Simulation supported directed self-guided learning of blended math-science sensemaking for historically marginalized STEM learners

Blended math-science sensemaking (MSS) is reflected in a student's ability to integrate math and science knowledge to develop mathematical descriptions of observations. While an essential component of scientific thinking, there is little research on teaching MSS. Students from backgrounds historically marginalized in STEM often lack the prior learning opportunities needed to succeed in STEM. Supporting them in developing MSS could help them build quantitative understanding and improve STEM performance. We designed and tested a self-guided learning model based on the use of open-source PhET interactive educational simulations and a previously validated MSS cognitive framework to teach MSS. We tested the self-guided activities grounded in the framework with 27 students from backgrounds historically marginalized in STEM attending minority-serving 2- and 4-year US colleges. We evaluated long and short-term MSS transfer by asking students to complete computer-based transfer tasks immediately following and at least 1 week post activity. Most students started at the lowest and progressed to the highest MSS level and demonstrated considerable short and long-term transfer after completing just one activity, suggesting the effectiveness of this approach for fostering transferable MSS. This work provides a roadmap for designing self-guided MSS learning experiences across STEM contexts for a diverse range of learners by leveraging the power of open-source interactive computer simulations and validated MSS learning theory.