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A growing consensus holds that preservice K–8 teachers (PSTs) need to experience the modeling process as learners to understand it and envision teaching modeling in their future classrooms. We examine this recommendation by exploring how PSTs construct models and how collaborative learning practices influence them in revising and refining their models. We also explore their reflections on modeling as a pedagogical experience. We introduce Modeling Decision Maps as a tool to examine how PSTs construct and refine mathematical models, and we draw on reflective journal entries to capture PSTs’ perspectives on the process. Our findings indicate that realistic modeling tasks provide opportunities to foster PSTs’ understanding of modeling, grow their mathematical modeling skills, and attune them to important pedagogical practices. 

Social justice mathematical modeling is powerful in helping teachers build awareness of social issues, critique existing systems, and engage in rich mathematical reasoning. In this article, we document a task in which 28 preservice teachers (PSTs) explored if teacher pay is fair and how to define “fair” mathematically. Through qualitative analysis of PSTs’ reflections, we studied the effectiveness of the task through the lens of critical consciousness. Twenty-six of the participants reported developing social and mathematical agency with respect to the task. Because the task related to PSTs’ lived experiences, it allowed them to examine their assumptions about teacher pay, empowered them to use mathematics to explore different perspectives, and helped them envision ways they could enact change. 

Although culturally responsive mathematics teaching is important, post-secondary education for preservice teachers (PTs) does not typically lead to learning opportunities for them to use mathematics to recognize the roles of social agents. To address this issue, we created a culturally responsive mathematical modeling task in which we invited PTs to use diverse approaches to create an action plan for the handling of local pothole data and maintenance. This study explores the approaches that PTs take when generating solutions to solve a culturally responsive mathematical modeling task and to investigate the perceptions of preservice teachers towards the task. The study involved 41 PTs, most of whom had no prior experience with mathematical modeling before participating in the task. PTs compared and inspected the documented data and noticed problems, such as a large economic disparity in the city. The PTs drafted solutions and shared their findings and suggestions in a proposal to the City’s Public Works Department. The proposals reflected different approaches, which helped the PTs understand the large income disparity and inequities certain neighborhoods in the city experience and provided recommendations for the city to address these issues. The creation of a culturally responsive mathematical modeling task provided an opportunity for PTs to engage in critical thinking and problem-solving while using diverse approaches to address a local social justice issue. This study provides insights into effective strategies for promoting socially responsible mathematics education in teacher preparation programs. 

Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students’ learning. Supplementary materials for this article are available online. 

The Great Pacific Garbage Patch activity involves an urgent environmental issue that students can discuss. It engages students in the interpretation of visual data, measurements, units, and the area of regular and irregular figures.