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The purpose of this research full paper is to examine the development of undergraduate students’ research identity during a summer undergraduate research experience. Identity development through socialization experiences is crucial for students to explore future career paths, especially in careers that require research-focused graduate degrees. However, literature is limited on how effective socialization occurs for research and future research-related careers. This paper follows 10 undergraduate engineering and physics students participating in an engineering-focused Research Experiences for Undergraduates (REU) program at an R1 institution to explore this gap in knowledge. As part of a longitudinal multi-method study, participants completed a pre- and post-experience survey, and participated in three interviews over the course of the summer. Survey data were analyzed using descriptive statistics and a Wilcoxon signed-rank test. Interviews were analyzed through the lens of academic self-concept theory for common themes of socialization and identity development in research through the course of the program. Findings indicate that undergraduate students’ research self-concepts are heavily influenced by research experiences and comparisons to their peers. The students’ increase in research self-concept as well as their experiences and interactions within the program allowed them to see research careers as attainable and increased their interest in pursuing graduate degrees after the program. Survey data showed a statistical increase in research self-efficacy and research identity at the end of the program, reinforcing the idea that students’ experiences in the REU helped them grow as researchers and engineers. This research increases our understanding of students’ research identity development and provides potential ways to implement research self-concept and identity development to similar undergraduate research experiences. 

The purpose of this methods paper is to identify the opportunities and applications of agent-based modeling (ABM) methods to interpretative qualitative and educational research domains. The context we explore in this paper considers graduate engineering attrition, which has been a funded research focus of our group for ten years. In attrition research, as with all human research, it is impossible and unethical to imperil real graduate students by subjecting them to acute stressors that are known to contribute to attrition in order to “test” different combinations of factors on persistence and attrition. However, agent-based modeling (ABM) methods have been applied in other human decision-making contexts in which a computer applies researcher programmed logic to digital actors, invoking them to make digital decisions that mimic human decision making. From our research team’s ten years of research studying graduate socialization and attrition and informed from a host of theories that have been used in literature to investigate doctoral attrition, this paper compares the utility of two programming languages, Python and NetLogo, in conducting agent-based modeling to model graduate attrition as a platform. In this work we show that both platforms can be used to simulate attrition and persistence scenarios for thousands of digital agent-students simultaneously to produce results that agree with both with previous qualitative data and that agree with aggregate attrition and persistence statistics from literature. The two languages differ in their integrated development environments (IDE) with the methods of producing the models customizable to fit the needs of the study. Additionally, the size of the intended agent pool impacted the efficiency of the data collection. As computational methods can transform educational research, this work provides both a proof-of-concept and recommendations for other researchers considering employing these methods with these and similar platforms. Ultimately, while there are many programming languages that can perform agent-based modeling tasks, researchers are responsible for translating high quality, theory-driven, interpretive research into a computational model that can model human decision-making processes.