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Modeling Scientific Processes With Mathematics Equations Enhances Student Qualitative Conceptual Understanding and Quantitative Problem Solving
Author(s) -
SCHUCHARDT ANITA M.,
SCHUNN CHRISTIAN D.
Publication year - 2016
Publication title -
science education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.209
H-Index - 115
eISSN - 1098-237X
pISSN - 0036-8326
DOI - 10.1002/sce.21198
Subject(s) - mathematics education , phenomenon , process (computing) , test (biology) , computer science , concept learning , science education , structural equation modeling , mathematics , epistemology , paleontology , philosophy , biology , operating system , machine learning
Amid calls for integrating science, technology, engineering, and mathematics (iSTEM) in K–12 education, there is a pressing need to uncover productive methods of integration. Prior research has shown that increasing contextual linkages between science and mathematics is associated with student problem solving and conceptual understanding. However, few studies explicitly test the benefits of specific instructional mechanisms for fostering such linkages. We test the effect of students developing a modeled process mathematical equation of a scientific phenomenon. Links between mathematical variables and processes within the equation and fundamental entities and processes of the scientific phenomenon are embedded within the equation. These connections are made explicit as students participate in model development. Pre–post gains are tested in students from diverse high school classrooms studying inheritance. Students taught using this instructional approach are contrasted against students in matched classrooms implementing more traditional instruction (Study 1) or prior traditional instruction from the same teachers (Study 2). Students given modeled process instruction improved more in their ability to solve complex mathematical problems compared to traditionally instructed students. These modeled process students also show increased conceptual understanding of mathematically modeled processes. The observed effects are not due to differences in instructional time or teacher effects.