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Transfer students, who are disproportionately URM and first-generation, are a target population for boosting engineering representation. Transfer students in mechanical, aerospace and civil engineering at [the institution] take thermodynamics, a required gateway course, in their first or second term. This paper outlines the results from an observational study to determine how students interact in a peer-led learning environment. The PEERSIST (Peer-led, Student Instructed, Study group) model promotes academic competence through peer dialogue, in which disciplinary knowledge is socially co-constructed and refined over successive sessions. In order to help demonstrate that student interactions are the main source of learning in Peer-Led Study Groups (PLSGs), interactions between students were recorded and compared to those in traditional TA-led recitations using the observation protocol. Results show that students in PLSGs interact with their peers significantly more than students in the TA-led control group. The study also compares peer interactions by incoming course preparedness and finds a non-significant relationship between incoming GPA and peer-to-peer interactions. In contrast, the study finds a negative relationship between the rate at which students ask for and receive help and incoming GPA. 

The purpose of this study is to develop an instrument to measure student perceptions about the learning experiences in their online undergraduate engineering courses. Online education continues to grow broadly in higher education, but the movement toward acceptance and comprehensive utilization of online learning has generally been slower in engineering. Recently, however, there have been indicators that this could be changing. For example, ABET has accredited online undergraduate engineering degrees at Stony Brook University and Arizona State University (ASU), and an increasing number of other undergraduate engineering programs also offer online courses. During this period of transition in engineering education, further investigation about the online modality in the context of engineering education is needed, and survey instrumentation can support such investigations. The instrument presented in this paper is grounded in a Model for Online Course-level Persistence in Engineering (MOCPE), which was developed by our research team by combining two motivational frameworks used to study student persistence: the Expectancy x Value Theory of Achievement Motivation (EVT), and the ARCS model of motivational design. The initial MOCPE instrument contained 79 items related to students’ perceptions about the characteristics of their courses (i.e., the online learning management system, instructor practices, and peer support), expectancies of course success, course task values, perceived course difficulties, and intention to persist in the course. Evidence of validity and reliability was collected using a three-step process. First, we tested face and content validity of the instrument with experts in online engineering education and online undergraduate engineering students. Next, the survey was administered to the online undergraduate engineering student population at a large, Southwestern public university, and an exploratory factor analysis (EFA) was conducted on the responses. Lastly, evidence of reliability was obtained by computing the internal consistency of each resulting scale. The final instrument has seven scales with 67 items across 10 factors. The Cronbach alpha values for these scales range from 0.85 to 0.97. The full paper will provide complete details about the development and psychometric evaluation of the instrument, including evidence of and reliability. The instrument described in this paper will ultimately be used as part of a larger, National Science Foundation-funded project investigating the factors influencing online undergraduate engineering student persistence. It is currently being used in the context of this project to conduct a longitudinal study intended to understand the relationships between the experiences of online undergraduate engineering students in their courses and their intentions to persist in the course. We anticipate that the instrument will be of interest and use to other engineering education researchers who are also interested in studying the population of online students. 

This work falls under the evidence-based practice type of paper. Online undergraduate engineering education is rapidly increasing in use. The online format not only provides greater flexibility and ease of access for students, but also has lower costs for universities when compared to face-to-face courses. Even with these generally positive attributes, online courses face challenges with respect to student attrition. Numerous studies have shown that the dropout rate in online courses is higher than that for in-person courses, and topics related to online student persistence remain of interest. Data describing student interactions with their Learning Management System (LMS) provide an important lens through which online student engagement and corresponding persistence decisions can be studied, but many engineering education researchers may lack experience in working with LMS interaction data. The purpose of this paper is to provide a concrete example for other engineering education researchers of how LMS interaction data from online undergraduate engineering courses can be prepared for analysis. The work presented here is part of a larger National Science Foundation-funded study dedicated to developing a theoretical model for online undergraduate engineering student persistence based on student LMS interaction activities and patterns. Our sample dataset includes six courses, two from electrical engineering and four from engineering management, offered during the fall 2018 semester at a large, public southwestern university. The LMS interaction data provides details about students’ navigations to and submissions of different course elements including quizzes, assignments, discussion forums, wiki pages, attachments, modules, the syllabus, the gradebook, and course announcements. Relatedly, the features created from the data in this study can be classified into three categories: 1) learning page views, which capture student interactions with course content, 2) procedural page views, which capture student navigation to course management activities, and 3) social page views, which capture learner-to-learner and learner-to-instructor interactions. The full paper will provide the rationale and details involved in choices related to data cleaning, manipulation, and feature creation. A complete list of features will also be included. These features will ultimately be combined with associative classification to discover relationships between student-LMS interactions and persistence decisions. 

Engineering students graduate from their programs with a broad range of skills that are set by professional societies, industry recommendations, and other stakeholders in student success. But when those engineers enter their jobs, how are those skills utilized and nurtured by the organizations they enter? The purpose of this paper is to present a cross-sectional, secondary qualitative analysis of research exploring the experiences of recent engineering graduates as they move from student to professional. Of particular interest were the ways engineers describe their autonomy or sense of choice, the way engineers recognize and make sense of their organizations’ values, and the alignment (or lack thereof) between personal values and those of their organization. To do so, qualitative data sets from three different studies of engineers’ experiences at various stages in their professional trajectories were combined and thematically analyzed, producing four major themes that speak to the ways engineers perceive their sense of agency in their work experiences. Looking across data sets, themes emerged regarding empowerment, organizational fit, and workplace expectations. While these themes were common across the studies included in the analysis, the way the themes manifested across data sets raises interesting questions about the formation of engineers and the socialization experiences that contribute to that formation. As research on engineering practice continues to develop, it is important that researchers consider where engineers are within their career trajectory and how that influences their perceptions about the work they do and the agency they have within organizations. 

This poster will report on the research design and methodology planned for a recently funded National Science Foundation-sponsored project focused on advancing knowledge about the factors that influence the decisions of undergraduate engineering student to complete (rather than drop out of) online courses. Through the application of both social science and learner analytics-based research methods, the research will explore how students’ perceptions about the characteristics of their online undergraduate engineering courses and engagement with their course learning management system (LMS) influence their persistence. To support these studies, we draw on the undergraduate engineering student population at a large, public university in the southwestern United States that has been an early adopter of comprehensive online undergraduate engineering education. The findings from this work will be both important and timely, as the field of engineering education shows signs of embracing the online presence critical to increasing access and participation in engineering. 

This highly interactive special session has two goals: developing a deeper understanding of current research on engineering practice, and connecting and growing a diverse and vibrant scholarly community interested in this topic. There has arguably never been a more exciting time to examine engineering practice. In addition to a strong employment outlook for most engineering specialties, engineering careers are being reshaped and reimagined by rapid technological change, intensified globalization trends, new cross-disciplinary interactions, demographic shifts, and changing organizational structures. Colleges, universities, and organizations such as ABET, Inc., the National Academy of Engineering (NAE), and the National Science Foundation (NSF) are leading the charge to improve the alignment of engineering education with the demands of professional practice in response to these trends, potentially revolutionizing how current and future engineers are prepared as innovators, leaders, and change agents. Yet, not much is known about the diverse and multi-faceted realities of modern engineering practice and how this knowledge can be used to improve the education and training of engineers across career stages. Through presentations, networking opportunities, and group discussions, the special session will focus on using research on engineering practice to transform engineering education and the workforce. 

This highly interactive special session has two goals: developing a deeper understanding of current research on engineering practice, and connecting and growing a diverse and vibrant scholarly community interested in this topic. There has arguably never been a more exciting time to examine engineering practice. In addition to a strong employment outlook for most engineering specialties, engineering careers are being reshaped and reimagined by rapid technological change, intensified globalization trends, new cross-disciplinary interactions, demographic shifts, and changing organizational structures. Colleges, universities, and organizations such as ABET, Inc., the National Academy of Engineering (NAE), and the National Science Foundation (NSF) are leading the charge to improve the alignment of engineering education with the demands of professional practice in response to these trends, potentially revolutionizing how current and future engineers are prepared as innovators, leaders, and change agents. Yet, not much is known about the diverse and multi-faceted realities of modern engineering practice and how this knowledge can be used to improve the education and training of engineers across career stages. Through presentations, networking opportunities, and group discussions, the special session will focus on using research on engineering practice to transform engineering education and the workforce. 

This research paper describes how engineering juniors and seniors perceive the influence of socializers on their post-graduation career planning. Grounded in Expectancy x Value Theory (EVT), this qualitative investigation is part of a sequential mixed-methods study that included two survey phases and an interview phase. An exploratory analysis of 72 interview excerpts revealed four dominant socializer groups, namely, family, peers, university related individuals, and work related individuals, as well as three distinct areas of socializer influence: thinking about specific jobs, job exploration in general, and choosing whether to pursue further education. A closer look showed that while parents, peers, professors, and supervisors were all important to students’ career plans, the type of influence each had tended to differ. In-depth examples of socializer influence and their impact on students’ job related decisions are shared in this paper. The results are insightful for researchers, university and industry stakeholders, and students. 

This paper provides an example of how an NSF-funded project, Professional Engineering Pathways Study [EEC-1360665, 1360956, and 1360958] or PEPS has incorporated a community of practice approach to disseminate the use of evidence-based decisions to design activities that assist engineering students in making career choices. The paper will discuss the elements of a community of practice, how it has been used in PEPS, and how other projects might use this approach to bring about other kinds of change. Key words: Community of practice, educational reform 

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 r2 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.