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This research paper discusses the process of refining and expanding the Decision-Making Competency Inventory developed by Miller and Byrnes. Byrnes is the author of the Self-Regulation Model of Decision-Making (SRMDM), which posits that that self-regulated decision-makers spend time in three phases: generation of options, evaluation of options, and learning from the results. Additionally, adaptive decision-makers are aware of moderating factors (such as stress or lack of information) and work to overcome them. A revised instrument is presented. 

The objective of this EEC project is to help students learn to make academic decisions that lead to success. The research goals are to: 1) identify curriculum-specific patterns of achievement that eventually lead to dropout and corresponding alternative paths that could lead to success; and 2) advance knowledge of self-regulation patterns and outcomes in engineering students. The education goals are to develop curricula and advising materials that help students learn how to effectively self-regulate their decision processes through contextual activities and story prompting. This poster will present current progress and future directions of the project. We will summarize accomplishments on the development of the Self-Regulated Decision-Making instrument, mapping of pathways, and development of the academic dashboard. 

This complete research paper documents how confidence in choice of intended major and self-regulated decision-making competency influence whether a student changes their intended major while participating in a compulsory first-year engineering (FYE) program. Initial major, confidence in that major choice, and self-regulated decision-making competency were documented in the Fall of 2017 for students matriculating into a FYE program. Student enrollment in a major in the Fall of 2018 was connected to this data. Retention in any engineering major and in the student’s intended major were analyzed using logistic regression. 

This Full Research Paper discusses ongoing work to develop a survey instrument to reliably assess undergraduate engineering student self-regulated decision-making. This work focuses on a second round of item expansion and refinement to the Decision-Making Competency Inventory (DMCI) to develop items related to learning from past decisions. The refined instrument was distributed to first-year engineering students enrolled in a large, public, land-grant institution located in the southeastern United States in the Fall of 2018. Of the approximately 1,200 students in first-year engineering courses, 883 valid surveys were randomly split into two separate samples for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA results indicated a viable four-factor solution, which was explored with the CFA. The CFA results also indicated a four-factor model was appropriate. Improving this instrument will help researchers document and understand students’ decision-making skills and how they relate to observed decisions like initial choice of major or change of major. A decision-making instrument will also be valuable in evaluating the effectiveness of interventions to help students build their decision-making competency and make adaptive choices. 

In this work in progress, we use Lee and Matusovich’s model of co-curricular support (MCCS) and the recruitment aspect of the Building Engineering and Science Talent (BEST) framework to investigate the policies, programs, and activities for, and recruitment and retention of, underrepresented students at four institutions. These four colleges of engineering are in the top 15 nationally in engineering bachelors’ degrees conferred on Black students. This information will provide background for a mixed-methods study on the choice, persistence, and attrition of Black students in computer, electrical, and mechanical engineering and is a critical first step to contextualize the student experiences to be revealed in individual interviews. We use publicly available information from web-based and other resources as well as interviews with key informants on each campus to ascertain the programs that are available on each campus and categorize them using the MCCS and BEST frameworks. This study lays the groundwork for future institutional data analysis and interviews with Black students currently or formerly enrolled in computer, electrical or mechanical engineering to uncover what causes them to enroll and persist in or leave these majors. Keywords: Race/ethnicity, undergraduate programs, support programs, MIDFIELD 

This work-in-progress paper represents our initial approach to developing a procedure for identifying indicators of “overpersistence.” This approach is one facet of a larger NSF CAREER project, “Empowering students to be adaptive decision-makers,” to model student pathways using a ground-up curriculum-specific approach with the ultimate goal of helping students choose more strategic paths to graduation. We define “overpersisters” as those students who enter college with a specific major in mind and never sway from that choice, nor graduate in a timely manner. While persistence in and commitment to a major choice are generally viewed positively, some students become fixated on a major that may not be the best fit for them. These overpersisters often spend years in a degree program and eventually leave the institution with no degree, but potentially with a substantial amount of debt. Identifying academic events that cause these students to eventually withdraw from school is the first step towards creating better strategies through which they can persist and succeed in their undergraduate studies. The concept of overpersistence is defined relative to a particular major, so a student who tries a different major before leaving the institution would not be considered an overpersister. We selected the discipline of Mechanical Engineering as a starting point because of its large enrollment and the first author’s familiarity with the discipline. Our goal is to begin developing a procedure that will identify indicators of overpersistence and provide a foundation that will help to answer the larger research question: In Mechanical Engineering, what academic events commonly lead to late dropout without changes in academic major?