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Title:Grit, mindsets, and persistence of engineering students
Author(s):Choi, Dong San
Director of Research:Loui, Michael C
Doctoral Committee Chair(s):Loui, Michael C
Doctoral Committee Member(s):Eden, James G; Ravaioli, Umberto; Herman, Geoffrey L
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
logistic regression
transtheoretical model of health behavior change
design-based research
Abstract:Undergraduate engineering programs in the United States suffer from high rates of attrition. To develop the knowledge base that can inform efforts to reduce attrition rates, I conducted three studies focused on helping students persist in engineering. In the first study, I investigated whether grit would help students persist in engineering. In the second study, I explored the gritty behaviors of engineering students who persisted through academic failures. In the third study, I developed an intervention to encourage students to adopt healthy learning dispositions and behaviors to help them persist in engineering. The first study investigates whether a noncognitive factor called Grit could predict engineering retention. Specifically, I explored whether Grit predicts one- and two-year engineering retention, and whether student characteristics and academic performance affect the relationship between Grit and retention. I aggregated data from two first-year engineering cohorts who enrolled in a large public university in Fall 2014 and in Fall 2015. I used binary logistic regression to predict retention with Grit and its two subscales, Perseverance of Effort (PE) and Consistency of Interest (CI), gender, socioeconomic status, ACT math, high school grade-point-average (GPA), first math grade in college, first-semester GPA, first-year cumulative GPA, and second-year cumulative GPA. Grit and second-year cumulative GPA were significant predictors for two-year retention but not one-year retention. PE was a better predictor of retention than Grit for both one- and two-year retention, whereas CI was not a significant predictor of retention at all. Additionally, ACT math, high school GPA, first-semester GPA, and first-year cumulative GPA were significant predictors for both one- and two-year retention. Grit’s utility in predicting engineering retention relies on the PE construct. I recommend more research on the CI construct to better understand how it relates to Grit and success. Though PE is a statistically significant predictor of retention, estimates of predictive power suggest that PE should not be used to predict engineering retention. The second study explores the gritty behaviors of engineering students who persisted through academic failures. Academic failures can influence students to depart from engineering programs. In addition, researchers have identified many reasons for why students depart from engineering including perceived academic difficulty, chilly climates, and poor teaching and advising. However, the problems that departers experience are not unique to them; persisters share the same kinds of problems. To better understand the experience of persisters, I explored the experiences of persisting engineering students who had previously failed a required technical course. I used phenomenography as the qualitative research method to construct categories of description that describe the variety of ways persisting engineering students experienced academic failures. Based on 26 student interviews, I constructed four categories to describe their failure experiences: Unresponsive, Avoidant, Floundering, and Rebounding. Also, I found that students do not always experience failure the same way every time; they can experience failure differently for different instances of failure. Based on our findings, I recommend that failure be normalized in engineering education, and that course and program policies be revised to promote learning from failure. The third study entails the development of a course to encourage students to adopt healthy learning dispositions and behaviors to help them persist in engineering. Healthy learning dispositions encompass attitudes and beliefs that promote learning. Healthy learning behaviors comprise actions such as planning, monitoring, and reflecting that produce effective learning. I used the design-based research methodology to bridge from laboratory studies to classroom implementation. Following design-based research, I used the Transtheoretical Model of Health Behavior Change to guide this translation of theories related to healthy learning dispositions and behaviors into the design of the course. I found that this course helped students adopt the growth mindset and that elements of course design helped students engage in several processes of change. This study demonstrates that theory-informed interventions, like this course, can be effective in helping students adopt healthy learning dispositions. However, more research is needed to help students adopt healthy academic behaviors.
Issue Date:2018-07-10
Rights Information:Copyright 2018 Dong San Choi
Date Available in IDEALS:2018-09-27
Date Deposited:2018-08

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