Inform Your Teaching with Statistics Education Research

MCTM 2025
Published

April 25, 2025


Session Description

An overview of statistics education research including historical and current perspectives and findings. Get information about the primary journals, conferences, and resources. And, learn how this work can inform your teaching.


Slides


Suggestions for Teachers

  • View the primary goal of your instruction as discovery and application of concepts.
    • This takes more time than simply teaching calculations.
    • Focus on core concepts:
      • Data, Variation, Distribution, Comparing Distributions, Samples and Sampling, Inference, Covariation
  • Use carefully developed activities to promote student reasoning.
    • Create activities around statistical investigative questions that require students to discuss and think about the data and the problem.
    • Avoid activities that lead students step-by-step through a list of procedures.
    • Have students make conjectures and then implement technology to test those conjectures.
  • Build on students’ intuitive notions and prior knowledge.
    • Acknowledge and scaffold students’ transition from their colloquial understanding to more statistical understandings.
    • Because misconception persist, even “simple” ideas will need to be revisited many times.
  • Integrate active learning.
    • Engage students in collaboration, interaction, and discussion.
    • Instructors might need to initally facilitate this process.


Suggested Reading List (in alphabetical order by author)

Abstract: The statistical problem-solving process is key to the statistics curriculum at the school level, post-secondary, and in statistical practice. The process has four main components: formulate questions, collect data, analyze data, and interpret results. The Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) emphasizes the importance of distinguishing between a question that anticipates a deterministic answer and a question that anticipates an answer based on data that will vary, referred to as a statistical question. This article expands upon the Pre-K-12 GAISE distinction of a statistical question by addressing and identifying the different types of statistical questions used across the four components of the statistical problem-solving process and the importance of interrogating these different statistical question types. Since the publication of the original Pre-K-12 GAISE document, research has helped to clarify the purposes of questioning at each component of the process, to clarify the language of questioning, and to develop criteria for answering the question, “What makes a good statistical question?”
Abstract: GAISE II presents a vision where every individual is confident in reasoning statistically, making sense of data, and knowing how and when to bring a healthy skepticism to information gleaned from data. Presented here is a framework of essential concepts and 22 examples across the three levels of skills development. This framework supports all students as they learn to appreciate the vital role of statistical reasoning and data science and acquire the essential life skill of data literacy.
Abstract: In this paper, we develop a personal synthesis of the most outstanding research on the teaching and learning of probability in the past years. We conducted a systematic search to examine publications on this topic in mathematics education, statistics education, education, and psychology journals. This exploration was complemented by additional studies published in conference proceedings or books. We classified these papers to highlight the main recent research tendencies in the field, according to the theme studied and considering the research objectives. Epistemological analyses suggest that informal inference based on simulation diminishes the topic abstraction but reduces probability to its frequentist view. Topics receiving particular attention include children’s probabilistic knowledge, the effect of visualizations on solving conditional probability problems, teachers’ education and probability modelling. In the final section, we recommend relevant points in which more investigation is needed to complete our knowledge about teaching and learning. In particular, we miss research on teachers’ mathematical knowledge of many probability concepts and on their didactic knowledge.
Abstract: When experienced analysts explore data in a rich environment, they often transform the dataset. For example, they may choose to group or filter data, calculate new variables and summary measures, or reorganize a dataset by changing its structure or merging it with other information. Such actions background, highlight, or even fundamentally change particular features of the data, allowing different types of questions to be explored. We call these actions data moves. In this paper, we argue that paying explicit attention to data moves, as well as their purposes and consequences, is necessary for educators to support student learning about data. This is especially needed in an era when students are expected to develop critical literacy around data and engage in purposeful, self-directed exploration of large and often complex datasets.
Abstract: Research in the areas of psychology, statistical education, and mathematics education is reviewed and the results applied to the teaching of college-level statistics courses. The argument is made that statistics educators need to determine what it is they really want students to learn, to modify their teaching according to suggestions from the research literature, and to use assessment to determine if their teaching is effective and if students are developing statistical understanding and competence.
  • Konold, C. & Harradine, A. (2014). Contexts for highlighting signal and noise. In T. Wassong, D. Frischemeier, P. R. Fischer, R. Hochmuth, & P. Bender (Eds.), Mit werkzeugen mathematik und stochastik lernen: Using tools for learning mathematics and statistics (pp. 237–250). Springer. [Available here]
Abstract: During the past several years, we have conducted a number of instructional interventions with students aged 12–14 with the objective of helping students develop a foundation for statistical thinking, including the making of informal inferences from data. Central to this work has been the consideration of how different types of data influence the relative difficulty of viewing data from a statistical perspective. We claim that the data most students encounter in introductions to data analysis—data that come from different individuals—are in fact among the hardest type of data to view from a statistical perspective. In the activities we have been researching, data result from either repeated measurements or a repeatable production process, contexts which we claim make it relatively easier for students to view the data as an aggregate with signal-and-noise components.

Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248. https://doi.org/10.1111/j.1751-5823.1999.tb00442.x [Available here]

Abstract: This paper discusses the thought processes involved in statistical problem solving in the broad sense from problem formulation to conclusions. It draws on the literature and in-depth interviews with statistics students and practising statisticians aimed at uncovering their statistical reasoning processes. From these interviews, a four-dimensional framework has been identified for statistical thinking in empirical enquiry. It includes an investigative cycle, an interrogative cycle, types of thinking and dispositions. We have begun to characterise these processes through models that can be used as a basis for thinking tools or frameworks for the enhancement of problem-solving. Tools of this form would complement the mathematical models used in analysis and address areas of the process of statistical investigation that the mathematical models do not, particularly areas requiring the synthesis of problem-contextual and statistical understanding. The central element of published definitions of statistical thinking is “variation”. We further discuss the role of variation in the statistical conception of real-world problems, including the search for causes.


Resources

Resources

Statistics Education Web Hubs


Journals

Research-Oriented Journals

Practitioner/Teaching-Oriented Journals


Books

Books on Learning and Assessment [All are Open-Access]

  • National Research Council. (1999). How people learn: Bridging research and practice. Washington, DC: The National Academies Press. https://doi.org/10.17226/9457

Abstract:

How People Learn: Bridging Research and Practice provides a broad overview of research on learners and learning and on teachers and teaching. It expands on the 1999 National Research Council publication How People Learn: Brain, Mind, Experience, and School, Expanded Edition that analyzed the science of learning in infants, educators, experts, and more. In How People Learn: Bridging Research and Practice, the Committee on Learning Research and Educational Practice asks how the insights from research can be incorporated into classroom practice and suggests a research and development agenda that would inform and stimulate the required change.

The committee identifies teachers, or classroom practitioners, as the key to change, while acknowledging that change at the classroom level is significantly impacted by overarching public policies. How People Learn: Bridging Research and Practice highlights three key findings about how students gain and retain knowledge and discusses the implications of these findings for teaching and teacher preparation. The highlighted principles of learning are applicable to teacher education and professional development programs as well as to K-12 education. The research-based messages found in this book are clear and directly relevant to classroom practice. It is a useful guide for teachers, administrators, researchers, curriculum specialists, and educational policy makers.

  • National Academies of Sciences, Engineering, and Medicine. (2018). How people learn II: Learners, contexts, and cultures. Washington, DC: The National Academies Press. https://doi.org/10.17226/24783

Abstract:

There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy.

In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom.

Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments.

How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.

  • National Research Council. (2005).* How students learn: History, mathematics, and science in the classroom.* Washington, DC: The National Academies Press. https://doi.org/10.17226/10126

Abstract:

How do you get a fourth-grader excited about history? How do you even begin to persuade high school students that mathematical functions are relevant to their everyday lives? In this volume, practical questions that confront every classroom teacher are addressed using the latest exciting research on cognition, teaching, and learning.

How Students Learn: History, Mathematics, and Science in the Classroom builds on the discoveries detailed in the bestselling How People Learn. Now, these findings are presented in a way that teachers can use immediately, to revitalize their work in the classroom for even greater effectiveness.

Organized for utility, the book explores how the principles of learning can be applied in teaching history, science, and math topics at three levels: elementary, middle, and high school. Leading educators explain in detail how they developed successful curricula and teaching approaches, presenting strategies that serve as models for curriculum development and classroom instruction. Their recounting of personal teaching experiences lends strength and warmth to this volume.

The book explores the importance of balancing students’ knowledge of historical fact against their understanding of concepts, such as change and cause, and their skills in assessing historical accounts. It discusses how to build straightforward science experiments into true understanding of scientific principles. And it shows how to overcome the difficulties in teaching math to generate real insight and reasoning in math students. It also features illustrated suggestions for classroom activities.

How Students Learn offers a highly useful blend of principle and practice. It will be important not only to teachers, administrators, curriculum designers, and teacher educators, but also to parents and the larger community concerned about children’s education.

  • National Research Council. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: The National Academies Press. https://doi.org/10.17226/10019

Abstract:

Education is a hot topic. From the stage of presidential debates to tonight’s dinner table, it is an issue that most Americans are deeply concerned about. While there are many strategies for improving the educational process, we need a way to find out what works and what doesn’t work as well. Educational assessment seeks to determine just how well students are learning and is an integral part of our quest for improved education.

The nation is pinning greater expectations on educational assessment than ever before. We look to these assessment tools when documenting whether students and institutions are truly meeting education goals. But we must stop and ask a crucial question: What kind of assessment is most effective?

At a time when traditional testing is subject to increasing criticism, research suggests that new, exciting approaches to assessment may be on the horizon. Advances in the sciences of how people learn and how to measure such learning offer the hope of developing new kinds of assessments-assessments that help students succeed in school by making as clear as possible the nature of their accomplishments and the progress of their learning.

Knowing What Students Know essentially explains how expanding knowledge in the scientific fields of human learning and educational measurement can form the foundations of an improved approach to assessment. These advances suggest ways that the targets of assessment-what students know and how well they know it-as well as the methods used to make inferences about student learning can be made more valid and instructionally useful. Principles for designing and using these new kinds of assessments are presented, and examples are used to illustrate the principles. Implications for policy, practice, and research are also explored.

With the promise of a productive research-based approach to assessment of student learning, Knowing What Students Know will be important to education administrators, assessment designers, teachers and teacher educators, and education advocates.

Books to Connect Research and Statistics Teaching

  • Batanero, C., Burrill, G., & Reading, C. (Eds.) (2011). *Teaching statistics in school mathematics—Challenges for teaching and teacher education: A Joint ICMI/IASE Study: The 18th ICMI Study. Springer. https://doi.org/10.1007/978-94-007-1131-0

Abstract:

Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education results from the Joint ICMI/IASE Study Teaching Statistics in School Mathematics: Challenges for Teaching and Teacher Education. Oriented to analyse the teaching of statistics in school and to recommend improvements in the training of mathematics teachers to encourage success in preparing statistically literate students, the volume provides a picture of the current situation in both the teaching of school statistics and the pre-service education of mathematics teachers.

A primary goal of Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education is to describe the essential elements of statistics, teacher’s professional knowledge and their learning experiences. Moreover, a research agenda that invites new research, while building from current knowledge, is developed. Recommendations about strategies and materials, available to train prospective teachers in university and in-service teachers who have not been adequately prepared, are also accessible to the reader.

Abstract:This handbook connects the practice of statistics to the teaching and learning of the subject with contributions from experts in several disciplines. Chapters present current challenges and methods of statistics education in the changing world for statistics and mathematics educators. Issues addressed include current and future challenges in professional development of teachers, use of technology tools, design of learning environments and appropriate student assessments. This handbook presents challenging and inspiring international research perspectives on the history and nature, current issues, and future directions of statistics education and statistics education research.

Abstract: Increased attention is being paid to the need for statistically educated citizens: statistics is now included in the K-12 mathematics curriculum, increasing numbers of students are taking courses in high school, and introductory statistics courses are required in college. However, increasing the amount of instruction is not sufficient to prepare statistically literate citizens. A major change is needed in how statistics is taught. To bring about this change, three dimensions of teacher knowledge need to be addressed: their knowledge of statistical content, their pedagogical knowledge, and their statistical-pedagogical knowledge, i.e., their specific knowledge about how to teach statistics. This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students’ learning.

This is a unique book. While providing a wealth of examples through lessons and data sets, it is also the best attempt by members of our profession to integrate suggestions from research findings with statistics concepts and pedagogy. The book’s message about the importance of listening to research is loud and clear, as is its message about alternative ways of teaching statistics. This book will impact instructors, giving them pause to consider: “Is what I’m doing now really the best thing for my students? What could I do better?”


Conferences