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During and since the 2023 PME-NA Annual Conference the working group has carried out many ongoing activities with the goals of creating community and producing scholarship in relation to our group's interests in statistics and data science education. During the conference, issues of environmental justice/sustainability and social justice were two major common interests of participants. Though both themes are clearly linked, discussions of social justice do not always include conversations about environmental justice. We then broke into two groups based on these interests to discuss current work we might have related to the topic or ideas for future work. These groups have continued to meet and have discussions throughout the year and will continue into the 2024 conference. We additionally had a group of interested participants publish an article in Mathematics Teacher: Learning and Teaching PK-12 based on a gap in the literature identified by the group. [ABSTRACT FROM AUTHOR]

Simulations are considered essential instructional tools in teaching probability and statistics, with computer simulations in particular receiving emphasis in recent years. Despite this, little research has been reported on how learners use computer simulations to solve problems and investigate stochastic contexts. Clinical interviews with four preservice secondary mathematics teachers investigated this issue as they worked with a series of probabilistic tasks in CODAP. This report presents findings based on participants' performance on one of the tasks, which involved making predictions about a school board election based on simulated polling results. [ABSTRACT FROM AUTHOR]

In going into our sixth year as a working group, our aims are to build off of previous work of the group and look to new productive avenues of scholarship. In particular, based on ongoing conversations of people who met during the 2023 PMENA conference, we seek to find ways of forming interdisciplinary collaborations to support the teaching and learning of statistics and data science through data investigations in meaningful contexts drawing from the contextual knowledge of experts from other fields such as social studies education and science education. We hope to begin work on a grant proposal to pursue funding for ideas developed and refined during the conference to support the ongoing work of the working group. We also invite new members who are interested in exploring issues around teaching and learning with data investigations. [ABSTRACT FROM AUTHOR]

We continue the past four years of the working group on teaching and learning data investigation by 1) sustaining the ongoing cross-institutional collaboration to develop frameworks and resources for supporting mathematics educators in facilitating data investigations and 2) advancing our discussions on designing data investigations with an eye towards taking action to promote socially and environmentally just outcomes. Based on the role of context in teaching and learning with data, we organize this working group into three themes: the context of mathematics teacher preparation, the context of cross-disciplinary work, and the context of research and collaboration. [ABSTRACT FROM AUTHOR]

This report presents preliminary results from the initial survey and task within an ongoing design study investigating preservice elementary teachers’ approaches to sampling. Nineteen preservice teachers enrolled in an elementary mathematics methods course completed an initial survey involving tasks related to sampling and inference, followed by a series of designed activities using computer simulations as a tool for inquiry of these concepts. Of interest were the preservice teachers’ initial ideas, and how exposure to the simulations may have impacted their approaches. Initial analysis of the surveys suggests that participants’ ideas about sampling showed similarities to research involving K-12 students. Their work during the first task suggests that computer simulations may help learners make more reasonable predictions within sampling contexts, along with providing a way to quantify the likelihood of those predictions. [ABSTRACT FROM AUTHOR]

Context plays a crucial role when students engage in analysis and interpretation of data, and is broadly recognized as one of the main elements that separate the studies of mathematics and statistics (Cobb & Moore, 1997). The ways that students engage with the environment surrounding data within a statistical task are of interest to researchers because of this. In this analysis of a teaching experiment with third grade students, interactions with self-collected wingspan data led to insights about graphs as students attended to individual values, but students varied in their willingness to use the data to describe/infer more broadly. [ABSTRACT FROM AUTHOR]