This theoretical article describes a framework to conceptualize computational thinking (CT) dispositions—tolerance for ambiguity, persistence, and collaboration—and facilitate integration of CT in mathematics learning. CT offers a powerful epistemic frame that, by foregrounding core dispositions and practices useful in computer science, helps students understand mathematical concepts as outward oriented. The article conceptualizes the characteristics of CT dispositions through a review of relevant literature and examples from a study that explored secondary mathematics teachers' engagement with CT. Discussion of the CT framework highlights the complementary relationship between CT and mathematical thinking, the relevance of mathematics to 21st-century professions, and the merit of CT to support learners in experiencing these connections.

is currently a doctoral student at the Ohio State University, where she is in her second year of the STEM education PhD program. She is a graduate research assistant on the STEM+C NSF funded project, looking at integrating computer science and engineering concepts into algebra classrooms. Bailey received her BS in mechanical engineering from Ohio Northern University and her M.Ed. in curriculum and instruction from University of Cincinnati. Her research area of interest is creating a more equitable learning environment for underrepresented populations of students in the STEM fields.

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Kindergartners and first-grade students listen excitedly to a modified storybook to guide their geometry activities.

Recent calls have been made to enhance and extend the statistical experiences of K-12 students. However, to ensure that such goals are met, teachers also need to develop deep conceptual understanding and pedagogical content knowledge that are essential to statistical thinking and reasoning. In this regard, over the past two decades, leading thinkers and professional organizations had advocated that teaching and curricula should be focused and organized around problem solving. In this paper we describe three such technology-supported curricula-a project-based learning (PjBL) unit, problem-solving activities (PS) unit, and a model-eliciting activities (MEA) unit-that align with this perspective and discuss the ways in which they supported pre-service teachers' engagement with elementary statistics concepts and technology. Our findings target two specific gaps in the literature-research on the use of technology in the development of statistical literacy and providing empirical support for advancing teachers' statistical knowledge through engagement in the statistical investigation cycle.