The Quest for URM Doctoral Persistence: An Analysis of Feedback Loops in the Academic System

Martha Lucia Cano-Morales, Michael Corey Weinberg, Eduardo Rodriguez Mejia, Juan M. Cruz, Gwen Lee-Thomas, Holly M. Matusovich, Mayra S. Artiles, Abimelec Mercado Rivera, Stephanie G. Adams

    Research output: Contribution to journalConference articlepeer-review


    Studies have shown that the graduation rate for underrepresented minorities (URM) students enrolled in engineering doctorates is significantly lower than their peers. In response, we created the “Rising Doctoral Institute (RDI)”. This project aims to address issues that URM students encounter when transitioning into a Ph.D. in engineering and their decision to persist in the program. To suggest institutional policies that increase the likelihood of URM students to persist in their doctorate, we identify and analyze some factors in the academic system that reinforce or hinder the retention of URM students in doctoral education. Although the factors that influence persistence in URM students have been largely studied as direct causes of attrition or retention, there is a need for a system perspective that takes into account the complexity and dynamic interaction that exists between those factors. The academic system is a complex system that, by nature, is policy resistant. This means that a positive variation of a factor can incur unintended consequences that could lead to a negative variation in other factors and ultimately hinder the positive outcomes of that policy. In this work-in-progress article, we analyze the dynamics of the factors in the academic system that reinforce or hinder the retention of URM graduate students in engineering. The purpose is to build some of the causal loops that involve those factors, to improve the understanding of how the complex system works, and prevent unintended consequences of institutional policies. We used Causal Loop Diagrams (CLD) to model the feedback loops of the system based on initial hypotheses of causal relationships between the factors. We followed a process that started with establishing hypotheses from a previous literature review, then using a different set of articles we identified the factors related to the hypotheses and the causal links between them. Next, we did axial coding to group the concepts into smaller categories and established the causal relations between categories. With these categories and relations, we created the CLDs for each hypothesis. For the CLDs that have connections missing to close the loop, we went to find additional literature to close them. Finally, we analyzed the implications of each CLD. In this article, we analyze and describe three major CLDs found in literature. The first one was built around the factor of having a positive relationship with the supervisor. The second centered on the student's experience. The third focused on factors that relate to university initiatives.

    Original languageEnglish (US)
    JournalASEE Annual Conference and Exposition, Conference Proceedings
    StatePublished - Jun 25 2023
    Event2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023 - Baltimore, United States
    Duration: Jun 25 2023Jun 28 2023

    All Science Journal Classification (ASJC) codes

    • General Engineering


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