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

ERM Technical Session: Developing Engineering Competencies III

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alexis Grace Daniels, Johns Hopkins University; Rachel E Durham

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Diversity

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Educational Research and Methods Division (ERM)

, interests, goals, and actions, that confidenceand competence are directly proportional—are insufficient, particularly with minoritized youth.Under some circumstances, students can develop a sense of self-efficacy that is not aligned withtheir actual proficiency. Those circumstances include distrust of adults in the school, awarenessof low-quality instruction, and lack of access to high-quality STEM courses. In this study,overinflated mathematics self-efficacy has negative repercussions. While intuitively low self-efficacy does not support persistence in STEM, prior research has found that high mathematicsself-efficacy (measured in high school) was associated with enrollment in a four-year institutionfor young Black women; however, this mathematics

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

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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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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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

ERM Technical Session: Quantitative Instrument Development

Collection

2025 ASEE Annual Conference & Exposition

Authors

Dina Verdín, Arizona State University, Polytechnic Campus

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Diversity

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Educational Research and Methods Division (ERM)

ways goals differentially impact students, I decided to investigate if, among 2engineering students, there were differences in how these goal orientations impacted Latina,Latino, and White engineering students’ self-efficacy and persistence beliefs. The aim of thiscomparison is to highlight the implications of using a theory and its related survey measures thatwere designed from the perspective of one group of students (i.e., White students).PurposeI take a Quantitative Critical Race Theory (QuantCrit) lens to situate and interpret my researchfindings specifically by answering the following research questions: RQ1. Given that AGT was developed

Conference Session

ERM Technical Session: Methods in Graduate Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nosakhare Iyobosa Idiaghe, University of Nebraska - Lincoln; Erin Johnson, Pennsylvania State University; Catherine G. P. Berdanier, The Pennsylvania State University; Jessica Deters, University of Nebraska - Lincoln

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Educational Research and Methods Division (ERM)

knowledge as its subdimensions [71]. Motivational CQ is thedrive to learn about and engage in culturally diverse settings broken down into intrinsic,extrinsic, and self-efficacy components [71]. Behavioral CQ is the ability to adapt one's verbaland nonverbal behavior to suit different cultural contexts, with subdimensions related to verbal,non-verbal, and speech acts [71]. The ECQS’s expanded structure and accessible format make itparticularly suitable for this study's focus on graduate research contexts. Empirical evidence ofvalidity of the ECQS, using data from 286 participants across 30 countries, demonstrated goodmodel fit through confirmatory factor analysis (CFA). The analysis provided evidence ofconvergent validity and discriminant validity

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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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

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

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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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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

ERM Technical Session: Strategies for Student Support

Collection

2025 ASEE Annual Conference & Exposition

Authors

Matthew Bahnson, Purdue University at West Lafayette (COE); Eric Trevor McChesney, University of Pittsburgh; Allison Godwin, Cornell University; Christian D Schunn, University of Pittsburgh; Linda DeAngelo, University of Pittsburgh

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Educational Research and Methods Division (ERM)

belongingintervention, programming self-efficacy, and course grade for first-year engineering students.Improving the retention of undergraduate students in engineering pathways requires clearframeworks that include predictors and influences on continued enrollment in engineering courses.The persistence of Black, Latiné, or Indigenous (BLI) students remains lower than their peers anddisproportionate to the U.S. population [1]. The persistence of engineering students remains amajor concern with BLI students demonstrating disproportionate attrition in comparison to Whiteand Asian peers. This increased attrition from engineering pathways is often related to systematicexclusion and marginalization in engineering environments [2]-[5]. While some progress has

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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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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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

ERM Technical Session: Strategies for Student Support

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sierra Outerbridge, University of Central Florida; Michelle Taub, University of Central Florida; Sudeshna Pal, University of Central Florida; Hyoung Jin Cho, University of Central Florida

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Educational Research and Methods Division (ERM)

and together,providing further nuance into how Hispanic/Latina/o/é/x students might differ between transferand non-transfer. Much of the present literature focuses exclusively on Hispanic/Latina/o/é/x ortransfer students (e.g.,[34]-[35]), and we build on the present insights within literature to bettersupport Hispanic/Latina/o/é/x Transfer students (HLT) (e.g., [36]-[39]).MethodsAt a large southeastern university in the United States, 152 students (n=152) in Dynamics (afundamental engineering course) completed entry and exit surveys in the Spring 2024 semester.The questions were the same at the beginning and end of the semester to assess students’ self-reported perceptions of pedagogical practices, SRL, motivation (measured by self-efficacy

Conference Session

ERM Technical Session: Examining Undergraduate Recruitment & Retention

Collection

2025 ASEE Annual Conference & Exposition

Authors

Matthew Currey, Rowan University; Juan M Cruz, Rowan University; Kaitlin Mallouk, Rowan University; Stephanie Farrell, Rowan University; Justin Charles Major, Rowan University

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Diversity

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Educational Research and Methods Division (ERM)

think like one. The factor of an advancedengineering degree is focused on students' intentions to pursue graduate studies in engineering.The factor of academic engineering confidence relates to students’ performance in coursework,exams, and comprehension. The factor of DEI components is associated with discussions ondiversity, equity, and inclusion among instructors and students. Later in this paper we willexamine how strong each factor is and how well the items measure the factors. The six factorsalign with Tinto’s four key elements of student motivation and persistence, serving assubcomponents of intensity and clarity of goal to graduate, self-efficacy, sense of belonging, andcurriculum perception, as illustrated in Figure 2

Conference Session

ERM Technical Session: Faculty Influences on Student Support

Collection

2025 ASEE Annual Conference & Exposition

Authors

Linda DeAngelo, University of Pittsburgh; Christa E. Winkler, Mississippi State University; Blayne D. Stone, University of Pittsburgh; Charlie Díaz, University of Pittsburgh

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Diversity

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Educational Research and Methods Division (ERM)

majority of respondents in the analytic sample identified asmen (n=92, 67.2% men; n=41, 23.0% women; n=1, 0.7% non-binary; n=3, 2.2% not reported).Both tenure stream/track (n=108, 78.8%) and non-tenure stream/track (n=29, 21.2%) facultywere represented. Among the tenure stream/track faculty, varying ranks were represented (n=23,21.3% assistant professor; n=11, 10.2% associate professor; n=73, 67.6% full professor; n=1,1.0% not reported).Instrument. A survey instrument was used to understand the ways that faculty take upresponsibility for driving DEIB changes, as well as their self-efficacy and readiness for change..The instrument included 7 scales (see Appendix) measuring various aspects of facultyperceptions of DEIB policies and practices

Conference Session

ERM Technical Session: Student Mental Health & Wellbeing I

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ava Kay Huth, Iowa State University of Science and Technology; Sarah A Wilson, University of Kentucky; Matthew Whitwer, University of Kentucky; Joseph H Hammer; Joseph Francis Mirabelli, University of Michigan

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Educational Research and Methods Division (ERM)

% Sexual Identity Heterosexual 401 85.9% LGBQ+ 56 12.0%There is no widely agreed-upon sample size requirement for latent class analysis, but previousresearch has indicated that common fit statistics perform adequately when N ≈ 300–1000. Modelsthat use fewer indicators and sufficiently well-separated classes may still produce acceptableresults with a sample size of less than 300 [9].Measures of Help-Seeking Mechanisms and Help-Seeking IntentionFive mental health help-seeking mechanisms (attitude, perceived norm injunctive, perceived normdescriptive, self-efficacy, and perceived control) were assessed in

Conference Session

Educational Research and Methods Division (ERM) Technical Session 29

Collection

2024 ASEE Annual Conference & Exposition

Authors

David Paul Harvie, Embry-Riddle Aeronautical University ; Kimberly A. Luthi, Embry-Riddle Aeronautical University ; Monica Surrency, Embry-Riddle Aeronautical University ; John K. Wilson, Embry-Riddle Aeronautical University

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Educational Research and Methods Division (ERM)

, vol. 15, no. 2, pp. 7-15, 2014.[7] S. B. Wilson and P. Varma-Nelson, "Small Groups, Significant Impact: A Review of Peer- Led Team Learning Research with Implications for STEM Education Researchers and Faculty," Journal of Chemical Education, vol. 93, pp. 1686-1702, 2016.[8] S. B. Wilson and P. Varma-Nelson, "Implementing Peer-Led Team Learning and Cyber Peer-Led Learning in an Organic Chemistry Course," Journal of College Science Teaching, vol. 50, pp. 44-50, 2021.[9] J. E. Klobas, S. Renzi and M. L. Nigrelli, "A scale for the measurement of self-efficacy for learning (SEL) at univeristy," Bocconi University, 2007.[10] K. Wilson, K. Luthi, D. Harvie and M. Surrency, "Strategies for Engagement of Non- Traditional Students

Conference Session

ERM WIP V: Assessing & Developing Competencies in Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tiantian Li, Purdue University at West Lafayette (COE); Kerrie A Douglas, Purdue University – West Lafayette (College of Engineering); Nimota Nasiru, Purdue Engineering Education; Stephanie Masta, Purdue University at West Lafayette (PPI)

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Educational Research and Methods Division (ERM)

factors that hidden curriculum stands on and use them to identify and understand themechanism of hidden curriculum. These key factors include emotions, self-efficacy, self-advocacy, and awareness [14], [15]. More specifically, Villanueva et al.’s model describes that anindividual recognizes hidden curriculum through hidden curriculum awareness, which isprocessed by emotions. Emotions are then regulated by self-efficacy, which ultimately sustainsand reinforces the individual’s self-advocacy. While Villanueva et al.’s conceptual model isfocused on the coping mechanism upon discovering hidden curriculum, our study usesVillanueva et al.’s work on identifying hidden curriculum in engineering classroom exams basedon the described mechanism.Examining

Conference Session

Educational Research and Methods Division (ERM) Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Bruce Frederick Carroll, University of Florida; Janice Mejía, Northwestern University; Kent J. Crippen, University of Florida; Sheila Castro, University of Florida

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Educational Research and Methods Division (ERM)

Paper ID #42415Latina Engineering Student Graduate Study Decision Processes—Developmentand Initial Results of a Mixed-Methods InvestigationDr. Bruce Frederick Carroll, University of Florida Dr. Carroll is an Associate Professor of Mechanical and Aerospace Engineering at the University of Florida. He holds an affiliate appointment in Engineering Education. His research interests include engineering identity, self-efficacy, and matriculation of Latin/a/o students to graduate school. He works with survey methods and overlaps with machine learning using quantitative methods and sequential mixed methods approaches.Dr. Janice

Conference Session

ERM Technical Session: Equity & Accessibility in Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Eric Trevor McChesney, University of Pittsburgh; Blayne D. Stone, University of Pittsburgh; Charlie Díaz, University of Pittsburgh; Kelly Tatone, University of Pittsburgh; Gerard Dorvè-Lewis, University of Pittsburgh; Linda DeAngelo, University of Pittsburgh

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Educational Research and Methods Division (ERM)

oftenexpress concern about discussing race in the classroom [35] due to a lack of self-efficacy anduncertainty regarding their ability to authentically connect with students. Despite these concerns,research demonstrates that explicitly discussing race as a factor in engineering experiences andpathways is crucial for creating change within the discipline and validating the experiences ofstudents of color [36], [37], [32]. Adopting race-evasive approaches to engineering teaching andmentoring can be harmful to students of color [38], [39], further accentuating the necessity ofenhancing faculty self-efficacy for inclusive change. A final concern regards the difficult andoften inequitably distributed expectations of engagement in equity work among

Conference Session

Student Teams and Teamwork

Collection

2023 ASEE Annual Conference & Exposition

Authors

Yiyi Wang, San Francisco State University; Xiaorong Zhang, San Francisco State University; Fatemeh Khalkhal; Stephanie Claussen, San Francisco State University; Ana Karen Biviano

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Educational Research and Methods Division (ERM)

through P3). Also, students who reported better interactions withteammates (B.5) had a stronger sense of self-efficacy in engineering classes in a statisticallysignificant way. Except for interactions with teammates, all behavior metrics were positively andsignificantly linked to the EI dimension that measured how much they were perceived as a goodengineer by their professors and peers. Similarly, when a student was perceived as a goodengineer by their peers, he or she tended to do a better job keeping the team on track (B.2), at asignificance level of 0.001. Results were detailed in Appendix Table A.4.Teamwork behaviors were linked to team conflicts in modest ways. Students who ratedthemselves lower on interactions with teammates tended to

Conference Session

Educational Research and Methods Division (ERM) Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Autumn R. Deitrick, Pennsylvania State University; Catherine G. P. Berdanier, Pennsylvania State University

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Educational Research and Methods Division (ERM)

Small and Big-C creativity in Poland,” The International Journal of Creativity &Problem Solving, vol. 19, pp. 7-26, 2009.[45] J. C. Kaufman and R. A. Beghetto, “Beyond big and little: The four c model ofcreativity,” Review of general psychology, vol. 13, no. 1, pp. 1-12, 2009.[46] M. Karwowski, I. Lebuda, and E. Wiśniewska, “Measuring creative self-efficacy andcreative personal identity,” The International Journal of Creativity & Problem Solving, 2018.[47] P. Tierney and S. M. Farmer, “Creative self-efficacy development and creative performanceover time,” Journal of applied psychology, pp. 96, no. 2, 2011.[48] A. Bandura, Social foundation of thought and action: A social cognitive theory, EnglewoodCliffs, NJ: Prentice-Hall, 1986.[49] T

Conference Session

Educational Research and Methods Division (ERM) Technical Session 12

Collection

2024 ASEE Annual Conference & Exposition

Authors

Hank Boone, University of Nevada, Las Vegas; Danxu Wang, University of Nevada, Las Vegas; Emma Regentova, University of Nevada, Las Vegas; Venkatesan Muthukumar, University of Nevada, Las Vegas; SJ Kim, University of Nevada, Las Vegas; Jonathan Hilpert, University of Nevada, Las Vegas

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Educational Research and Methods Division (ERM)

in today will be important for my future goals”. Interest wasdefined as interest in the subject material. An example of Interest is “I found fulfillment in doingengineering ”.Self-perceptions and definitions were operationalized as students’ personal and social attributeswhile learning. Two underlying factors were used to measure self-perception and definitions:Self-efficacy (3 items; α= .83; ω=.86) and Self-concept (3 items; α= .73; ω=.78) [26-28]. All self-perceptions and definitions questions were listed in one block with the following prompt “Pleaseconsider how confident you were today in the camp”. Self-efficacy was defined as students’ self-assessment in solving content related problems. An example of Self-efficacy is “I

Conference Session

ERM Technical Session: Strategies for Student Support

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jan Edwards, College of Lake County; Carrie Kortegast, Northern Illinois University

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Educational Research and Methods Division (ERM)

in community college, most participants stated they did not yet identify as an engineer.However, they felt that faculty recognized their potential to become an engineer even when theywere struggling. Wang [27, p. 37] described these interactions as compassion enhancedpedagogy when reflecting on changes in the classroom during the pandemic. She furtherobserved that faculty were more aware of student perseverance driven by their hopes forthemselves.The above outcomes of increased technical understanding and improved confidence align withprevious research that building a student’s self-efficacy prior to transfer is essential [28]-[30].Acknowledging that several of the students repeated a critical math course prior to successfullytransferring

Conference Session

Educational Research and Methods Division (ERM) Technical Session 21

Collection

2024 ASEE Annual Conference & Exposition

Authors

Trevion S. Henderson, Tufts University; Collette Patricia Higgins, Tufts University

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Educational Research and Methods Division (ERM)

relationships.The research represents a preliminary analysis of data examining the role of students’ socio-academic relationships in their learning in undergraduate science and engineering education. Thebroader study also examines sociocognitive influences, such as self-efficacy beliefs andacademic adjustment, in students’ socio-academic experiences. While findings from thispreliminary analysis appear to undermine research that has consistently documentedunderrepresented minorities (URM) students’ negative experiences in STEM classroomsbroadly, and within engineering classrooms specifically, we intend to analyze these andadditional data using social network analysis, which we believe may be better suited forunderstanding students’ socio-academic

Conference Session

Investigating Student Pathways to and through Undergraduate and Graduate Programs

Collection

2023 ASEE Annual Conference & Exposition

Authors

Joyce B. Main, Purdue University; Tram Dang, Purdue University; Beata Johnson, Purdue University at West Lafayette (COE); Qian Shi, Purdue University; Cesare Guariniello, Purdue University; Daniel Delaurentis, Purdue University

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Educational Research and Methods Division (ERM)

study following over 23,000 students from 2009 to 2016.The data were analyzed using multiple regression analyses to correlate high school,demographic, academic achievement factors from the 2009 and 2012 data collection waves to astudent’s likelihood of attending college and majoring in a STEM field. The high school levelfactors that were found to be significant predictors for college STEM major declaration includethe student’s family background, high school STEM GPA, and measures for math/scienceidentity. The findings are mixed and suggest further research is needed, particularly indisaggregating the math/science self-efficacy, identity, and utility measures, as well as ininvestigating potential differences in major choice by field separately

Conference Session

Problem- and Project-Based Learning

Collection

2023 ASEE Annual Conference & Exposition

Authors

Micah Lande, South Dakota School of Mines and Technology

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Educational Research and Methods Division (ERM)

their way through these learning experiences too, issomething that provides, in the face of ambiguity, to create agency and build self-directedlearners: “Yeah, so I think there are there are course aspects and there's general, general thought process that come through. So, one of the big themes … is self-efficacy. Given a problem, figure out how to solve it. Right. It’s open ended. Is that right? You may not be the technical expert. You may not be even aware of the background, but it's on you to figure it out. And you can do it right. You don't you don't need to be an expert to solve a problem. This whole project-based learning thing, sort of coupling design thinking leads to this increase in self

Conference Session

Educational Research and Methods Division (ERM) Technical Session 28

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mitchell Gerhardt, Virginia Polytechnic Institute and State University; Nicole P. Pitterson, Virginia Polytechnic Institute and State University; Emily Dringenberg, The Ohio State University; Benjamin Ahn, The Ohio State University

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Educational Research and Methods Division (ERM)

.” Ultimately,perceived norms are shaped by an individual’s perception of other’s attitudes toward thebehavior and social expectations about the consequences of the behavior – critical components ofintention.The third component, perceived behavior control, encompasses individuals’ perceptions of theircapacity or control over executing a specific behavior. This concept aligns with the notion ofself-efficacy [36], where actions are contingent upon one’s belief in their capability to performthem, as acknowledged the authors: “It can be seen that our definition of perceived behavioralcontrol…is very similar to Bandura’s conception of self-efficacy” [4, p. 155]. In this manner, theRAA connects to behavioral theories commonly employed in engineering