Conference Session

Entrepreneurship and Inventive Thinking & Student Beliefs

Collection

2023 ASEE Annual Conference & Exposition

Authors

Ying Wang, Georgia Institute of Technology; Joy Harris; Karthik Ramachandran, Georgia Institute of Technology

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Educational Research and Methods Division (ERM)

HarrisKarthik Ramachandran, Georgia Institute of Technology ©American Society for Engineering Education, 2023 Measuring Engineering Students’ Entrepreneurial Self-Efficacy in an Entrepreneurship Education ProgramAbstract In this research paper, we developed and examined an Entrepreneurial Self-Efficacy forEngineering Students (ESE-E) instrument. Entrepreneurial self-efficacy refers to individuals’perceived capabilities to perform entrepreneurial tasks and produce entrepreneurial-relatedoutcomes. It is critical to develop and test the measurement of entrepreneurial self-efficacy withthe engineering student population. Further, entrepreneurship education programs are increasingand play a crucial

Conference Session

Research Methodologies – Session 2

Collection

2023 ASEE Annual Conference & Exposition

Authors

Todd M. Fernandez, Georgia Institute of Technology; David S. Ancalle, Department of Civil and Environmental Engineering, Kennesaw State University

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Educational Research and Methods Division (ERM)

self-efficacy with engineering students1 IntroductionIn this research paper, we re-evaluate structural aspects of validity for two instruments, the CurrentStatistics Self-Efficacy (CSSE) scale and the Statistical Reasoning Assessment (SRA) [1, 2]. The CSSE isa self-report measure of statistics self-efficacy while the SRA is a scored and criterion-based assessment ofstatistical reasoning skills and misconceptions. Both instruments were developed by statistics educationresearchers and have been consistently used to measure learning and interventions in collegiate statisticseducation. Our re-evaluation is part of a broader study of the effect of using a reflection-based homeworkgrading system in a biomedical engineering statistics course [3, 4

Conference Session

Educational Research and Methods Division (ERM) Technical Session 27

Collection

2024 ASEE Annual Conference & Exposition

Authors

Aturika Bhatnagar, New Jersey Institute of Technology; Prateek Shekhar, New Jersey Institute of Technology; Jeffrey Stransky, New Jersey Institute of Technology

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Educational Research and Methods Division (ERM)

disparities between engineers’ practices and their micro- and macroethics. Dr. Stransky is passionate about developing innovative educational interventions that measurably enhance students’ skills and competencies. https://orcid.org/0000-0002-4247-4322 ©American Society for Engineering Education, 2024 Exploratory Factor Analysis of Students’ Entrepreneurial Self-efficacy: Implications for Survey ValidationINTRODUCTIONHuman skills can take on a variety of forms as they evolve. These various functional domainsrequire unique knowledge and abilities. Given no one can embody all knowledge and abilities,one's perceptions of their efficacy in various activity domains vary one’s efficacy belief

Conference Session

Student Experiences and Development – Session 2

Collection

2023 ASEE Annual Conference & Exposition

Authors

Tejas Gupta, Nanyang Technological University ; Ibrahim H. Yeter, Nanyang Technological University; Michael Jin Khoo, Singapore University of Technology and Design

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Diversity

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Educational Research and Methods Division (ERM)

scores for all eight items were averaged to calculate the mean self-efficacystrength scores. Lower scores were indicative of weaker self-efficacy percepts, while higherscores were indicative of stronger self-efficacy percepts. The computed Cronbach’s α was.89, reflecting adequate internal consistency.Outcome Expectation (OE). Ten measures were used to determine participants’ OE, inspiredby Lent et al. (2003). Participants were required to answer their level of understanding withstatements that contained positive outcomes resulting from obtaining a Bachelor of Sciencedegree in engineering (e.g., “graduating with a BS degree in engineering will likely allow meto earn an attractive salary”). Their answers were ranked from 1 (strongly disagree) to 5

Conference Session

Work-in-Progress Session: Supporting Students To, Through, and Beyond Transitions

Collection

2023 ASEE Annual Conference & Exposition

Authors

Cody D. Jenkins; Thien Ngoc Y. Ta, Arizona State University; Ryan James Milcarek, Arizona State University; Gary Lichtenstein, Quality Evaluation Designs; Samantha Ruth Brunhaver, Arizona State University; Karl A. Smith, University of Minnesota, Twin Cities

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Educational Research and Methods Division (ERM)

Paper ID #39089Work in Progress: PEERSIST – A Formation of Engineers Framework forUnderstanding Self-Efficacy and Persistence among Transfer StudentsCody D. JenkinsMs. Thien Ngoc Y. Ta, Arizona State University Thien Ta is a doctoral student of Engineering Education Systems and Design at Arizona State University. She obtained her B.S., and M.S. in Mechanical Engineering. She has taught for Cao Thang technical college for seven years in Vietnam. She is currentlyDr. Ryan James Milcarek, Arizona State University Ryan Milcarek obtained his B.S., M.S. and Ph.D. in the Mechanical & Aerospace Engineering Department at

Conference Session

Instrument Design and Development

Collection

2023 ASEE Annual Conference & Exposition

Authors

Alicia Nicki Washington Ph.D., Duke University; Anna Romanova, Winthrop University; Philip Nelson; Siobahn Day Grady, North Carolina Central University; Legand Burge, Howard University

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Diversity

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Educational Research and Methods Division (ERM)

. Thisforces students to (re-)enter the same harmful environments with the expectation of developingenough “grit” to “persist” [13]. These efforts place the responsibility on the most minoritized,with no focus on those from dominant identities who create/enable these environments. Creatingand sustaining more equitable and inclusive environments requires improving everyone’scultural competence (not just increasing sense of belonging and self-efficacy in those who aremost harmed).As more computing departments develop interventions to increase diversity, equity, andinclusion that target all students [2], [14], an instrument for measuring their impact beyondenrollment, retention, and graduation rates is needed. This work details the development andtesting

Conference Session

Educational Research and Methods Division (ERM) Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Leonardo Pollettini Marcos, Purdue University; Julie S. Linsey, Georgia Institute of Technology; Melissa Wood Aleman, James Madison University; Robert L. Nagel, Carthage College; Kerrie A. Douglas, Purdue University ; Eric Holloway, Purdue University

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Educational Research and Methods Division (ERM)

Paper ID #41956Defining Measurement Constructs for Assessing Learning in MakerspacesMr. Leonardo Pollettini Marcos, Purdue University Leonardo Pollettini Marcos is a 3rd-year PhD student at Purdue University’s engineering education program. He completed a bachelor’s and a master’s degree in Materials Engineering at the Federal University of Sao Carlos, Brazil. His research interests are in assessment instruments and engineering accreditation processes.Dr. Julie S. Linsey, Georgia Institute of Technology Dr. Julie S. Linsey is a Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute

Conference Session

Formation and Development of Engineers

Collection

2023 ASEE Annual Conference & Exposition

Authors

Denise Wilson, University of Washington; Jennifer J. VanAntwerp, Calvin University; Shruti Misra, University of Washington

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Educational Research and Methods Division (ERM)

Paper ID #38149Engineering CAReS: Measuring Basic Psychological Needs in theEngineering WorkplaceProf. Denise Wilson, University of Washington Denise Wilson is a professor of electrical and computer engineering at the University of Washington, Seattle. Her research interests are split between technical research in sensors and sensor systems and engineering education with an emphasis on the role of self-efficacy, belonging, and other non-cognitive aspects of the engineering classroom and engineering workplace.Dr. Jennifer J. VanAntwerp, Calvin University Jennifer J. VanAntwerp is Professor of Engineering at Calvin University

Conference Session

Reviewing Methods for Educational Research

Collection

2023 ASEE Annual Conference & Exposition

Authors

Xiaoye Yang, University of Massachusetts, Lowell; Hsien-Yuan Hsu, University of Massachusetts, Lowell; Giovanni Bautista, University of Massachusetts, Lowell; Yanfen Li, University of Massachusetts, Lowell

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Diversity

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Educational Research and Methods Division (ERM)

STEM education for future researchers. He is currently participating in an NSF-funded grant (#1923452) to spearhead research into middle school students’ digital literacies and assessment. Recently, Dr. Hsu has received a seed grant at UML to investigate how undergradu- ate engineering students’ digital inequalities and self-directed learning characteristics (e.g., self-efficacy) affect their learning outcomes in a virtual laboratory environment during the COVID-19 pandemic. Dr. Hsu’s research interests include advanced quantitative design and analysis and their applications in STEM education, large-scale assessment data (e.g., PISA), and engineering students’ perception of faculty en- couragement and

Conference Session

Entrepreneurship and Inventive Thinking & Student Beliefs

Collection

2023 ASEE Annual Conference & Exposition

Authors

Joanna K. Garner, Old Dominion University; Melissa G. Kuhn, Old Dominion University; Jayme M. Cellitioci; Matthew Carter

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Diversity

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Educational Research and Methods Division (ERM)

and a possible solution,(Conradty, Sotiriou & Bogner, 2020). Self-report measures of design self-efficacy also tend toreflect subject domains such as science (self-efficacy for designing experiments; Hushman &Marley, 2015) and the arts (designing in a visual arts environment; Catterall & Peppler, 2007).Notably, we did not find a self-report measure for problem-finding, ingenuity, or inventivenessthat could be used in elementary and middle school settings.1 Models of the invention process are analogous to the pedagogical guidance provided in models of the engineeringdesign process or the scientific method.Rationale for the Study Using Inventive Mindset measure data from 252 elementary and middle school agedchildren, Garner

Conference Session

Educational Research and Methods Division (ERM) Technical Session 16

Collection

2024 ASEE Annual Conference & Exposition

Authors

Omar Jose Garcia, University of Oklahoma; Javeed Kittur, University of Oklahoma

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Educational Research and Methods Division (ERM)

. In addition, some PhDstudents have extensive prior teaching experiences while others have none.While a career in academia typically requires research, teaching, and service, most doctoraldegrees in the United States are conferred at research intensive universities, where researchaccomplishments are prioritized over instructional training for future faculty members [4].However, as some engineering PhD students wish to pursue a more teaching-focused career at aprimarily undergraduate institution, these future faculty members eventually find they did not feeladequately prepared for their career [5].Further investigation on the self-efficacy regarding instruction for engineering PhD students isneeded. Specifically, there is a need to better

Conference Session

Instrument Design and Development

Collection

2023 ASEE Annual Conference & Exposition

Authors

Felicity Bilow, Clarkson University; Mohammad Meysami, Clarkson University ; Jan DeWaters, Clarkson University

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Educational Research and Methods Division (ERM)

profession.DiscussionWe developed a questionnaire to measure student attitudes toward and perceptions ofengineering using items adapted from two previously vetted questionnaires. The instrument’sreliability and validity were confirmed through item analysis and an iterative EFA in which weexplored four models, with the number of factors ranging from three to six. Most adequate wasthe 6-factor structure, which assesses students’: (1) academic self-confidence and self-efficacy;(2) sense of belonging in engineering; (3) attitudes toward persisting and succeeding inengineering; (4) understanding of the broad nature of engineering; and perceptions of theimportance of (5) non-technical and (6) technical skills in engineering.Although the scree plot (Figure 1

Conference Session

Instrument Design and Development

Collection

2023 ASEE Annual Conference & Exposition

Authors

Wei Zhang, Zhejiang University; Liang Wang, School of Public Affairs, Zhejiang University; 帅 王, the School of Public Affairs, Zhejiang University

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Educational Research and Methods Division (ERM)

between authentic engineering learning and student engagement [35],professional identity or learning interest [36] , student-perceived learning outcomes [37], reasonableassumptions and problem-solving abilities [32], engineering learning self-efficacy [38] and so on.RESEARCH PURPOSEThe current study was situated in the engineering learning in communities of practice. Communities ofpractice were seen as an effectively collaborative learning situations with a group of learners sharingprofessional knowledge and common career enthusiasm. In our previous study, we found community ofpractice is an important engineering learning context and engineering learning happening in communitiesof practice usually focused on solving the authentic engineering

Conference Session

Educational Research and Methods Division (ERM) Technical Session 20

Collection

2024 ASEE Annual Conference & Exposition

Authors

Terkuma Stanley Asongo, University of Massachusetts, Lowell; Hsien-Yuan Hsu, University of Massachusetts, Lowell

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Educational Research and Methods Division (ERM)

Paper ID #42246Scoping Review of Instruments for Measuring Doctoral Students’ MentoringRelationships with Advisors or MentorsTerkuma Stanley Asongo, University of Massachusetts, Lowell I hold a degree in science education from the University of Agriculture Makurdi in Nigeria. Following that, I completed coursework for a master’s program in research, measurement, and evaluation at the University of Nigeria, Nsukka. I also earned a master’s degree in biomedical science from the Moscow Institute of Physics and Technology. Currently, I am pursuing a Ph.D. in research and evaluation at the University of Massachusetts Lowell

Conference Session

Educational Research and Methods Division (ERM) Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Kenneth West, University of Florida; Bruce Frederick Carroll, University of Florida; Jinnie Shin, University of Florida; Kent J. Crippen, University of Florida

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Diversity

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Educational Research and Methods Division (ERM)

supportprocess[2]. Outcomes include improvements in student self-efficacy and ultimately in studentpersistence to remain in the major[3]. The Mediation Model of Research Experiences (MMRE)empirically established engineering self-efficacy, teamwork self-efficacy, and identity as anengineer as mediating, person-centered motivational psychological, processes that transmit theeffect of programmatic support activities into an increased commitment to an engineeringcareer[4]–[8]. For the current work, we speculate that students with low measures of engineeringself-efficacy, teamwork self-efficacy, or engineering identity are good candidates for proactiveadvising intervention. Additional measures of non-cognitive and affective attributes may alsoprovide

Conference Session

Student Experiences and Development – Session 1

Collection

2023 ASEE Annual Conference & Exposition

Authors

Maham A Godil, Purdue University West Lafayette; Jennifer Deboer, Purdue University at West Lafayette (COE); Dhinesh Balaji Radhakrishnan, Purdue University at West Lafayette (COE)

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Diversity

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Educational Research and Methods Division (ERM)

in our courses to see students' attitudes towardengineering and analyze the engineering course progress.As the assessment team (authors), we develop new learning models and assessment methodsspecifically tailored to the LED program. These methods allow us to measure the effectivenessof the program in promoting engineering understanding and attitudes among students. Byanalyzing the results of our assessments, we provide instructors and researchers with valuableinsights into how the LED program can be improved and how it compares to other engineeringeducation programs. We are particularly interested in examining the influence of the LEDprogram on students' self-determination, motivation, and self-efficacy, as these factors haveshown to be

Conference Session

Disciplinary Engineering Education Research – Session 1

Collection

2023 ASEE Annual Conference & Exposition

Authors

Joreen Arigye, Purdue University; Alejandra J. Magana, Purdue University at West Lafayette (COE); Joseph A. Lyon, Purdue University; Elsje Pienaar

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Educational Research and Methods Division (ERM)

.[13] P. Yantraprakorn, P. Darasawang, and P. Wiriyakarun, “Enhancing self-efficacy through scaffolding,” Proceedings from FLLT, 2013.[14] A. Bandura, “Self-efficacy: toward a unifying theory of behavioral change.,” Psychological review, vol. 84, no. 2, p. 191, 1977.[15] R. M. Klassen and E. L. Usher, “Self-efficacy in educational settings: Recent research and emerging directions,” The decade ahead: Theoretical perspectives on motivation and achievement, vol. 16, pp. 1–33, 2010.[16] M. J. Scott and G. Ghinea, “Measuring enrichment: the assembly and validation of an instrument to assess student self-beliefs in CS1,” in Proceedings of the tenth annual conference on International computing education research, 2014, pp. 123

Conference Session

Educational Research and Methods Division (ERM) Technical Session 14

Collection

2024 ASEE Annual Conference & Exposition

Authors

Cory Lam, University of Washington; Denise Wilson, University of Washington

Tagged Topics

Diversity

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Educational Research and Methods Division (ERM)

following the COVID-19 pandemic) andremote (during the pandemic) learning settings in mechanical and electrical and computerengineering. Variables representing expectancy, value, and predictors of expectancy and valuewere integrated into hierarchical linear models to understand their influence on cognitiveengagement and to explore whether or not the expectancy-value model was stable over time inthe engineering education context. Consistent with expectancy-value theory, our results indicatedthat expectancy (measured by self-efficacy) and value (as measured by intrinsic and utility value)positively and significantly predicted cognitive engagement for all time periods. Previousacademic achievements as measured by overall GPA was also consistent across

Conference Session

Educational Research and Methods Division (ERM) Technical Session 12

Collection

2024 ASEE Annual Conference & Exposition

Authors

Matthew Bahnson, Purdue University; Eric Trevor McChesney, University of Pittsburgh; Carlie Laton Cooper, University of Georgia; Allison Godwin, Cornell University; Linda DeAngelo, University of Pittsburgh; Kevin R. Binning

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Diversity

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Educational Research and Methods Division (ERM)

differences in these relationships by studentrace and gender. The model includes engineering identity as directly predicted by self-efficacy,interest, and sense of belonging. Sense of belonging is likewise predicted by self-efficacy andinterest, generating additional indirect influences on engineering identity. Finally, a sense ofbelonging is further predicted by cross-racial and cross-gender belonging experiences. The strongrelationships between measures provide insight into the potential for interventions to improveengineering identity in early career engineering students. Future work to analyze the longitudinalchange in measures and identity in association with the intervention will further demonstratevariable relationships. Results provide

Conference Session

Educational Research and Methods Division (ERM) Technical Session 13

Collection

2024 ASEE Annual Conference & Exposition

Authors

Rachel E. Durham, Notre Dame of Maryland University; Michael L. Falk, The Johns Hopkins University; Alexis Daniels, The Johns Hopkins University; Allison Reigel, The Johns Hopkins University; Alisha Nicole Sparks, The Johns Hopkins University; Margo K. Williams, The Johns Hopkins University; Emily J Yanisko, American University

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Diversity

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Educational Research and Methods Division (ERM)

design was used where schools were assignedto either treatment or control conditions. Students in treatment schools accessed algebra-for-engineering modules, STEM-professional role model videos, and field trips, while students incontrol schools accessed role model videos and field trips only. Surveys measuring math self-efficacy, and STEM interest, outcome expectations, and choice goals were completed byparticipants in both conditions at the beginning and end of two separate program years, 2021-22and 2022-23. Across both years, quantitative results suggest some positive effects of BOASTparticipation, particularly for STEM choice goals, but benefits depend upon student participationlevels. Qualitative data offer student voice around prior

Conference Session

Educational Research and Methods Division (ERM) Technical Session 18

Collection

2024 ASEE Annual Conference & Exposition

Authors

Navarun Gupta, University of Bridgeport; Junling Hu, University of Bridgeport; Ioana A. Badara, Post University; Buket D. Barkana, The University of Akron; Deana A. DiLuggo, University of Bridgeport

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Diversity

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Educational Research and Methods Division (ERM)

specific questions and aspects of the engineering design process,brainstorming ideas, and actively engaging in research as a team. Observations have revealedstrong student engagement in course activities and evidence of faculty following the ARG model.4.3 EDSE InstrumentThe EDSE instrument is a 36-item questionnaire designed to measure students' self-conceptstoward engineering design tasks. It assesses four areas related to engineering identity developmentusing a scale of 0 to 100 (0 = low level; 50 = moderate level; 100 = high level). The areas assessedinclude: self-efficacy, motivation, expectancy, and anxiety. In each area the following engineeringdesign tasks were assessed: conducting engineering design, identifying a design need, researchinga

Conference Session

Educational Research and Methods Division (ERM) Technical Session 25

Collection

2024 ASEE Annual Conference & Exposition

Authors

Alexander Pagano, University of Illinois at Urbana - Champaign; Taylor Tucker Parks, University of Illinois at Urbana - Champaign; Saadeddine Shehab, University of Illinois at Urbana - Champaign

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Educational Research and Methods Division (ERM)

Design at the University of Illinois Urbana-Champaign. Part of our mandate is to support the integration of Human-Centered Design [12]–[17] concepts within the College of Engineering. This study is motivated by the design question,“How might we develop assessment tools to measure student learning of human-centeredengineering design over a four-year undergraduate degree?” To this end, self-efficacy has beenselected as an indicator of learning progress. While not a perfect analog for learning [18], self-efficacy has been shown to track with achievement in a variety of contexts including engineeringeducation [19]–[23]. For our purposes, self-assessment provides an accessible way to collect datawithout significant effort or cognitive load from our

Conference Session

Educational Research and Methods Division (ERM) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Laine Schrewe Ph.D., Otterbein University; Elena Joy Caruthers, Otterbein University

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Educational Research and Methods Division (ERM)

these beliefs are shaped by mastery experiences, socialpersuasion, vicarious experiences, and physiological experiences. In turn, these beliefs impactcognitive processes, motivational processes, affective processes, and selection processes [12].Related specifically to this study, self-efficacy can be explained as a measure of how confidentstudents are in their ability to complete their engineering coursework and become an engineer,with implications ranging from how they feel when they are working on their engineeringcoursework to whether or not they ultimately continue to pursue the field. Related to thephysiological experiences component of self-efficacy, stress can impact student’s self-efficacyand has been found to be a concern specifically

Conference Session

Educational Research and Methods Division (ERM) Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ribhav Galhotra, Nanyang Technological University; Panting Yu, The University of Edinburgh; Jiafei Wang, The Education University of Hong Kong; Ibrahim H. Yeter, Nanyang Technological University

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Educational Research and Methods Division (ERM)

different demographic groups.ResultsThe lowest reliability within this data set, seen in Table 2, is observed in the ‘Test Anxiety’ and‘Help Seeking’ scales. This could suggest that these are less important within the Southeast Asiancontext. Data represented by Pintrich [20] align with the ‘Help-Seeking’ aspect, displaying asimilar alpha coefficient of 0.52. However, on the ‘Test Anxiety’ scale, there is a significantdifference between the study’s 0.56 and Pintrich’s 0.80. That could suggest that test anxiety is notimportant within this region or it has become less important over the last 30 years since theappearance of the MSLQ. Self-efficacy is shown to be a reliable construct, with a measured 0.96alpha coefficient, which is higher in comparison

Conference Session

Exploration of Written and Team Communication

Collection

2023 ASEE Annual Conference & Exposition

Authors

D'Arcy Randall, Dept. of Mechanical Engineering, The University of Texas at Austin; Hyesun You, The University of Iowa; Daniel S. Puperi, The University of Texas at Austin; Thomas E. Lindsay, The University of Texas at Austin; Rhya Moffitt Brooke, The University of Texas at Austin

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Educational Research and Methods Division (ERM)

“illustrating quantitative results withqualitative findings” [25, p. 68]. Quantitative data from the surveys were analyzed to measurethe students’ self-efficacy in targeted writing skills. In addition, quantitative data from theassessed student writing samples were analyzed to measure improvements from draft to final,and from control to intervention. The qualitative data from students’ reflections supported thequantitative results.BME Laboratory Course, Writing Assignment, and Intervention BackgroundEntering BME students enroll in the laboratory course during their first year; for fall semesterstudents, it is one of their first university courses. Unlike the engineering undergraduates studiedin [12]-[13], most students in this study had not completed a

Conference Session

Educational Research and Methods Division (ERM) Technical Session 14

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sierra Outerbridge, University of Central Florida; Michelle Taub, University of Central Florida; Marino Nader, University of Central Florida; Sudeshna Pal, University of Central Florida; Ricardo Zaurin, University of Central Florida; Hyoung Jin Cho, University of Central Florida

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Educational Research and Methods Division (ERM)

learning strategies. These strategies require further investigation as they areincreasingly important to integrate within the classroom, especially for challenging STEM-basedcourses. By specifically fostering motivation and SRL, students can engage more effectivelywith the material, leading to improved learning outcomes. To investigate these components of thelearning process in engineering, we collected self-report measures of achievement goalorientation (motivation), general self-efficacy (motivation), and motivated strategies for learning(SRL) for 146 undergraduate engineering students in Thermodynamics.To better understand (1) the interconnected nature of these constructs for students and (2) theself-regulatory and motivational profiles of

Conference Session

Educational Research and Methods Division (ERM) Technical Session 12

Collection

2024 ASEE Annual Conference & Exposition

Authors

Xiao Ge, Stanford University; Mark Schar, Stanford University; Helen L. Chen, Stanford University; George Toye; Sheri D. Sheppard, Stanford University

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Educational Research and Methods Division (ERM)

’ workplace behaviors. The same Likert scale was used.Job roles: A few variables were included to measure job roles, including: working for a medium-or large-size business (relative to all the alternatives, coded as a 0-1 dummy variable), andmultiple choice questions about specific work functions (e.g., working in R&D, Design,Manufacturing, or Management roles) and career choices (e.g., Startup career).Self-efficacy measures: Self-efficacy measures people’s perceived confidence in their ability tosuccessfully perform tasks and activities in certain domains, and have shown to be importantpredictors of their work outcomes [74]. We use pre-established scales as detailed in [61] tocapture participants’ beliefs about their personal efficacies in four

Conference Session

Reviewing Emergent Topics and Theory in Engineering Education

Collection

2023 ASEE Annual Conference & Exposition

Authors

Alexander V. Struck Jannini, Purdue University, West Lafayette; Muhsin Menekse, Purdue University, West Lafayette

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Educational Research and Methods Division (ERM)

year. Another important finding was that expectancy beliefs werepositive predictors of academic achievement in the form of higher GPAs, while value beliefswere predictors of more concrete career plans. Another study that found differences based on sexwas looking into intelligence beliefs and social comparisons [50]. The results of this papershowed that strong self-efficacy, which relates to expectancy measures, was more beneficial tofemales than males regarding final course grades. Another interesting, and concerning, findingwas that intelligence growth mindsets had no correlational effect on self-efficacy for women andthat students concerned with social comparisons were just as detrimental to self-efficacy for bothmales and females. Social

Conference Session

Educational Research and Methods Division (ERM) Technical Session 21

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sarah A. Wilson, University of Kentucky; Ava Kay Huth, Iowa State University of Science and Technology; Sara Xochilt Lamer, University of Kentucky; Joseph H. Hammer; Matthew Whitwer, University of Kentucky

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Educational Research and Methods Division (ERM)

theoretical framework to identify the beliefs that mostaccurately predict behavior. In December 2021, a survey was conducted in the first-yearengineering program at a large public university with a predominantly White population (n = 452).The self-report survey instrument included measures of mental health help-seeking intention,attitude, perceived norm, personal agency, and outcome beliefs guided by the IBM. Respondentsexhibited high scores on scales measuring their attitude towards seeking help, perceived control,and self-efficacy. This suggests that, on average, first-year engineering students had positiveperceptions of their seeking help, felt in control of their decisions to seek help, and were confidentin their ability to seek help. Students

Conference Session

Educational Research and Methods Division (ERM) Technical Session 16

Collection

2024 ASEE Annual Conference & Exposition

Authors

Jasmine Smith, University of Florida; David J. Therriault, University of Florida; Jeremy A. Magruder Waisome, University of Florida

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Educational Research and Methods Division (ERM)

-awareness related to the dimensions of self-reflection and insight. In the literature, thedimension of self-awareness is often assessed as engineering self-efficacy. Self-efficacy is anindividual's belief in their capacity to act in the ways necessary to reach specific goals [20]. Inengineering education, studies have measured self-efficacy among engineering students relatedto engineering design [21], mathematics aptitude [22], and general and skill-specific engineering[23]. Nevertheless, self-efficacy is only one dimension of one’s overall self-awareness. We arguethat you cannot consider a single aspect of an engineer’s being, such as their efficacy, andneglect to assess how that contributes to their overall identity as an engineer (i.e., overall