Amplifying Statistics and Data Science in Classrooms

Our world is rich with data sources and today’s media is flooded with graphs and data that require data literate citizens. Technology makes data more accessible than ever before, and statistics and data science skills can help students be ready for data-intensive STEM careers. Many countries around the world have increased the emphasis on statistics and data science in school curriculum — from elementary grades through college. This professional learning experience will help you develop strategies for using an investigation cycle to teach statistics and data science, ignite students’ interest in real world data investigations with technology, and emphasize inferential reasoning through posing different types of investigative questions.

Amplifying Statistics and Data Science in Classrooms is organized into two modules, each with 5 units. You can earn a 20 hour certificate of completion for each module (40 hrs for both). In both modules, there are opportunities to hear from experts, learn with colleagues to gain different perspectives on teaching and learning statistics and data science, and to build a library of resources to support your teaching.

Both modules feature conversations with expert teachers and statistics educators, including Christine Franklin, the American Statistical Association K-12 Statistical Ambassador. Both modules also feature opportunities to engage with data and view videos of students and teachers engaging with data in classrooms.

These professional learning resources are in an on-demand format. Once registered, you will always have access and can select the modules of interest to you and complete modules at your own pace, on your own schedule!

Over 4,400 educators from around the world — all 50 states, and 94 countries — have already engaged with materials in these modules when they were first offered from 2015-2019. The materials have been updated and slightly revised to reflect current research, trends, tools, and resources for teaching statistics and data science in grades 6-12. We think you will find a wealth of learning opportunities and resources that you can immediately use in your classrooms!

Who Should Use These Modules?

These modules are applicable to anyone interested in strengthening their approaches to teaching statistics and data science. The statistical content and strategies are appropriate for implementation with middle school through early college learners. Thus, teachers in grades 6–12 are the primary audience, although post-secondary or elementary teachers may also benefit from the professional learning experience. The modules may also be of interest to teacher educators, and teachers of other disciplines that use data-based explorations extensively to make claims and inferences (e.g., science, social science).

Course Objectives

Module 1: Teaching Statistics Through Data Investigations

  • Strengthen your understanding of how to engage students in a statistical investigation;
  • Explore a framework for guiding your teaching of statistical investigations to promote deeper data explorations for your students;
  • Use rich data sources and dynamic graphing tools to support investigations of questions that are of interest to you and your students;
  • Examine ways students reason with data to make evidence-based claims;
  • Personalize applications of statistical investigations to your students.

Module 2: Teaching Statistics Through Inferential Reasoning

  • Strengthen your understanding of how to engage students in a statistical investigation process for the purpose of making inferences or claims;
  • Explore a framework for guiding your teaching of statistical investigations to promote inferential reasoning for your students;
  • Use rich data sources and dynamic graphing tools to support data exploration for investigative questions that give students opportunities to make inferences about contexts and issues of interest them;
  • Examine ways students reason with data to make inferences or claims;
  • Apply a framework for inferential reasoning to your educational practices.

Learn More

The modules in this professional learning experience build on content developed in two previous professional learning courses at the Friday Institute for Educational Innovation at NC State University: Teaching Statistics Through Data Investigations and Teaching Statistics Through Inferential Reasoning.

We greatly appreciate the funding support that was provided by the William and Flora Hewlett Foundation.

Thank you as well for the partial funding support and the open education resources made available by the American Statistical Association.

This professional learning experience builds on content developed in two previous professional learning courses: Teaching Statistics through Data investigations (TSDI) and Teaching Statistics through Inferential Reasoning (TSIR).

Dr. Hollylynne S. Lee

Dr. Hollylynne S. Lee is a Professor of Mathematics and Statistics Education in the department of Science, Technology, Engineering, and Mathematics Education at NC State University. Her work focused on statistics education, spanning 20+ years, began with developing the Probability Explorer software and conducting research on learners' reasoning about probabilistic phenomena through a simulation approach. Her current work focuses on preparing teachers of statistics and data science to use innovative approaches and powerful technology tools to engage learners in building understandings. Her work has led to numerous books, research publications, and awards. In 2013, she was named a NC State University Faculty Scholar and received an Alumnae Association Outstanding Teacher award in 2014. In 2020, she was named a Fellow of the American Statistical Association and earned the University of North Carolina Board of Governor’s Award for Teaching Excellence.

Dr. Gemma Mojica

Dr. Gemma F. Mojica is a Research Scholar at the Friday Institute for Educational Innovation at NC State University. Her current work focuses on statistics education, particularly supporting the professional learning of practicing teachers to teach statistics and data science, as well as preparing prospective mathematics teachers to teach statistics. Her research interests include designing and exploring the types of experiences in teacher education and professional development that support teachers in creating learning environments that support students' thinking about statistics and data.

Dr. Christina Azmy

Dr. Christina Azmy is an Assistant Professor of Education in the Department of Teacher Education at Catawba College. As a Graduate Research Assistant, she contributed to the content development of TSIR.

Dr. Jennifer N. Lovett

Dr. Jennifer N. Lovett is an Assistant Professor of Mathematics Education in the Department of Mathematical Sciences at Middle Tennessee State University. As a Graduate Research Assistant, she contributed to the content development of TSDI.

Dr. Dung Tran

Dr. Dung Tran is a Senior Lecturer at Macquarie University in Melbourne Sydney. As a Postdoctoral Research Associate at the Friday Institute for Educational Innovation at NC State University he contributed to the content development of TSDI.

Alex Dreier

Alex Dreier is an Instructional Design Lead on the Professional Learning and Leading Collaborative team at the Friday Institute for Educational Innovation at NC State University. He contributed to the development of TSDI and TSIR.

Theresa Gibson

Theresa Gibson is a founding partner and the Director of Operations at Leading EDge Learning. When she was a Project Coordinator at the Friday Institute for Educational Innovation at NC State University, she contributed to the development of TSDI.

Christine Franklin

Christine Franklin is the American Statistical Association K-12 Statistical Ambassador. She is also a Senior Lecturer Emeritus and former Lothar Tresp Honoratus Honors professor in the Department of Statistics at the University of Georgia. She served as an expert for TSDI and TSIR.

Dr. Susan Friel

Dr. Susan Friel is a retired Professor of Mathematics Education and former McMichael Term Professor in the School of Education at the University of North Carolina at Chapel Hill. She served as an expert in TSDI.

Dr. Maryann Huey

Dr. Maryann Huey is an Associate Professor of Mathematics in the Department of Mathematics and Computer Science at Drake University. She served as an expert for TSIR.

Kaycie Maddox

Kaycie Maddox is a Mathematics Specialist at Northeast Georgia Regional Educational Service Agency. She is a former teacher and former President of the Georgia Council of Teachers of Mathematics. She served as an expert in TSIR.

Stephen Miller

Stephen Miller is a teacher of statistics at the Winchester Thurston High School in Pittsburgh, PA and former member of the American Statistical Association/National Teachers of Mathematics Joint Committee on K-12 Education in Statistics and Probability.

Greg Ray

Greg Ray is a doctoral student in Science, Technology, Engineering, and Mathematics Education at NC State University and a former mathematics and statistics teacher. His statistics classroom is featured in many of the video resources in TSIR.

Doug Tyson

Doug Tyson is a statistics teacher at Central York High School in York, PA and a member of the American Statistical Association/National Teachers of Mathematics Joint Committee on K-12 Education in Statistics and Probability. He served as an expert for TSIR. He served as an expert for TSIR.

Dr. Webster West

Dr. Webster West is the developer of StatCrunch. He was formally on faculty at NC State University, Texas A&M, and University of South Carolina. He served as an expert for TSIR.

Dr. Roger Woodard

Dr. Roger Woodard is a Teaching Professor and Director of the Data Science MS Program in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He served as an expert for TSIR.

Amplifying Statistics and Data Science in Classrooms provides a scalable, accessible, and flexible approach that is aligned with principles of effective professional learning. Our approach is grounded in authentic, active, and collaborative professional learning activities. The approach builds upon the following key design principles:

  1. Self-directed, job-connected, personalized learning, to enable you to focus your time and attention on what you will find most valuable, whether you want to learn more about developing strategies for using an investigative cycle and promoting inferential reasoning with your students, or try data investigations yourself with some online technology tools.
  2. Multiple perspectives, including opportunities to learn from a variety of experts in statistics education, including experienced teachers and state and district leaders. Video resources not only focus on discussions with these educators but include videos featuring students and teachers in classrooms.
  3. Peer-supported learning, to enable you to learn from other teachers and to share your ideas, questions, and experiences with your online colleagues. We highly encourage small groups of teachers to consider using a professional learning team model to engage with the modules together.
  4. Anywhere, anytime learning, so that you can use this professional learning experience as you plan to teach statistics and data science and continue your learning as you go.
Available On Demand
Duration 2 modules, each with 5 units
Cost Free
Primary Audience Grade 6-12 Teachers
Lead Developers Dr. Hollylynne Lee
Dr. Gemma Mojica
Technology CODAP online data visualization and analysis tool

Many free apps such as GapMinder, Tuva, GeoGebra, Desmos, and other online statistics applets
Related Project Invigorating Statistics Teacher Education Through Professional Learning
Certificate Available Yes
Certificate Hours 20+