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.

Course-based undergraduate research experiences (CUREs) have the potential to improve undergraduate biology education by involving large numbers of students in research. CUREs can take a variety of forms with different affordances and constraints, complicating the evaluation of design features that might contribute to successful outcomes. In this study, we compared students’ responses to three different research experiences offered within the same course. One of the research experiences involved purely computational work, whereas the other two offerings were bench-based research experiences. We found that students who participated in computer-based research reported at least as much interest in their research projects, a higher sense of achievement, and a higher level of satisfaction with the course compared with students who did bench-based research projects. In open-ended comments, similar proportions of students in each research area expressed some sense of project ownership as contributing positively to their course experiences. Their comments also supported the finding that experiencing a sense of achievement was a predictor of course satisfaction. We conclude that both computer-based and bench-based CUREs can have positive impacts on students’ attitudes. Development of more computer-based CUREs might allow larger numbers of students to benefit from participating in a research experience.

Scientific ideas are often expressed as mathematical equations. Understanding the ideas contained within these equations requires making sense of both the embedded mathematics knowledge and scientific knowledge. Students who can engage in this type of blended sensemaking are more successful at solving novel or more complex problems with these equations. However, students often tend to rely on algorithmic/procedural approaches and struggle to make sense of the underlying science. This deficit may partly be the fault of instruction that focuses on superficial connections with the science and mathematics knowledge such as defining variables in the equation and demonstrating step-by-step procedures for solving problems. Research into the types of sensemaking of mathematical equations in science contexts is hindered by the absence of a shared framework. Therefore, a review of the literature was completed to identify themes addressing sensemaking of mathematical equations in science. These themes were compiled into nine categories, four in the science sensemaking dimension and five in the mathematics sensemaking dimension. This framework will allow for comparison across studies on the teaching and learning of mathematical equations in science and thus help to advance our understanding of how students engage in sensemaking when solving quantitative problems as well as how instruction influences this sensemaking.

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There is little consensus on the kinds and amounts of teacher support needed to achieve desired student learning outcomes when mathematics is inserted into science classrooms. When supported by educative curriculum materials (ECM) and heavy investment in professional development (PD), teachers implementing a unit designed around mathematical modeling of scientific mechanisms substantially increased students’ ability to make both qualitative and quantitative predictions (Schuchardt & Schunn, 2016). Because of concerns about equitable access to support resources, we investigated whether variations in PD support while retaining ECM could differentially affect two student learning outcomes: Quantitative Predictions and Qualitative Predictions. Two contrasts were performed examining: (1) the effect of reducing PD and (2) whether eliminating PD entirely caused further harm to student learning. Reducing and eliminating PD had no significant effect on student gains in Qualitative Predictions, suggesting ECM can be sufficient for teachers to support student learning of conceptual science content. However, student gains in Quantitative Predictions decreased significantly upon reducing PD; eliminating PD did not cause significant additional decreases. Combined, these findings suggest that amount of face‐to face PD support necessary to achieve student‐learning gains can vary depending on whether the practice requires application of qualitative science content or quantitative reasoning.