Mathematics

Boosting Data Science Teaching and Learning in STEM

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Boosting Data Science Teaching and Learning in STEM (aka “Data Fluency”) is a project that seeks to improve teachers’ and students’ “data fluency,” which includes understanding sources of data, structuring data for analysis, interpreting representations of data, inferring meaning from data, and explaining data and findings to diverse audiences. We will accomplish this goal by iteratively developing research-based professional learning that prepares teachers to offer next generation data-rich learning.

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Attributions of Mathematical Excellence in Teaching and Learning

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This project aims to examine how teachers' beliefs about the origins of mathematical excellence, which might be attributed to genetic, social, or personal characteristics, affect racial and gender equity in their classrooms. A three-part study will create, validate, and apply the Attributions of Mathematical Excellence Scale (AMES), correlating scores with race, gender, and achievement equity.

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Applying and Refining a Model for Dynamic, Discussion-based Professional Development for Middle School Teachers about Fractions, Ratios and Proportions (Collaborative Research: Brown)

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In this design-based research project, we are refining a model for mathematics professional development. We engage middle grades teachers in playful, discussion-based exploration of fractions and proportions through tasks and dynamic "toys” as well as planning for classroom implementation. We are now adding “Connections”, designed to support participants in transitioning from learners to teachers during the PD. For one group, Connections are reflection on paper, for the other, they are part of the PD discussion.

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Anchoring High School Students in Real-Life Issues that Integrate STEM Content and Literacy

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We present a framework for using scenario-based assessments (SBAs) to measure middle school students' ability to formulate written arguments around socio-scientific issues. We present data showing both the current strengths and limitations of these SBAs. We also present data which shows that, through the process of writing over a 2-week time span, the students showed significant improvements in their ability to make a claim, locate evidence, use reasoning, and use scientific vocabulary in their arguments.

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Advancing Equity and Strengthening Teaching with Elementary Mathematical Modeling (Collaborative Research: Suh)

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Advancing Equity and Strengthening Teaching with Elementary Mathematical Modeling is a teacher PD project focused on strengthening K-5 teaching with mathematics modeling. Building on previous foundational work around mathematics modeling and equity, this project will bring together equity-oriented teaching practices and mathematical modeling to design and research the impact of a blended PD program on teacher practice.

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Adapted Measure of Math Engagement: Designing Self-Report Measures of Mathematics Engagement for Black and Latina/o Middle School Students (Collaborative Research: Holquist)

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A three year, mixed-methods, critical participatory action research project to develop a culturally sustaining self-report measure of Black and Latina/o student math engagement, called the Adapted Measure of Math Engagement (AM-ME).

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Opportunities for Research within the Data Science Education Community

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

Author/Presenter

Katherine Miller, Chad Dorsey, The Concord Consortium; Kirsten Daehler, Leti Perez, WestEd; Kayla DesPortes, New York University; Nicholas Horton, Amherst College; Seth Jones, Middle Tennessee State University; Josephine Louie, Education Development Center; Josh Rosenberg, University of Tennessee, Knoxville; David Weintrop, University of Maryland

Lead Organization(s)
Year
2023
Short Description

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

Young Mathematicians: Expanding an Innovative and Promising Model Across Learning Environments to Promote Preschoolers' Mathematics Knowledge

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Young children’s mathematics development underpins cognitive development, building brain architecture, fostering problem-solving, puzzling, and persevering, and strongly impacting and predicting future success in school – but systemic opportunity gaps have created unequal access to high-quality mathematics learning experiences, with differences in children’s math knowledge beginning before kindergarten entry.

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Patterns of Using Multimodal External Representations in Digital Game-based Learning

Although prior research has highlighted the significance of representations for mathematical learning, there is still a lack of research on how students use multimodal external representations (MERs) to solve mathematical tasks in digital game-based learning (DGBL) environments. This exploratory study was to examine the salient patterns problem solvers demonstrated using MERs when they engaged in a single-player, three-dimensional architecture game that requires the acquisition and application of math knowledge and thinking in game-based context problem solving.

Author/Presenter

Yanjun Pan

Fengfeng Ke

Chih-Pu Dai

Lead Organization(s)
Year
2022
Short Description

Although prior research has highlighted the significance of representations for mathematical learning, there is still a lack of research on how students use multimodal external representations (MERs) to solve mathematical tasks in digital game-based learning (DGBL) environments. This exploratory study was to examine the salient patterns problem solvers demonstrated using MERs when they engaged in a single-player, three-dimensional architecture game that requires the acquisition and application of math knowledge and thinking in game-based context problem solving.

Exploring Students’ Learning Support Use in Digital Game-based Math Learning: A Mixed-Methods Approach Using Machine Learning and Multi-cases Study

Digital game-based math learning environments (math DGBLE) are promising platforms that provide students with opportunities to master conceptual understanding and cultivate mathematical thinking, on which the contemporary mathematics education places an emphasis. Literature on learning support in digital game-based learning (DGBL) rarely investigate learners' support-use behaviors and interaction patterns in relation to math learning. We addressed this research gap in this exploratory mixed-methods study.

Author/Presenter
Chih-Pu Dai
Fengfeng Ke
Yanjun Pan
Yaning Liu
Lead Organization(s)
Year
2023
Short Description

Digital game-based math learning environments (math DGBLE) are promising platforms that provide students with opportunities to master conceptual understanding and cultivate mathematical thinking, on which the contemporary mathematics education places an emphasis. Literature on learning support in digital game-based learning (DGBL) rarely investigate learners' support-use behaviors and interaction patterns in relation to math learning. We addressed this research gap in this exploratory mixed-methods study. We designed and developed a packet of learning supports (i.e., Task Planner and Math Story) in a math DGBLE.