Matthew Scheidt

Bio: Matthew Scheidt is an academic researcher from Purdue University. The author has contributed to research in topics: Engineering education & Exploratory factor analysis. The author has an hindex of 4, co-authored 15 publications receiving 41 citations. Previous affiliations of Matthew Scheidt include Honda.

Validity Evidence for the SUCCESS Survey: Measuring Non-Cognitive and Affective Traits of Engineering and Computing Students

Abstract: This research paper examines the validity evidence from an exploratory factor analysis for a pilot of the SUCCESS survey (Studying Underlying Characteristics for Computing and Engineering Student Success). This survey was developed to measure underlying factors that may influence student success including personality, community, grit, thriving, identity, mindset, motivation, perceptions of faculty caring, stress, gratitude, self-control, mindfulness, and belongingness. We measure these underlying factors because each engineering and computing student admitted to a university has clear potential for academic and personal success in their undergraduate curriculum from admissions criteria, however, while some thrive academically, others flounder. In this project, we ask, “Why is it that highly credentialed and previously successful students do not see the same success in college?” We posit that some collection of characteristics— apparently not visible on their admission applications and perhaps not related to their talent or intelligence—is an important piece of the student performance puzzle. We developed a survey to measure various non-cognitive and affective factors that we believe are important for student achievement, academically, personally, and professionally. These non-cognitive and affective factors are representative of multifaceted aspects of undergraduate student success in prior literature. Each of the constructs we chose had validity evidence from prior studies, some within an engineering population. We piloted the survey across two different universities, one West Coast and one Midwest (n = 490), in Summer 2017. We used Exploratory Factor Analysis (EFA) to evaluate instrument performance to decide which items to include in the national release of the survey in Fall 2017. Our results provide preliminary validity evidence for items that measure various non-cognitive and affective factors. The wide-ranging constructs within the SUCCESS survey provide multiple pathways to understand students’ likelihood for success in engineering and computing. Our future work includes distributing this survey to over a dozen universities across the U.S., yielding a broad dataset of non-cognitive profiles of engineering and computing students broadly. In parallel, we will link these results with students’ registrar information at three study sites to develop predictive models for student success. Motivation for this study Engineering and computing education remains critical for U.S. workforce development and technological innovation now and into the future [1]–[3]. Many students recognize the importance and opportunity associated with studying STEM majors, and engineering and computing programs today have a talented applicant pool [4]. As a consequence, many institutions see relatively uniform and strong applicant credentials in terms of high school GPA, standardized test scores, and leadership experiences [5]. Each admitted student has the clear potential for academic success in the undergraduate curriculum. However, while some thrive at the university, many languish near the middle of the pack, or worse, they struggle academically. We want to know why highly credentialed and previously successful students sometimes do not see the same success in college. We posit that there are characteristics—apparently not visible on their admission applications—of such students that may make them more likely to navigate successfully the difficult pathways in their engineering and computing programs. We believe that an important piece of the student performance puzzle lies in the collection of non-cognitive and affective (NCA) factors including grit, study habits, personality, feelings of belongingness, and a sense of engineering or computing identity. To decide on these factors, and others, we engaged in an extensive process including a literature review, prioritization based on interests, and constructs with existing measurements to decide on this set of NCA factors. Each of the factors that we included in our pilot survey consisted of items used in other studies and are described in detail below. Differences in these traits, not asked on admissions materials and perhaps formed through the college experience, may explain particular reasons why some students thrive while others struggle. This project begins to answer the call from National Academy of Sciences [6] to see how interpersonal and intrapersonal factors contribute to student success, by focusing on how NCA factors influence student performance. The purpose of this paper is to introduce the SUCCESS project survey and to describe how we used a pilot study and exploratory factor analysis (EFA) as part of a decision-making process to evaluate items for inclusion on the national survey. In this process, we assume that our sample is reflective of our future national sample and do not anticipate our models to significantly improve [7] or become worse [8]. The results of this work also suggest how items used to measure NCA factors may be useful in future studies.

SAT Does Not Spell Success: How Non-Cognitive Factors Can Explain Variance in the GPA of Undergraduate Engineering and Computer Science Students

Abstract: This work-in-progress research paper uses multiple regression of both cognitive and non-cognitive factors to model current GPA of engineering and computer science students. High school GPA and ACT/SAT scores are among the most common scores used as admission criteria, which result in a relatively homogeneous engineering population. Prior research, however, shows that these scores do not account for the variance in GPA once these students start undergraduate studies. In this work, we explore how students’ cognitive (e.g., study skills, test performance, regulatory behaviors, etc.) and non-cognitive factors (e.g., identity, motivation, personality, etc.) predict student success in their engineering pathways. The data for this initial study comes from a pilot survey, deployed in the summer of 2017, of 490 engineering and computing students from two large, public institutions, one on the West Coast, the other in the Midwest. We used multiple linear regression to control for demographic variables while examining the predictive value of particular cognitive and non-cognitive factors for student academic achievement (i.e., GPA) in university. Our analysis shows, not surprisingly, that standardized test scores only explain a small portion of the variance of undergraduate GPA. Including non-cognitive and affective factors into a regression model produced a marked increase in the explained variance. Our work is novel in examining a constellation of possible factors that predict undergraduate student GPA, by combining both cognitive and non-cognitive factors as predictors. This analysis begins to unpack particular factors that have potential to predict GPA of engineering and computer science students.

Connecting Engineering Students’ Perceptions of Professional Competencies and Their Leadership Development

Abstract: The evolving challenges and demands of engineering require future professionals to have a broad skillset. To be adequately prepared for industry, undergraduate engineering students need to ...

Exploring the Relationship Between Non-Cognitive and Affective (NCA) Factors and First-Year Retention of Undergraduates in Engineering

Abstract: In this work in progress research paper, we investigated whether non-cognitive and affective (NCA) factors that predict academic success through GPA may relate to other forms of success such as student retention in engineering programs. Studies show that most students leave engineering within the first two years, making national retention rates in engineering less than 50%. Furthermore, the students who leave engineering are often academically talented, indicating a need to examine other success measures beyond GPA such as non-cognitive and affective (NCA) factors. Using data from a single institution (n = 540), we explore the NCA differences between students who remained in engineering after their first year and those who are no longer enrolled in engineering, or even in college at all. Results show that only one demographic and five NCA measures are statistically significant predictors of continued enrollment. Overall, a better understanding of student success as measured by retention using NCA profiles might assist researchers and practitioners with developing interventions and supportive environments that promote students’ academic success and thriving in engineering.

Using Multivariate Statistics

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Mindset The New Psychology Of Success

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Occupational Outlook Handbook

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A Scoping Literature Review of Engineering Thriving to Redefine Student Success

Abstract: Background: The importance of thriving is well-established, but little is known about thriving for undergraduate engineering students. We introduce engineering thriving as the process by which engineering students develop optimal functioning in undergraduate engineering programs. Since thriving is currently underexplored in the engineering education literature, we investigated the larger body of literature on engineering student success. Purpose: We introduce the concept of engineering thriving to synthesize the largely discrete existing bodies of literature on engineering student success to bring together many different perspectives, methodological approaches, and findings that shape our understanding of engineering thriving. Our work on thriving unites disparate lines of research on engineering student success, challenges the assumption that addressing barriers automatically leads to success, and strives to change the way engineering education views student success. Scope/Method: We used the scoping literature review method to investigate papers on undergraduate engineering student success. Four databases were searched, yielding 726 initial papers that studied separate dimensions of engineering student success, such as academic, personal, cognitive, and behavioral. We integrated the relationships among these dimensions to develop an understanding of engineering thriving. Our final analysis included 68 papers after removing duplicates and applying selection criteria. Conclusions: Our findings indicate that an engineering student thriving includes multiple dimensions of success, involves cyclical processes of growth and adaptation, and consists of synergistic competencies that should ideally be studied together with as many other competencies as possible. These findings support the conclusion that engineering thriving can be understood as helping students manage constantly changing internal and external factors within the broader engineering education system.

A Sociocultural Learning Framework for Inclusive Pedagogy in Engineering

Abstract: This paper describes a sociocultural learning framework for classroom inclusion based on research in three interconnected areas: learner identity, classroom context, and engineering culture. This paper is intended to serve as a resource for CEE authors to incorporate research-based inclusive pedagogy into the design and implementation of their chemical engineering education efforts.