Computational Analysis of Student Learning of Physics

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Abstract Summary

Nationally, African American women only represent 156 of the current 22,000 Ph.D.s in physics and astrophysics earned between 2016 and 1972 [1]. As a Science, Technology, Engineering, and Mathematics (STEM) field, physics lags behind all other fields in the representation of Hispanic and Black students [2]. Creating an environment where these students can foster an interest in physics and learn the foundational skills that support science are critical to changing their perception of physics as a topic and as a career path. The research question to address is the following; “Does PBL pedagogy have a statistically significant effect on content knowledge, scientist identity, or self-motivation, as indicated by the selected surveys?”  A student creating her own code in Python to analyze the data addresses this question. The Python macros created by the student will run automatically at regular intervals to include newly created data each semester. If this analysis does not fully answer the research question, the faculty member and student will work together to create a relational database to probe the dataset more thoroughly. The resulting analysis will determine if the course pedagogy has any effect on identity, self-efficacy, and course content. 

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2019-452
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Abstract Topics
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Spelman College
Spelman College

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