Statistics

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.

This paper explores a comprehensive framework to develop students’ data literacy by guiding them in making sense of complex data visualizations. With the growing complexity and prevalence of data visualizations in media, it’s crucial to equip students with the skills to critically analyze and engage with these visual forms of data. This toolkit emphasizes the importance of fostering data habits of mind, rather than mere computational proficiency, and encourages students to consider what a visualization is conveying, how it was created, and why it was created.

In this document, we identify key considerations to guide thinking and actions for data investigations, where the goal of an investigation is to answer a statistical question within a context to communicate approaches and solutions to a problem based on evidence. This process is composed of six phases: Frame the Problem, Consider and Gather Data, Process Data, Explore & Visualize Data, Consider Models, and Communicate & Propose Action.

When assisting students in a data investigation, it can be useful to help them develop key ways of thinking and dispositions that are helpful in developing expertise in conducting data investigations like a statistician or data scientist. It can be useful for students to see a reminder of this process and key considerations. This poster version of the Data Investigation Process that can be used in your classroom for this purpose.
 

Today, the ability to make sense of data is essential. K-12 students need educational experiences that can assist them in developing data literacy for global citizenry, and career and college pathways related to statistics and data science (e.g., Engel, 2017; Gould, 2017). Statistics and practices with data are included in standards and goals across the K-12 curriculum. Science puts a heavy emphasis on reasoning from and with data to understand scientific phenomena.

Developing the ability to do data science and make sense of the data science work done by others involves building skills and knowledge that span across all phases of a data life cycle and draw on many subject areas. Science tends to put a heavy emphasis on reasoning from and with data to understand scientific phenomena. English and Social Studies incorporate the use of data and data visualizations as evidence to support arguments, interpret information, and evaluate claims in social structures of our world.

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.

Data science, as a multidisciplinary field, has gained considerable interest in K-12 education. Prior research has explored innovative ways to introduce data science to young learners, emphasizing not only the development of data skills but also the connection of data science to students’ authentic inquiries and critical actions.

With the rise of the popularity of Bayesian methods and accessible computer software, teaching and learning about Bayesian methods are expanding. However, most educational opportunities are geared toward statistics and data science students and are less available in the broader STEM fields. In addition, there are fewer opportunities at the K-12 level. With the indirect aim of introducing Bayesian methods at the K-12 level, we have developed a Bayesian data analysis activity and implemented it with 35 mathematics and science pre-service teachers.

This report characterizes forms of dialogic support that a sixth-grade teacher generated during whole-class and small-group conversations to help students develop a practice of statistical modeling. During four weeks of instruction, students constructed and revised models to account for variability and uncertainty across a variety of random processes, many of which they experienced first-hand. Data sources for the research included field notes and video recordings of classroom conversations involving the teacher.