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Background <p>Blended mathematical sensemaking in science (“MSS”) involves deep conceptual understanding of quantitative relationships describing scientific phenomena. Previously we developed the cognitive framework describing proficiency in MSS across STEM disciplines, and specifically Physical Science. The framework was validated with undergraduate students using assessment built around PhET sims. Students in the prior study were from a reasonably selective university serving predominantly White student population. In this study we investigate whether the framework can help identify specific patterns of engagement in MSS among students from backgrounds historically marginalized in STEM [i.e., members of Black/African American, Hispanic/Latinx/Indigenous/Native American and People of Color (POC) communities] attending U.S. Minority-Serving Institutions (MSIs). This study provides insights on how to better support these students in building transferable MSS skills.</p> Results <p>The framework is generally effective in characterizing engagement in MSS by learners in this study. One distinction is that these students relied less than our previous population on quantitative pattern identification as a stage in successfully developing the mathematical relationship describing their observations. Unlike students from non-marginalized backgrounds, students in this study productively leverage lower level MSS to develop the formula for observations without using quantitative pattern identification. In addition, students in this study tend to rely more on PhET sims rather than other data sources (e.g., data tables) to find the correct formula compared to students from non-marginalized backgrounds.</p> Conclusions <p>The MSS framework can guide the development of instructional and assessment strategies to support students from backgrounds historically marginalized in STEM in building MSS skills. The framework helped identify specific types of MSS that should be supported to facilitate the transition to the highest framework levels among these students, and these types of MSS turned out to be similar for both dominant and diverse student groups. Furthermore, PhET sims provide an effective environment for learning MSS skills, and their capabilities should be leveraged for designing learning experiences in the future. Finally, students at level 1 of the MSS framework should be supported in developing a deeper math understanding and integration of math and science when making sense of phenomena.</p>

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Investigating blended math-science sensemaking with historically marginalized STEM learners

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Leonora Kaldaras,
  • Carl Wieman

摘要

Background

Blended mathematical sensemaking in science (“MSS”) involves deep conceptual understanding of quantitative relationships describing scientific phenomena. Previously we developed the cognitive framework describing proficiency in MSS across STEM disciplines, and specifically Physical Science. The framework was validated with undergraduate students using assessment built around PhET sims. Students in the prior study were from a reasonably selective university serving predominantly White student population. In this study we investigate whether the framework can help identify specific patterns of engagement in MSS among students from backgrounds historically marginalized in STEM [i.e., members of Black/African American, Hispanic/Latinx/Indigenous/Native American and People of Color (POC) communities] attending U.S. Minority-Serving Institutions (MSIs). This study provides insights on how to better support these students in building transferable MSS skills.

Results

The framework is generally effective in characterizing engagement in MSS by learners in this study. One distinction is that these students relied less than our previous population on quantitative pattern identification as a stage in successfully developing the mathematical relationship describing their observations. Unlike students from non-marginalized backgrounds, students in this study productively leverage lower level MSS to develop the formula for observations without using quantitative pattern identification. In addition, students in this study tend to rely more on PhET sims rather than other data sources (e.g., data tables) to find the correct formula compared to students from non-marginalized backgrounds.

Conclusions

The MSS framework can guide the development of instructional and assessment strategies to support students from backgrounds historically marginalized in STEM in building MSS skills. The framework helped identify specific types of MSS that should be supported to facilitate the transition to the highest framework levels among these students, and these types of MSS turned out to be similar for both dominant and diverse student groups. Furthermore, PhET sims provide an effective environment for learning MSS skills, and their capabilities should be leveraged for designing learning experiences in the future. Finally, students at level 1 of the MSS framework should be supported in developing a deeper math understanding and integration of math and science when making sense of phenomena.