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The Making of an Innovative Engineer: Academic and Life Experiences that Shape Engineering Task and Innovation Self-Efficacy
Author(s) -
Mark Schar,
Shan Gilmartin,
Beth Rieken,
Samantha Brunhaver,
Helen Chen,
Sheri Sheppard
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--28986
Subject(s) - class (philosophy) , engineering education , task (project management) , self efficacy , curriculum , psychology , mathematics education , engineering , computer science , pedagogy , social psychology , engineering management , artificial intelligence , systems engineering
This research paper presents the results of a study that uses multivariate models to explore the relationships between participation in learning experiences, innovation self-efficacy, and engineering task self-efficacy. Findings show that many engineering students participated in learning experiences that are typically associated with engineering education, such as taking a shop class or engineering class in high school (47%), taking a computer science (81%) or design/prototyping (72%) class as an undergraduate, working in an engineering environment as an intern (56%), or attending a career related event during college (75%). Somewhat surprisingly, given the rigors of an engineering curriculum, a significant number of students participated in an art, dance, music, theater, or creative writing class (55%), taken a class on leadership topics (47%), and/or participated in student clubs outside of engineering (44%) during college. There also were important differences in rates of participation by gender, underrepresented racial/ethnic minority status, and first generation college student status. Overall prediction of engineering task self-efficacy and innovation self-efficacy was relatively low, with a model fit of these learning experiences predicting engineering task self-efficacy at (adjusted r of) .200 and .163 for innovation self-efficacy. Certain patterns emerged when the learning experiences were sorted by Bandura’s Sources of Self-Efficacy. For engineering task self-efficacy, higher participation in engineering mastery and vicarious engineering experiences was associated with higher engineering task self-efficacy ratings. For the development of innovation self-efficacy, a broader range of experiences beyond engineering experiences was important. There was a strong foundation of engineering mastery experiences in the innovation self-efficacy model; however, broadening experiences beyond engineering, particularly in the area of leadership experiences, may be a factor in innovation selfefficacy. These results provide a foundation for future longitudinal work probing specific types of learning experiences that shape engineering students’ innovation goals. They also set the stage for comparative models of students’ goals around highly technical engineering work, which allows us to understand more deeply how “innovation” and “engineering” come together in the engineering student experience. Key Concepts: self-efficacy, engineering task self-efficacy, innovation self-efficacy, learning experiences, academic pathway 1.0 Introduction This study provides an initial view of learning experiences that are associated most strongly with engineering students’ engineering and innovation self-efficacy, two domains of great interest to recent work in engineering education (Gilmartin et al. 2017). The data for this research come from an NSFfunded initiative called Epicenter (2013) that aimed to better understand the conditions that may encourage engineering students to be more entrepreneurial and innovative. Among Epicenter’s several research projects is an ongoing longitudinal survey study of the development of engineering students’ career goals around innovation and engineering, referred to as the Engineering Majors Survey (EMS 2016). The EMS study follows a nationally representative sample of engineering students from their undergraduate experiences through graduation and into the workplace (Gilmartin et al. 2017). Within this survey are measures of engineering task self-efficacy and innovation self-efficacy, as well as 39 background learning experiences and extra-curricular activities spanning high school through undergraduate education, which form the basis for this analysis. 2.0 Background This research is at the intersection of three important areas of study: self-efficacy, (learning-based) sources of self-efficacy, and the measurement of self-efficacy. 2.1 Self-Efficacy and Social Cognitive Career Theory Defined as an individual's belief in their ability to implement behaviors necessary to produce specific outcomes (Bandura 1995), self-efficacy has been shown to be an important predictor for a wide variety of positive outcomes (e.g., Bandura 2004, Caprara and Steca 2005, Scholz et al. 2002, Stajkovic and Luthans 1998, Zimmerman 2000), and has proven a useful indicator of academic major selection and performance and career choice (Lent, Brown, and Larkin 1986). Lent et al. developed a predictive model for career choice that is importantly influenced by self-efficacy. This model is Social Cognitive Career Theory (SCCT, see Figure 1) and it provides a framework for understanding, explaining, and predicting the processes through which people develop occupational choice (Lent, Brown, and Hackett 1994; Lent and Brown 2006). The SCCT model has proven to be useful in predicting career choice among postsecondary students, including engineering students (Lent et al. 2005, 2007). Figure 1Lent’s (1994, 2006) Social Cognitive Career Theory (SCCT) model. Shaded nodes are included in this study. SCCT posits that vocational or career choice is a function of several social-cognitive variables, such as self-efficacy, outcome expectations, interests and goals. Importantly, the SCCT framework suggests that self-efficacy is a result of a combination of person inputs, background environmental influences and learning experiences. It is this connection that this paper is fundamentally exploring, as part of a larger effort to explain innovative career goals as part of the broader EMS study design (Gilmartin et al. 2017). 2.2 Bandura’s Sources of Efficacy Beliefs Bandura (1995) provides guidance on likely the sources of individual efficacy beliefs. He indicates there are four sources of efficacy beliefs – mastery experiences, vicarious experiences, social persuasion, and positive physiological and emotional states. Mastery Experiences – Bandura (1995) describes mastery experiences as “the most authentic evidence 1 In the model for this paper, “person inputs” include age, gender, college GPA, URM and first generation college status; “background environmental influences” include family income and post-secondary school environment in terms of size of the engineering school and Carnegie Classification status. of whether one can muster whatever it takes to succeed” where successful experiences “build a robust belief in one’s personal efficacy” and failures “undermine it” (p. 3). Mastery experiences help acquire “the cognitive, behavioral and self-regulatory tools for creating and executing appropriate courses of action” (p. 3). In the context of engineering task self-efficacy, mastery experiences may involve engineering specific coursework, direct hands-on experiences with engineering tasks such as building, prototyping and design, and engineering work experience through an internship. Vicarious Experiences – Bandura describes this source of creating and strengthening efficacy as the influences provided by “social models” through relevant vicarious experiences. Bandura (1995) describes these vicarious experiences as “seeing people similar to themselves succeed by perseverant effort [and raising] observer’s beliefs that they, too, possess the capability to master comparable activates” (p. 4). There is also an element of aspiration to these vicarious experiences as students “seek proficient models who possess the competencies to which they aspire.” (p. 4) In the context of engineering task self-efficacy, vicarious experiences may involve attending a presentation on innovative engineering activity in the workplace, listening to others who have experience that may be valuable in the future (such as attending leadership seminars), and experiencing workplace success through the perspective of others like accomplished entrepreneurs. Social Persuasion – This source of efficacy involves verbal persuasion “that [individuals] possess the capabilities to master given activities [and] are likely to mobilize greater effort to sustain it than if they harbor self-doubts and dwell on personal deficiencies” (Bandura 1995, p. 4). In the context of engineering task self-efficacy, social persuasion may typically occur in the context of social groups or activities like participation in a robotics or engineering competition where work is done in teams, or through involvement or leadership of student clubs and organizations engaged in engineering activity. Positive Physiological and Emotional States – Finally, Bandura (1995) suggests that physiological and emotional states play an important role in judging one’s capabilities. Often students “interpret their stress reactions and tension as signs of vulnerability to poor performance” while “mood also affects people’s judgements of their personal efficacy” (p. 4). This domain is perhaps most difficult to operationalize, but we posit that activities outside of engineering-related mastery, vicarious, or social experiences can still be classified as influencing physiological states; examples include sports, experience with the arts, and involvement in study abroad. 2.3 Measuring Engineering Task and Innovation Self-Efficacy In this study, the dependent variables are self-efficacy measures (or scales): Engineering Task SelfEfficacy and Innovation Self-Efficacy. The background and construction of these variables are described in the EMS Technical Report (Gilmartin et al. 2017) and summarized below. Engineering Task Self-Efficacy (ETSE) ETSE is designed to measure confidence in one’s ability to perform integral technical engineering tasks. For this measure, we drew from Fouad and Singh’s (2011) work on engineering career outcomes, and the items in our scale, based on Fouad and Singh’s instrumentation, were initially adapted to the Pathways of Engineering Alumni Research Survey (Brunhaver et al. 2013). The scale is composed of five items that were identified through factor analysis of a longer list of engineering task items. The i

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