinary approaches. Using a Design-Based Research (DBR) methodology, the study iteratively refines the PD model while collecting mixed-methods data to examine changes in teacher knowledge, instructional practices, and collaborative engagement. Preliminary findings highlight the transformative potential of targeted PD in advancing data science integration and empowering teachers to meet the evolving demands of 21st-century education.
An initial exploration of teacher integration of interactive technology in statistics education
Our study explored how secondary mathematics teachers integrate the DOTS applet, a digital tool designed to support distributional reasoning, into statistics tasks. Three pre-service and in-service teachers participated in a retrospective qualitative case study, where they created and reflected on tasks that integrated the applet. Qualitative content analysis of the teachers’ written submissions focused on technology integration and the development of distributional reasoning. Results revealed both similarities, such as promoting interactive engagement, and differences, including the use of context, conceptual focus, and the balance between exploration and guidance. These findings highlight that the same technology can be used in diverse ways to support student learning, emphasizing the importance of intentional task design aligned with specific learning objectives. This research contributes to STEAM education by providing indications of how teachers’ pedagogical intentions shape the integration of technology in statistics education.
Videos as formative and summative assessment of students’ learning in civic statistics
Formative and summative assessments provide essential feedback to both teachers and learners, highlighting what is valued in the learning process and measuring the extent of student progress. In this study, we analyze 37 short videos created by teams of two or three students in a Civic Statistics course. Each video follows a structured script that includes: introducing a hot sociopolitical topic, describing the structure and origin of a rich dataset, presenting data visualizations and analyses leading to well- founded conclusions, and offering a critical review. The video analysis provides insights into students’ abilities to analyze data using suitable graphical presentations and data moves, as well as their capacity to connect statistical findings with sociopolitical contexts.
The use of digital components in an undergraduate statistics course
In a research project, the Economic and Social Statistics Department of Trier University was able to get an insight into the use of screencasts and e-tutorials in an undergraduate statistics course. The empirical data show that students make ample use of the flexibility those tools offer. We also see that active exercises are very important in terms of exam preparation. The participation rates and the tenacity in working exercise problems can be improved. A natural next step would be the implementation of adaptive e-learning scenarios.
Design of statistical projects at the school level, advances and challenges when engaging in the statistical problem-solving process
This paper presents the results of a design-based research study aimed at fostering 8th-grade students’ Statistical Thinking through contextualized investigations. Drawing on GAISE II recommendations for novice levels (A and B) and the Statistical Working Spaces framework, two statistical projects were designed using local orange production as context. Findings indicate that students made significant progress in anticipating variability, organizing data, and applying measures of central tendency and variability. Nevertheless, difficulties were observed when formulating comparative questions, understanding concepts such as sample and variable, and interpreting graphical representations like dot plots and histograms.
Data collection as a catalyst for data literacy: A concept for building Smart City gadgets at school
In a data-driven world, fostering data literacy among students is essential - yet the sub-skill of data collection remains underrepresented in educational practice. This study explores how personal data collection using environmental sensors within a ‘Smart City’ project influences students’ ability to critically evaluate corrupted datasets. In a quasi-experimental design, 79 high school students participated in an intervention, during which they developed sensor-based Smart City projects and collected their own data. A pre- and posttest measured their ability to identify outliers in a corrupted dataset. The results indicate an improvement in the experimental group compared to a control group that worked with externally sourced data. Findings suggest that engaging with personally collected data enhances students’ understanding of data quality and supports the development of critical data reasoning skills. The study underscores the educational value of authentic data practices and calls for further research into their long-term curricular integration.
Preservice teachers’ exploration of multivariate data based on personal interests
The DataSETUP project addresses the critical need for data science competencies in teacher education by developing short, modular courses for preservice teachers. Based on the DataSETUP framework, these modules guide future teachers through key data science processes, including data exploration, problem formulation, modeling, and results communication. In a pilot study, preservice primary teachers used a real-world dataset to explore digital gaming habits of young people. Findings show that students effectively formulated statistical questions and created visualizations to answer them. However, they tended to select their own topics and struggled to connect their analyses to the module’s thematic context, highlighting both the motivational potential and the challenges of working with large, multivariate datasets.
Project work for a large class - Authentic assessment
Improving graduate employability is increasingly essential due to evolving industry demands. Through experiencing the data investigative workflow comprising framing research questions, implementing data analysis and communicating results, students develop key employability skills such as organisational and time management skills. Large statistics classes pose challenges for fostering engagement and assessing practical skills authentically. This paper presents a scalable approach using authentic data for a project assessment that enables students to make decisions for their data investigative workflow. Examples of student work indicate that project-based assessment deepens understanding and strengthens connections between theory and professional practice. These experiences help build critical thinking and communication skills which are vital for today’s job market. The paper concludes with implications for assessment design and acknowledges current limitations, offering a roadmap for educators aiming to bridge classroom learning and employability through authentic experiences.
Core ideas for teaching hypothesis testing – Structure, concepts and validation
Hypothesis testing is often perceived as an inaccessible and difficult to teach topic in school education, partly due to weak curricular connections across lower and upper secondary levels. The complexity of key statistical concepts further contributes to this challenge. Additionally, instruction often emphasizes computational procedures over conceptual understanding and reasoning. To address these issues, we propose a spiral curriculum that utilizes core ideas as conceptual anchors to support early informal understanding and facilitate the transition to more formal understanding of hypothesis testing. Four preliminary core ideas were theoretically derived and validated through an expert survey. Initial findings confirm the relevance of the core ideas while highlighting the need to modify the core ideas' content and to adjust the relationships among them. This underscores the importance of further refinement of the theoretical framework prior to classroom implementation and lays the foundation for developing learning environments within a design-based research.
A design research project on fairness in data-driven algorithmic decision-making
Data-driven algorithmic decision-making systems, including many AI technologies, are ubiquitous in today’s society. Early engagement with these systems and the promotion of critical statistical literacy are therefore essential. This involves not only fostering a basic understanding of how such systems work, but also addressing their broader social impact. As part of a design research project, we developed a learning activity that enables upper secondary students and pre-service teachers to explore issues of fairness in the context of automated credit granting. In this paper, we present the design of the activity, outline the intended learning trajectory, and report on initial implementations with pre-service teachers. We also discuss preliminary findings from a qualitative analysis of students’ fairness-related arguments.
Effects of a simulation-based training on students conceptual understanding of the Binomial test
Significance tests are used intensively in quantitative empirical research and are also taught at schools and universities. However, even experts and statistics lecturers are subject to misconceptions when interpreting significance tests. Our study focuses on the binomial test, and examines the extent to which a refresher course that focuses on a simulation-based approach (using CODAP) is more conducive to learning than typical education on the binomial test that focuses on calculations. While the conceptual knowledge in the experimental group with simulations improved slightly more than in the control group, the students in the control group showed more improvement in procedural knowledge. However, the pre-test performance was weak overall and only a slight increase was observed in both groups after the 100 minutes of training. A comprehensive development of understanding hypothesis testing is important in teaching, and the results suggest that this cannot be sufficiently improved by our short training session.
Fairness in machine learning: A design research study for secondary education
This conceptual paper presents a design research study that explores fairness in machine learning (ML) as an interdisciplinary learning opportunity for secondary education. Drawing on the societal relevance of algorithmic decision-making systems, the study emphasizes the importance of integrating technical and ethical perspectives within a cohesive teaching-learning arrangement – an approach that is still rarely implemented in practice. The paper provides an overview of fairness definitions and algorithmic intervention strategies, alongside a review of relevant educational research on ML and fairness in both school and higher education contexts. It outlines the methodological foundations of the design research study and introduces preliminary ideas for a prototypical teaching-learning arrangement, accompanied by guiding research questions that frame the study.
Designing online professional learning to support advances in teaching strategies in statistics and data science
This paper describes the design of an innovative online platform that has over 50 hours of learning experiences to support educators in further advancing their understandings and pedagogical skills in teaching statistics and data science to learners age 11-18+. Two frameworks are described that support effective classroom practices: a Data Investigation Process and Seven Dimensions of Teaching Statistics and Data Science. Examples of features and interface designs are included to illustrate how the frameworks work together to support teachers' learning in a cohesive way, even as they personalize their experience and engage in different learning pathways.
Statistical tools to assess environmental risks
Young people are increasingly aware of the climate crisis, witnessing its effects and actively engaging in discussions and actions towards sustainable behavior. The purpose of the project EduS4EL has been to foster basic competencies of youngsters for critically assessing environmental risks based on reliable data. Specific analytical skills to be developed include risk assessment, data analysis, logical reasoning, and a foundational understanding of environmental issues. This paper explores a set of technological tools designed to help high school students develop these skills through targeted educational interventions focused on experience-based learning.
Designing for fusion in statistics education: A variation theory approach
This paper presents a design approach for teaching statistics in STEAM education, grounded in variation theory and enriched by real-world contexts drawn from Education for Sustainable Development (ESD). Variation theory serves as the central theoretical framework, offering a way to support learners in discerning critical aspects of the object of learning through structured variation. A key feature of variation theory is the coordination of part–whole relationships, enabling students to learn statistics not as a set of fragmented procedures, but as an integrated and coherent whole. Within this framework, we suggest that sustainability-related problems provide powerful contexts for supporting the development of statistical reasoning, particularly when aligned with the interdisciplinary goals of STEAM education. The paper outlines how these elements can be brought together in a coherent instructional design to foster purposeful, conceptually grounded learning in statistics.
From supervised to unsupervised learning - Structuring the core components of understanding
With the increasing public relevance of machine learning, the need for corresponding educational opportunities is also growing. While theoretical concepts for structuring learning content already exist for supervised learning, there is no comparable basis for unsupervised learning. This paper examines the extent to which the so-called model concept, a theoretical model to structure the core components of understanding of supervised learning, can be transferred to unsupervised learning. Using the example of k-means cluster analysis, it is shown that the basic structure of the model concept, the so-called facets, is largely transferable, even if individual core components of understanding need to be re-differentiated in terms of content. The results provide a theoretical basis for the development of learning objectives and teaching materials for unsupervised learning and open up further questions regarding the implementation and empirical validation of the proposed structure.
Early statistics: Introducing informal inferential reasoning through storytelling-based learning task in grades 1-3
This study presents part of a hypothetical learning trajectory designed to introduce inferential reasoning, support, and expand emerging understanding of statistical concepts in grades 1 to 3 (n=59). Through a design-based research approach, a playful activity was designed and implemented based on a pirate story involving gold pearls, allowing children to construct, analyze, and refine data models in scenarios of uncertainty. Students worked in teams with assigned roles (counter, recorder, and shaker) to collect and record data. In whole-class dialogues guided by the teacher, students completed three dot plots, visualized sample variability, and made inferences about the distribution of the pearls. The results show that children can develop key notions such as randomness, empirical sample variation, and maximum sample frequency by interacting with data and comparisons of groups. This study provides evidence of how the children complete data modeling tasks, articulate mathematical and statistical concepts, and make informal inferences.
Looking forward: Reflecting on the present, envisioning the future of statistics and data science education
In the spirit of the conference emphasis on the critical role of statistics and data science in shaping education across all STEAM disciplines and all educational levels, the conference concluded with a panel session encouraging and eliciting reflection, discussion and prompting views of current and future challenges and opportunities. Three invited panellists, representing a range of international and statistical contexts, were asked to give presentations reflecting their individual experiences and expertises with reference to a theme, followed by comments guided by questions put to them before the conference.
Innovating mobile learning in statistics: Profiling university students' needs and expectations
This study explores university students’ profiles and expectations for the use of a mobile application to support statistics learning. Using a mixed-methods approach, 440 undergraduates participated in a survey combining quantitative Likert-scale responses and over 200 qualitative open-ended answers. A factor analysis revealed four key dimensions shaping students’ perceptions toward mobile-assisted learning, while cluster analysis identified three distinctive user profiles with varying levels of engagement. Topic modeling of qualitative responses revealed critical preferences and concerns, such as the need for real-data applications, interactive tools, gamification features, and intuitive design. Concerns included excessive advertising, poor interface usability, and lack of personalized feedback. These findings offer a framework for developing adaptable, student-centered mobile applications in statistics education, aligned with learner diversity and pedagogical goals.
Investigating links between student awareness and use of generative AI tools, and student anxiety and confidence when studying statistics
Statistics anxiety is common amongst Higher Education students, with an estimated 70–90% experiencing anxiety while studying statistics. The emergence of Generative AI (GenAI) allows for new possibilities for how students might support their learning of statistics. In this international, multi- institutional study, we consider links between student awareness, perceptions, and usage of GenAI with student anxiety and confidence while studying statistics. Data were collected from students at universities across the UK and Australia during 2024 and 2025. Findings suggest that while GenAI usage is modest, it is more common among postgraduate students, and using GenAI tools can have a positive impact by reducing student anxiety and improving confidence while studying statistics. This study informs GenAI-driven pedagogy in higher education by highlighting the value of GenAI tools in supporting student learning of statistics and reducing student anxiety while studying statistics.
It’s in the wind: An opportunity for understanding epidemiological evidence
The government in England in 2024 initiated a wide-ranging review of curriculum and assessment across all subjects and ages. There is a broad consensus in the UK that data education should be across multiple subject areas. Ridgway (2022) sets out the rationale for Civic Statistics as an important element of the curriculum. We will review the literature on how epidemiology has been used recently in various innovative curricula. The drive for the introduction of Civic Statistics in curriculum reflects the public need to better understand information about the state of society. Epidemiology is inherently important in this context, as mis-information and dis-information about Covid-19, other diseases and vaccinations in general, has been increasingly problematic in an age where social media is the predominant source of information for many people, especially in the younger generations. Epidemiology offers a context in which the interplay between policy and evidence is immediate and transparent.
Data-based argumentation tasks in the context of climate change and primary students’ reported self-efficacy growth in climate change-related discourse participation
Education for sustainable development should enable citizens to take part in social discourses and decision-making through argumentation in favour of the protection of resources. As misinterpretations of statistical data are often part of sustainability-related discourses, the mathematics classroom should empower future citizens by building up competence in data-based argumentation as well as fostering self-efficacy necessary for responding to misinterpretations of data and actively participating in sustainability-related discourses. However, empirical research about the role of data-based argumentation for self-efficacy in taking part in climate-related discourses is scarce, especially for young learners. This study responds to this research need: 86 primary students took part in a survey in which they were asked to comment on incorrect interpretations of climate-related data and to justify their answers. These stimuli showed impacts on students’ reported self-efficacy growth with respect to argumentational participation in climate change-related discourse.
Theatre of statistics: A systematic review of performative approaches for enhancing statistical literacy
This paper investigates how theatre and drama can enhance statistical literacy by transforming abstract concepts into embodied and participatory learning experiences. Through a systematic integrative review of 93 peer-reviewed studies (2010–2024), we map drama and theatre-based strategies and formats adopted in science, literacy, and arts education. Though rarely applied in statistics, these approaches enhance understanding, engagement, and reflection. Building on these insights, we introduce the Theatre of Statistics framework, which integrates classroom drama and public science theatre into a unified model. Drawing on the tradition of science theatre, it aims not only to teach, but also to inspire wonder, curiosity, and long-lasting emotional connections with statistical thinking. In this way, it advances inclusive, multimodal participatory approaches that bridge education and public dissemination of statistics in a digitally connected world.
Civic statistics in teacher education - Effects among future math and civic teachers
This paper presents findings from an interdisciplinary seminar for pre-service mathematics and civics teachers aimed at fostering civic statistical literacy. Based on the concept of civic statistics, the course integrated statistical learning with critical engagement with data in social and political contexts. Pre- and post-survey data were used to compare baseline differences between groups, assess learning outcomes, and explore how interdisciplinary learning develops through engagement across disciplinary boundaries.
Playful experiential approach to reasoning with evidence
There has been a resurgence of interest in experiential learning in Higher Education Institutions (HEIs), driven by the employability agenda and the move to build closer links between HEIs and the outside world. Within this context, the concept of ‘play’ in education emerges as a form of open learning that encourages learners to define their own goals and outcomes. Whilst it is acknowledged that within statistics and data science education, learners will still need guidance and frameworks within which to engage with and interpret data, incorporating elements of play creates opportunities for deeper engagement and understanding. Here, insights from research in playful methodologies, e.g. LEGO® Serious Play® and Clowning are applied to the domain of Reasoning with Evidence. Current, mainstream materials are viewed through the lens of playfulness, and playful design principles are presented through their application to the development of a novel interface for displaying complex multivariate data.
Quantifying the benefits to students of faking your fluency in English in recorded lectures
Heavily accented speech can hinder learning, particularly for non-English speaking students who struggle with comprehension and engagement. This study explores whether AI-generated voice-modded lectures, mimicking native English speech, improve academic outcomes and engagement in lectures by non-native English-speaking instructors. The study was conducted in an Introductory Statistics large enrolment service unit and compared student performance across original and AI-enhanced lectures. Surveys were used to collect engagement feedback from both domestic and international students. No significant performance difference was observed for international students. However, domestic students showed improved marks when exposed to synthetic lectures, highlighting this intervention's low-cost, scalable impact. This research offers practical insights into using AI voice-modding to improve engagement and equity in large, diverse classes.
Pre-service teachers’ use of eye-tracking data to diagnose students’ misinterpretations in statistical graphs
This study explores how a pre-service mathematics teacher interprets eye-tracking data to diagnose students’ reasoning when working with histograms. Using a qualitative case study design, the participant engaged with an eye-tracking vignette, featuring a student’s gaze plot, answer, and cued recall explanation. The targeted error was frequency–value confusion, a common systematic error where students treat bar heights as measured values. Findings show that the participant initially relied on assumptions rather than the gaze data itself. Over time, her reasoning shifted to a more reflective analysis, integrating multiple data sources to better understand the student’s thought process. She also identified the limitations of interpreting gaze data in isolation, especially when no clear pattern was evident. This study suggests that the use of eye-tracking data, when scaffolded through structured tasks, can enhance pre-service teachers’ diagnostic skills in the interpretation of statistical graphs.
Can code be responsible? - How to study and communicate algorithmic accountability
Before we can trust algorithms, we must learn how to question and understand them. Based on this principle, we explore how algorithmic accountability can be taught through interdisciplinary, project- based learning as a part of the Digital Humanities Ruhr project. We present a seminar involving students of journalism, statistics, computer science, and data science. In interdisciplinary teams, they investigated real-world algorithmic systems and presented their findings in accessible public formats. The seminar demonstrates how algorithmic accountability can become an integral part of interdisciplinary education in the humanities and beyond. To accomplish this, we reflect on lessons learned and outline future improvements, such as expanding participation and enhancing mentoring to support both the selection of algorithmic cases and the choice of communicative formats. We integrate our improved seminar into the broader Data Literacy Certificate at TU Dortmund University, contributing to the development of micro-credentials in Digital Humanities across the University Alliance Ruhr.
Civic statistical literacy for democratic education
Democracy depends on public reasoning grounded in evidence. For democratic societies to thrive, citizens must be able to understand socially relevant data, assess the credibility of sources, and participate in informed public debate and decision-making. In the contemporary information environment, these capacities are increasingly challenged by misinformation, selective availability of data, and unequal access to information. This paper argues that civic statistical literacy—understood as statistical literacy oriented toward democratic participation—requires more than technical skills. It highlights critical evaluation of data quality, source credibility, and contextual interpretation as core competencies. Beyond an individual perspective, the paper examines how structural conditions—such as access to data, data ownership, and the organization of information ecosystems—shape democratic culture and constrain or enable civic participation. We propose a conceptual framing of civic statistical literacy that connects individual competencies with societal and institutional dimensions of democracy, and discuss implications for democratic education.
Data are everywhere – In every class and even outside of school
This paper aims to expand our ideas of where to find data beyond the “usual places,” i.e. math, statistics and possibly science classes. It encourages readers to develop a “data lens” in order to see data everywhere. In the paper, I share the characteristics of a “data lens” and provide several examples of data activities for students of all ages, including museums, zoos, aquariums and after-school clubs.
Fostering data literacy by engaging in data cleaning
The increasing societal relevance of data-driven technologies highlights the importance of fostering data literacy in education. One important part is data cleaning, which plays a crucial role in data- driven technologies and offers authentic opportunities to foster data literacy through critical engagement with real-world data. Despite its mathematical richness, data cleaning – particularly outlier detection – remains underrepresented in school curricula and educational research. This paper presents a design-based research project focusing on the mathematical foundations of outlier detection methods. Using the four-level approach by Hußmann and Prediger (2016), we specify and structure the mathematical topic of boxplots for outlier detection. We explore how these concepts can be meaningfully embedded in intended learning trajectories to promote students’ understanding of variability, robustness, and the impact of assumptions. The material is based on real datasets and aims to support critical reflection on data-driven decision-making.
A deep dive into statistical, data, and digital literacies: A bibliometric analysis (2000–2024)
This study aims to explore the field of statistical literacy in relation to quantitative, data, and digital literacy, highlighting their interconnected development and significance, to better understand how STEAM educators and researchers contribute to advancing these essential skills. Scientific literature indexed in Web of Science from 2000 to 2024 (n = 674 articles and books) is analyzed using co- occurrence analysis in VOSviewer. Five interrelated key trends are discovered: Data Literacy, Statistical Applications for Modeling and Prediction, Statistics Education, Mathematics, and Quantitative skills, Statistical and Quantitative Literacies and Digital Literacy. The findings highlight the growing relevance of statistical, data, and digital literacy, as well as the need of an effective integration of these literacies for education, research, and professional practice in STEAM disciplines.
Statistical literacy in the age of disinformation
Persuasive speeches aim to influence audiences to adopt specific viewpoints, support causes, or endorse political candidates. In the political arena, media discourse disseminates rhetorical strategies designed to elicit identification, appeal to emotions, and strengthen arguments. This article reports an investigation into how the integration of media, Mathematics, and Statistical Education can foster students’ critical analysis of persuasive narratives in the classroom. The study was conducted with seven high school students (aged 17–18) from a public school in Brazil, using participant observation. Data were generated through media news reports and information from the MapBiomas platform addressing deforestation in Brazil, and analyzed based on students’ written records and audio recordings. The findings indicate that engaging with news and statistical data promotes critical awareness of deforestation, environmental issues, and social inequality, highlighting the potential of Mathematics and Statistics for educating reflective and critically informed citizens.
Critical statistics education: High school students’ experiences in statistical thinking within the context of brain drain
This study explores how high school students engage in critical statistical reasoning through a real- world inquiry on brain drain. Drawing on critical mathematics education, critical statistics education, and the critical statistical literacy habits of mind (CSLHM) framework, the study integrates social context into a statistics lesson to foster critical awareness and data-informed thinking. The research involved 90 ninth-grade students from three public schools. Working in small groups, students formulated statistical questions, collected or interpreted data, and presented findings through posters. Data sources included worksheets, audio recordings, and student-created artifacts. Thematic analysis based on CSLHM dimensions revealed students' abilities to question data limitations, interpret findings critically, and propose socially grounded solutions. Preliminary findings show the potential of context- rich, inquiry-based statistics instruction to support both statistical thinking and social agency. The study highlights how data literacy can serve as a tool for engaging students in civic reasoning and transformation.
Conciliating statistical and language literacy skills for the development of critical thinking and communication: Students stand up for the Brazilian biomes
Brazil’s National Curriculum determines that statistics be taught from very early education. Some of the skills expected from 12-year-old students are (i) interpreting data from media, (ii) identifying graph elements, and (iii) writing texts based on statistical data. This paper describes a project from the National School of Statistical Sciences (Ence/IBGE) to this public. Students had to use statistical data to make a public claim. They experienced a simulated Conference of the Parties, focusing on the fires that devastated Brazilian biomes in 2024, and had to fight for aid funds. School teachers and students were deeply engaged, and students had an impressive performance as they experienced criticizing disinformation, curating quality sources, organizing data in bar graphs, and finally stood up for their chosen biomes using arguments and statistical data. Articulating both literacies showed that students experiment engagement and pride in presenting compelling arguments based on contextual knowledge and statistical data.
Integrating data science education and responsible AI in teacher training: A pilot study within STEAM-based graduate learning
This pilot study explores the integration of data science and responsible AI within a STEAM-based graduate course for education majors, developed as part of the EU-funded DataSETUP project. Focusing on a single module—Responsible AI & Data Science: Ethics, Society, and Citizenship—the research examines a group-based activity in which student teachers collaboratively built an image classification model using Google’s Teachable Machine. Participants engaged with core elements of the machine learning pipeline and reflected on issues such as data quality, algorithmic bias, and educational relevance. To analyze student learning, a five-dimension analytical framework was developed, synthesizing concepts from data science, statistics education, and critical data literacy. The framework captures both technical engagement and student teachers’ evolving understanding of the social and ethical implications of data-driven AI decision-making. Findings indicate that focused, hands-on experiences within a targeted module can meaningfully enhance student teachers’ data science literacy and their critical awareness of responsible AI use in education.
Advancing critical data science and statistical literacy for democratic and civic education
In an increasingly data-driven world, the integration of critical data literacy and statistical reasoning into civic education has emerged as a vital component of democratic engagement. The ability to interpret, question, and ethically engage with data empowers individuals to participate meaningfully in civic life, challenge misinformation, and advocate for equitable policies. This paper examines the intersection of data literacy, statistical reasoning, and civic education, emphasizing their role in fostering informed and socially responsible citizens. Drawing from global scholarships, pedagogical innovations, and case studies, we argue that a critical approach to data education enhances democratic resilience by promoting analytical thinking, ethical awareness, and social justice. By reimagining curriculum design and instructional strategies, educators can equip learners with the competencies needed to navigate a data-saturated society, promote equity, and contribute to democratic decision- making.
Integrating data literacy into teacher education: Fostering reflection and critical thinking regarding AI
The increasing prevalence of data-driven technologies, including artificial intelligence (AI), underscores the need to develop data literacy skills and the ability to critically reflect on these technologies. This is especially important in teacher education, as educators play a crucial role in fostering such competencies in future generations. However, the reflective discussion is underrepresented compared to the demonstration of the performance and learners find it challenging to connect theoretical concepts to their lives. A seminar concept for prospective math teachers was developed that explicitly aimed to stimulate reflections on mathematical foundations of AI and the implications for society and the students’ own life. The material was tested twice. In the second iteration of the study, modifications were made by including concrete examples from the students’ lifeworld. An analysis of the contextual references in the students’ discussions shows that in the second round, indeed, more and more diverse context-related answers were given.
Teaching regression calibration to correct for measurement error to develop statistical thinking
Introductory statistics and biostatistics courses are essential for cultivating statistical thinking. This work explores the effectiveness of teaching measurement error (ME) methods in an introductory biostatistics course to teach three statistical concepts: bias, uncertainty, and decision-making. A tutorial was developed to illustrate the application of ME methods for a specific case study using a sample of U.S. adults (n=600) from the National Health and Nutrition Examination Survey (NHANES). Data were analyzed using logistic regression models to predict diabetes status based on blood pressure levels adjusted for age, race, calories, alcohol intake, and body mass index (BMI). To imitate real world applications of ME, simulated random noise was introduced based on reliability levels. Regression calibration (RC) was applied and improved the estimate of the noisy data. Students were taught that RC may reduce the effect of ME in statistical modeling to reduce bias, and to also improve uncertainty and decision-making. This work demonstrated the utility of an ME tutorial to develop understanding of statistical bias, uncertainty, and decision-making.
Teachers’ role in promoting primary school students’ integration of mathematical, statistical, and other STEAM reasoning through data-based modelling
Data-based modelling driven by interdisciplinary contexts has emerged as a means of promoting multidisciplinary reasoning in primary school students. However, teachers play a crucial role in facilitating this process. This study aims to explore teachers’ role in promoting students’ integration of mathematical, statistical, and other STEAM reasoning through data-based modelling. Specifically, it analyses STEAM practice of a teacher with Grade 4 students. To do this, we adopt an interdisciplinary data-driven modelling (IDDM) framework, which includes six key components: data, an interdisciplinary context, a mathematical model, a statistical model, models in other STEAM subjects, and prediction and decision-making. We use it to identify the teacher’s support related to data variability and modelling, aimed at promoting students’ multidisciplinary reasoning for prediction and decision-making. The findings provide practical strategies to enhance STEAM education through data- based modelling with skills in mathematics, statistics, data science, and other STEAM disciplines.
Visualizing probability: Young children's reasoning in estimating probabilities on a probability scale
Data literacy education and the Center for Data Science & Simulation at TU Dortmund University
Economics versus the environment: Promoting systems thinking, sustainability, and collaboration in a “commons” game
We want students to grow up to be good citizens. Part of that is to care for the environment and collaborate with one another in allocating shared resources. At the same time, society often identifies success with short-term financial gain—and that often entails setting aside the common good. How can we help students navigate the balance between the ubiquitous drive for money and the need to save the planet? One strategy is to use simulation games in which students experience the phenomena we’re concerned about rather than simply endure explanations in class. In the games we present, students use data to see how to make a profit and preserve the environment simultaneously. These games were developed as a small part of the EduS4EL project, described in Martignon and Martinez (2026), in this volume. This paper focuses on the games themselves and on design and pedagogical considerations.
Developing statistical and data science skills in interdisciplinary scientists
The University of Nottingham’s Natural Sciences course is an interdisciplinary science undergraduate degree. A new module was developed to teach introductory statistics based on the recommendations and goals of the GAISE report (ASA Revision Committee, 2016) with a focus on statistical thinking, conceptual understanding, real contexts, active learning, use of technology, and assessment which drives learning. In the pilot, second year undergraduate Natural Sciences students participated, with qualitative feedback on module design and delivery collected through survey instruments. Student feedback was positive, with them acknowledging that interactive classes and computer classes were engaging ways to learn using real-world contexts and that continuous assessment added depth to their learning. This case study demonstrates the potential to teach statistics to scientists through context- relevant scenarios, ultimately enabling students to apply their knowledge to projects involving their own scientific data.
DataScEd4CiEn: Integrating data science into STEAM education for civic engagement and social justice – A case from Greece
This paper explores the implementation of a data science activity within a STEAM scenario on food waste and sustainability, carried out in a Greek lower secondary school as part of the Erasmus+ project DataScEd4CiEn. The Greek case offers a unique lens due to the traditionally formal and discipline- bound structure of the national curriculum, where data literacy and interdisciplinary approaches are still emerging and not yet systematically embedded. Through an iterative design process, students engaged with real-world datasets to explore social issues and propose civic actions. The findings reveal both the emergence of key data science practices as well as the challenges involved in supporting open- ended inquiry and integrating data insights into civic messaging. The paper also highlights broader pedagogical tensions in teaching data science in schools and emphasizes the strong potential of STEAM scenarios to promote social engagement through data-driven inquiry.