Statistics

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.

In today’s technologically complex and connected world, students’ needs to develop competencies and confidence with data have never been greater. A new On the Same Wavelength blog post shares work from a National Science Foundation project, Boosting Data Science Teaching and Learning in STEM. The authors describe effective practices for engaging all students with data in the classroom.

Perez , L. & Lionberger, K. (2023, May 3). Opening the door to data science in STEM classrooms. NextGenScience.

This self-reflective case study describes our project team’s efforts to promote equity in science professional learning (PL) by centering the voices of educators in the PL design process and within the course itself. We believe that educators’ experiences, priorities, and expertise are essential to developing professional learning that meets the needs of teachers and their students. We have a particular interest in amplifying the voices of those in historically underrepresented communities.

Rachel Folger laughs when she recounts the time one of the students in her eighth grade social studies class exclaimed, “Whoa, Ms. Folger! Did you know that this is just what we’re doing in math?” Rachel is thrilled that her students—who typically “walk through their day in these very isolated subject areas”—are making connections across the curriculum.

The DataPBL project enlisted a team of teachers, data science educators, and researchers to co-design data experiences for the eighth grade Japanese American Internment curriculum module developed by EL Education. In the DataPBL version of the interdisciplinary project-based module, students analyze and visualize data in CODAP. Project research is studying how students tell stories with data and how this data storytelling contributes to students’ data agency and identity.

A curriculum developer on the DataPBL project details his journey searching for data about the Japanese American internment for 8th grade students to explore with CODAP.

Roderick, S. (July 6, 2023). A search for data offers a new friendship and answers to 8th graders’ questions. The Concord Consortium.

Teachers tend to be lifelong learners, motivated to pursue professional learning that is meaningful to their particular needs. In 2013, Marrongelle et. al., noted “it is incumbent on the field to capitalize on emerging technologies in the design and delivery of effective professional development.” (p. 208). While the past decade has seen an increase in development of opportunities for personalized learning for mathematics teachers online (e.g., Silverman & Hoyos, 2018), more work is needed to provide additional research-based opportunities.

In this study, 82 middle and high school teachers engaged with the InSTEP online professional
learning platform to develop their expertise in teaching data science and statistics. We
investigated teachers’ engagement within the platform, aspects of the platform that were most
and least effective in building teachers’ expertise, and the extent to which teachers’ self-efficacy
changed. Using mixed methods, we collected, analyzed and integrated multiple data sources.