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In study abroad programs, overall student participation and the representation of minoritized students continue to rise, but barriers to participation for minoritized students persist, especially in engineering. As we strive to broaden participation in engineering study abroad programs, we must critically examine the experiences of minoritized students who engage in these programs so we can facilitate supportive educational environments. This study examines the experiences of minoritized students in a global engineering study abroad program, using the concept of habitus, a student's collection of identities and embodied cultural capital. We find that students draw on various forms of habitus while studying abroad, connecting their new experiences abroad to their prior experiences. Based on these findings, we make recommendations for designing more equitable and inclusive study abroad programming elements.

Background: Mixed methods research designs in engineering education often frame the "mixed" aspect of the design from the perspective of data collection. However, intentional mixing throughout the research design--particularly during data analysis--may enable richer meta-inferences about a phenomenon. Purpose: This paper provides examples of strategies for mixing during data analysis in mixed methods program evaluation in engineering education. Although the context is program evaluation, the procedures are applicable in engineering education research more broadly. Scope/Method: This review presents examples of mixed methods analysis strategies in the context of a data set with qualitative and quantitative data from a global engineering program at a large Mid-Atlantic university. The structure of the examples presents mixed research questions, pragmatic purposes for such studies, and examples of different mixing strategies for the analysis stage. The mixing strategies highlighted include extreme case sampling, converting, creating a blended variable, blending variables and themes across strands, and cross-case comparison. Conclusions: This review of mixed methods in program evaluation prompted a reflection of processes in the design of studies, how the designs are described beyond the usual taxonomies in the mixed methods literature, and implications for the greater community of engineering education researchers. Five mixed methods designs were formulated around the mixing strategies.

This study investigated the experiences of engineering students who participated in a cultural simulation activity to promote cross-cultural communication skills. A sample of 138 post-activity short reflections from a global engineering practice course were analyzed using qualitative inquiry. Results suggest that students recognize parallels between acclimating to the simulated culture in the cultural simulation activity and adjusting to a different country's culture while traveling abroad. Although the simulation was embedded in a pre-departure course for study abroad, the cultural simulation could also be used to develop cross-cultural communication in the classroom without traveling abroad.

Background: Mixed methods research designs in engineering education often frame the "mixed" aspect of the design from the perspective of data collection. However, intentional mixing throughout the research design—particularly during data analysis—may enable richer meta‐inferences about a phenomenon. Purpose: This paper provides examples of strategies for mixing during data analysis in mixed methods program evaluation in engineering education. Although the context is program evaluation, the procedures are applicable in engineering education research more broadly. Scope/Method: This review presents examples of mixed methods analysis strategies in the context of a data set with qualitative and quantitative data from a global engineering program at a large Mid‐Atlantic university. The structure of the examples presents mixed research questions, pragmatic purposes for such studies, and examples of different mixing strategies for the analysis stage. The mixing strategies highlighted include extreme case sampling, converting, creating a blended variable, blending variables and themes across strands, and cross‐case comparison. Conclusions: This review of mixed methods in program evaluation prompted a reflection of processes in the design of studies, how the designs are described beyond the usual taxonomies in the mixed methods literature, and implications for the greater community of engineering education researchers. Five mixed methods designs were formulated around the mixing strategies. [ABSTRACT FROM AUTHOR]