Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Doris Espiritu, Wright College; Ruzica Todorovic; Bridget O'Connell

. Schwarzer and M. Jerusalem, “Generalized self-efficacy scale. Measures in Health Psychology: A User's Portfolio”, Causal and Control Beliefs, vol. 1, pp. 35-37, 1995.[30] D. J. Espiritu, B. O'Connell and D. Potash, “Equity, Engineering, and Excellence: Pathways to Student Success”, in 2021 ASEE Virtual Annual Conference, Virtual Conference, 2021.[31] Excelencia in Education, “10 Trendsetting Institutions Certified with Seal of Excelencia for Intentionally Serving Latino Students”, 29 October 2021. [Online]. Available: 12 https://www.edexcelencia.org/press-releases/10-trendsetting-institutions-certified-seal-excelencia

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Jeremy Ernst, Embry-Riddle Aeronautical University - Worldwide; Brenda Brand, Virginia Polytechnic Institute and State University; Xiao Zhu, Virginia Polytechnic Institute and State University

year of a permanent faculty position. Toinvestigate the impact of the initial cohort Alliance the participant study research question, “Howdoes the project model implementation affect student participants’ a) self-efficacy, b) researchautonomy, and c) perceptions of social support networks?” was explored.MethodologyData collection for the purposes of the initial participant study involved 9 ABD participants.Again, each participant is an HBCU instructor who is classified as ABD within an existingSTEM doctoral program. Self-efficacy, research autonomy, and social support network data wascollected in pre-assessment and post-assessment format for the project implementationparticipants. Research self-efficacy was measured through the existing Self

Conference Session

DEED Technical Session 3 Capstone Design

Collection

2022 ASEE Annual Conference & Exposition

Authors

Austin Talley, Texas State University; C. Compeau, Texas State University

utilized the Makerspace as compared to other traditional EE projectsin the control group, and their design self-perception via administration of the Measuring EngineeringDesign Self-Efficacy survey. Fabrication and performance details such as insights into electroplating,characterized waveguide VSWR and insertion loss, and horn antenna radiation pattern as simulated vs.measured will also be stated.IntroductionMany of our EE Senior Design projects have limited scope beyond traditional EE design activities. Thesetraditional activities have consisted of audio projects (such as headphone amplifiers or a mixer), sensingand data gathering platforms (water quality or air temperature), child protection systems, and robotics.Robotic competitions have

Conference Session

Aerospace Division Technical Session: Student Success

Collection

2022 ASEE Annual Conference & Exposition

Authors

Chadia A. Aji, Tuskegee University; M. Javed Khan, Tuskegee University

intrinsic value dimension. Figure 9a: During Covid responses of AENG students by gender Figure 9b: During Covid % change in responses of AENG students by genderFor a better understanding of the changes in the perceptions of the students and the efficacy ofthe strategies used pre-COVIDand with modification duringCOVID as measured by theMSLQ, a comparative analysiswas done (Fig. 10, Table III).The data for all the participants(Fig. 10a) indicated that thevarious dimensions wereimpacted differently pre-COVID and during COVID.The % change in the average ofresponses increased in the self-efficacy and self-regulationdimensions during the remotelearning during COVID.However, the % change inaverage was reduced for Figure

Conference Session

We Love our MOMs (Mechanics of Materials)

Collection

2022 ASEE Annual Conference & Exposition

Authors

Casey Kidd, Louisiana Tech University; Ethan Hilton, Louisiana Tech University

, “The effect of authentic project‐based learning on attitudes and career aspirations in STEM,” J. Res. Sci. Teach., vol. 56, no. 1, pp. 3–23, Jan. 2019, doi: 10.1002/tea.21465.[9] N. A. Mamaril, E. L. Usher, C. R. Li, D. R. Economy, and M. S. Kennedy, (2016). “Measuring undergraduate students' engineering self‐efficacy: A validation study.” Journal of Engineering Education, vol. 105 no. 2, pp. 366-395, 2016. Appendix A General Engineering Self-EfficacyGen-1 I can master the content in the engineering-related courses I am taking this quarter.Gen-2 I can master the content in even the most challenging engineering course.Gen-3 I can do a

Conference Session

ERM: Find Out More About Faculty!

Collection

2022 ASEE Annual Conference & Exposition

Authors

Alejandro Espinal; Alejandra Magana, Purdue University at West Lafayette (COE); Camilo Vieira, Fundacion Universidad del Norte

educationalrobotics: An interaction effect between gender and scaffolding strategy. Computers in humanbehavior, 105, 105954.Byrne, B. M. (1996). Academic self-concept: Its structure, measurement, and relation to academicachievement.Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and whatis the role of the computer science education community?. Acm Inroads, 2(1), 48-54.Boulden, D. C., Rachmatullah, A., Oliver, K. M., & Wiebe, E. (2021). Measuring in-service teacher self-efficacy for teaching computational thinking: development and validation of the T-STEM CT. Educationand Information Technologies, 1-27.Dusick, D. M. (1998). What social cognitive factors influence faculty members’ use of computers

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Jacqueline Genovesi, Drexel University; Ian Marcus, Drexel University; Kimberly Sterin, Drexel University; Dominique Thomas, Drexel University

justifiableconclusions through triangulation, complementarity, development, initiation, and expansion [58]Study sample sizes are: 24 EngWINS students who experience both the curriculum andmentoring; 26 students who only experience the curriculum; and 24 EngWINS adult mentors.Quantitative Methods and Primary Sources-Instruments: We will examine changes in theEngWINS students’ interests and general dispositions toward engineering, through: 1) theStudent Attitudes toward STEM Survey [59] and 2) the STEM career inventory survey [60](STELAR site) to measure changes in young women’s self-efficacy in STEM, interest in STEMcareers, and 21st century learning skills.Quantitative Data Collection and Analysis: Baseline/pretest and posttest surveys wereadministered via

Conference Session

Community Engagement Division Technical Session 4- COVID and Virtual Learning

Collection

2022 ASEE Annual Conference & Exposition

Authors

Jessica Smith, Colorado School of Mines; Juan Lucena, Colorado School of Mines; Angelina Rivera, Colorado School of Mines

] A. Bandura, “Self-efficacy: Toward a unifying theory of behavioral change,” Psychol. Rev., vol. 84, no. 2, pp. 191–215, 1977, doi: 10.1037/0033-295X.84.2.191.[3] N. A. Mamaril, E. L. Usher, C. R. Li, D. R. Economy, and M. S. Kennedy, “Measuring Undergraduate Students’ Engineering Self-Efficacy: A Validation Study,” J. Eng. Educ., vol. 105, no. 2, pp. 366–395, 2016, doi: 10.1002/jee.20121.[4] M. A. Hutchison-Green, D. K. Follman, and G. M. Bodner, “Providing a Voice: Qualitative Investigation of the Impact of a First-Year Engineering Experience on Students’ Efficacy Beliefs,” J. Eng. Educ., vol. 97, no. 2, pp. 177–190, 2008, doi: 10.1002/j.2168-9830.2008.tb00966.x.[5] R. M. Marra, K. A. Rodgers, D. Shen, and B. Bogue

Conference Session

Pre-College Engineering Education Division Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Betsy Chesnutt, University of Tennessee at Knoxville; Courtney Faber, University of Tennessee at Knoxville; Daniel Mountain

Proceedings, 2010. 9. National Center for Education Statistics (NCES). 2018 National Teacher and Principal Survey: Overview. Available online at https://nces.ed.gov/surveys/ntps/overview.asp (accessed 10/30/2020). 10. S.Y. Yoon, M.G. Evans, J. Strobel. “Validation of the Teaching Engineering Self- Efficacy Scale for K-12 Teachers: A Structural Modeling Approach.” Journal of Engineering Education 103(3), 2014. 11. S.Y. Yoon, M.G. Evans, and J. Strobel, J. “Development of the Teaching Engineering Self-Efficacy Scale (TESS) for K-12 Teachers.” ASEE Annual Conference and Exposition, Conference Proceedings, 2012. 12. M. Tschannen-Moran, A.W. Hoy, and W. K. Hoy. “Teacher Efficacy: Its Meaning and Measure

Conference Session

ERM: ERM Medley Session!

Collection

2022 ASEE Annual Conference & Exposition

Authors

Tina Wang; Laura Jun Chee Yong, Pennsylvania State University; Allison Godwin, Purdue University at West Lafayette (COE); Linda Hanagan, Pennsylvania State University

the learning outcomes of themodule activities. Modules are categorized into introduction, exposure to professional engineers,meaningful engineering learning and career development. Refer to Table 1 for the summary ofmodules, targeted constructs, and category of the activities.Planned MethodsWe are currently developing and adapting existing quantitative and qualitative measures tounderstand how the MSAEPP affects students’ self-efficacy, identities, and motivations towardsSTEM careers. Our initial measures are mapped to each module and described in more detailbelow (Figure 2). Figure 2. Proposed research methods for understanding the impact of the MSAEPP on learners.Draw an Engineer Tool (DAET)The Draw an Engineer Test (DAET) is both a written

Conference Session

Pre-College Engineering Education Division Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Frank Bowman, University of North Dakota; Bethany Klemetsrud; Emine Ozturk; Julie Robinson, University of North Dakota

they lackthe training and self-efficacy to effectively implement open-ended engineering problem-solvingexperiences in their classrooms. This is additionally difficult for schools in rural and NativeAmerican settings, as resources and support may be limited [3]. Curriculum is often presentedfrom a Western framework that does not incorporate cultural knowledge, values, and beliefsembedded in a community [4]. Oftentimes, engineering design tasks are thought of as aculturaland devoid of community inclusion and values. However, engineering design is inherently acultural endeavor as problems needing engineering solutions or design thinking are situated in aspecific community and need community solutions. Furthermore, the engineering design

Conference Session

ETD - A Technology Potpourri I

Collection

2022 ASEE Annual Conference & Exposition

Authors

Osazuwa Okundaye, Texas A&M University; Malini Natarajarathinam, Texas A&M University; Mathew Kuttolamadom, Texas A&M University; Francis Quek, Virginia Polytechnic Institute and State University; Sharon Lynn Chu, University of Florida; Qing Li; Shaoping Qiu, Texas A&M University

students were male. Students’ ranged in age range 14 to 18.MeasuresA single pre-post measures was administered before the start and end of the 3-week class project.The Academic Self-Description Questionnaire II (ASDQ-II) 39 includes a sub-scales based onparticular subjects of interest and self-efficacy towards them including computer studies,mathematics, industrial arts, science, and general day-to-day self-efficacy. Each sub-scalecomposes of 8 items that assess identification with and perceived efficacy in academics, forexample for the case of ”Computer Studies”, items would include assessment statements such as“Compared to others my age I am good at COMPUTER STUDIES classes”). The ASDQ-IIfollows a 8-point Likert scale where participants are

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Hannah Huvard, New Mexico State University; Hengameh Bayat, New Mexico State University; Sandra M. Way, New Mexico State University; Catherine Brewer, New Mexico State University; Addison Miller; Antonio Garcia, New Mexico State University

59.4% 62.6% Low Income 50% 44.4% First Generation 32% 34.3%Survey Instrument and Measures The survey included demographic questions (age, gender, socioeconomic status, andfirst-generation status), as well as three scales related to students’ self-reported levels ofengineering identity, self-efficacy, and SDL. The engineering identity scale was adapted fromGodwin’s [7] Measure of Engineering Identity, which includes questions related to students’interest in engineering, recognition for contributions to their field by self and others, andperceptions of competence and performance in engineering. General engineering self-efficacywas

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Bimal Nepal, Texas A&M University; Michael Johnson, Texas A&M University; Amarnath Banerjee, Texas A&M University; Glen Miller

school STEM teachers. Working with universityfaculty and graduate students, these teachers will develop learning modules on ethical issuesrelated to their courses. The snapshot will also identify gaps and guide the creation of targetedinterventions that will be used in second-, third-, and fourth-year engineering courses.This data-driven project uses a mixed-methods approach to generate a better understanding of theimpact of ethics interventions at various points in a student's academic development by developingand using a set of instruments to measure cognitive, affective, and behavioral aspects of ethicalcompetency and self-efficacy. To that end, a second snapshot will be taken by testing andsurveying engineering students in their capstone

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Ignatius Fomunung, University of Tennessee at Chattanooga; Christopher Silver, University of Tennessee at Chattanooga; Marcy Porter, University of Tennessee at Chattanooga

of Belonging and Self Efficacy Sense of Belonging* Self-Efficacy** Pre-Program Mid-Program Pre-Program Mid-Program First-Year Average 3.7/5 3.8/5 4.0/5 3.8/5 (N=32| 32% response rate) Mentor Average (N=7| 23% 4.1/5 4.4/5 4.4/5 4.6/5 response rate)*Average of responses to three peer-reviewed sense of belonging questions, measured on a five-pointLikert scale. (Ex: “I feel loke an important member of my school community”)** Average of responses to three peer-reviewed sense of belonging questions, measured on a five-pointLikert scale

Conference Session

Entrepreneurship & Engineering Innovation Division Technical Session 2

Collection

2022 ASEE Annual Conference & Exposition

Authors

Ying Wang, Georgia Institute of Technology; Joy Harris; Janece Shaffer

. 4, pp. 880–895, 2010, doi: 10.1037/a0019506.[19] H. Piesch, H. Gaspard, C. Parrisius, E. Wille, and B. Nagengast, “How can a relevance intervention in math support students’ career choices?,” J. Appl. Dev. Psychol., vol. 71, p. 101185, Nov. 2020, doi: 10.1016/j.appdev.2020.101185.[20] M. Hartwell and A. Kaplan, “Students’ Personal Connection with Science: Investigating the Multidimensional Phenomenological Structure of Self-Relevance,” J. Exp. Educ., vol. 86, no. 1, pp. 86–104, Jan. 2018, doi: 10.1080/00220973.2017.1381581.[21] J. E. McGee, M. Peterson, S. L. Mueller, and J. M. Sequeira, “Entrepreneurial Self– Efficacy: Refining the Measure,” Entrep. Theory Pract., vol. 33, no. 4, pp. 965–988, Jul. 2009, doi

Conference Session

ERM: Diversity, Equity, and Inclusion

Collection

2022 ASEE Annual Conference & Exposition

Authors

Blaine Pedersen, Texas A&M University; Karen Rambo-Hernandez, Texas A&M University

Bandura’s [9] social cognitive theory, stating that motivation isgoal-directed behavior. Behaviors are produced and sustained by the anticipated consequences ofone’s actions (outcome expectations; OEE), a person’s judgment of their ability to attain theirgoals (self-efficacy; SE), and their career-oriented interests [9], [10]. Pertinent to the career-oriented goals people set is the degree to which they feel their values are congruent with theirwork, which is an aspect of outcome expectations [11]. Further, the effect of outcomeexpectations on career-oriented goals is expected to be mediated by students’ career-relevantinterests.Figure 1. Path diagram of the Social Cognitive Career Theory.In the seminal work establishing the SCCT, Lent et al. [11

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

George Tan, Texas Tech University; Luke LeFebvre, University of Kentucky; Tim Dallas, Texas Tech University; Changxue Xu, Texas Tech University; Jnev Biros

engineering students were expected to work with first-year medical students todevelop innovative solutions for clinical problems. Faculty members from both institutes servedas mentors for student projects. The education objectives are twofold: (1) to develop the criticalthinking skills and independent research ability by solving engineering problems throughauthentic projects, and (2) to increase students’ non-cognitive learning outcomes such ascommitment and engagement with engineering, communication (writing and presentation skills),self-efficacy, and teamwork in a multidisciplinary environment. The first class of this biomedical innovation course started in the fall of 2021. The completeteaching plan comprises two consecutive courses in fall and

Conference Session

First-Year Programs Division Technical Session 10: Best of First-Year Programs Division

Collection

2022 ASEE Annual Conference & Exposition

Authors

Campbell Bego, University of Louisville; Pamela Thomas; Xiaomei Wang, Texas A&M University; Arinan Dourado, University of Louisville

interventions. Here, threemachine learning tools, namely, clustering, principal component analysis (PCA), and decisiontrees, were applied to data from two cohorts of engineering students at a large public university.Concerning the SEVT framework, student responses to surveys given at the beginning and endof the first semester, containing established scales for self-efficacy and contingencies of academiccompetence self-worth (expectancies), and interest in engineering and perceived costs of studyingengineering (subjective task values) were used. Demographic data including race, gender, and Pelleligibility, alongside performance data in the form of introductory course grades, GPA, and per-sistence into Year 2, complete the set of gathered information

Conference Session

Thinking Outside the STEM Box: Equity, Culture & Social Justice in Education Division Technical Session 1

Collection

2022 ASEE Annual Conference & Exposition

Authors

Jan Fertig, Milwaukee School of Engineering; Subha Kumpaty, Milwaukee School of Engineering

persistence and perceived level ofprogram fairness (e.g., lack of discrimination based on race, gender, sexual orientation or anybasis). High discrimination/low fairness departments were characterized by notable prejudiceand discrimination, while low discrimination/high fairness departments were characterized bygender-blindness, equity, respectful treatment of students and greater trust. The relationshipbetween program fairness and persistence was moderated by level of individual empathy. Inother words, individual empathy enhanced or buffered the effect of program discrimination onpersistence. (Individual empathy scores did not differ by engineering program). Under conditionsof low fairness and low STEMpathy, engineering self-efficacy was low

Conference Session

Understanding Inclusivity and Equity in STEM Contexts: Equity, Culture & Social Justice in Education Division Technical Session 7

Collection

2022 ASEE Annual Conference & Exposition

Authors

Benjamin Lutz, California Polytechnic State University, San Luis Obispo; Steffen Peuker, California Polytechnic State University, San Luis Obispo

. As a team, we decided it was a critical item that should beretained, if possible, in order to assess student self-efficacy on these kinds of tasks. Further, if theitem was removed, the subscale related to perceived behavioral control would be measured byonly two items and negatively impact its reliability.7EFA Second IterationWe then prepared the data for a subsequent round of EFA with fewer variables. Table 3 belowshows the rotated factor solution for the second major iteration of EFA and the instrument that weplan to implement in upcoming academic terms for further validation.Table 3: Second iteration EFA results Item (from original 23 item ESJS) Factor

Conference Session

NEE Technical Session - the Best of NEE

Collection

2022 ASEE Annual Conference & Exposition

Authors

Sarah Wodin-Schwartz, Worcester Polytechnic Institute; Kimberly Lechasseur, Worcester Polytechnic Institute; Adam Powell, Worcester Polytechnic Institute; Yihao Zheng, Worcester Polytechnic Institute; Sneha Prabha Narra, Carnegie Mellon University

?” The five items related to self-efficacy are: “Enthusiasm forengineering,” “Interest in taking or planning to take additional engineering classes,” “Confidencethat you understand the material,” “Confidence that you can do statics work,” and “Your comfortlevel in working with complex ideas.” Response options were a five-point Likert scale from “nogains” to “great gains.” These five items are moderately to highly positively correlated with eachother (see Table 1) and have high internal reliability, with a Cronbach’s alpha of .93; togetherthese suggest the items can be combined to constitute a single measure that is a stronger signal ofself-efficacy than each individual item.Table 1. Correlations of Items in Self-Efficacy Measure Item

Conference Session

First-Year Programs Division Technical Session 8: Academic Progress, Retention, and Mathematics

Collection

2022 ASEE Annual Conference & Exposition

Authors

Yanfen Li, University of Massachusetts Lowell; Na'imah White, University of Massachusetts Lowell; Karoline Evans, University of Massachusetts Lowell; Douglas Correa Ospina, University of Massachusetts Lowell

points in time: Time 1—the beginning of the semester—and Time 2—the end of the semester.Each student received a link to a Qualtrics survey that was collected by a member of the research team who was not involved with thecourse. At Time 1, we measured general self-efficacy, Big 5 personality traits, leadership schema, followership role orientations,engineering identity, sense of belonging in the engineering major, engineering self-efficacy, and demographics. At Time 2, we againmeasured general self-efficacy, engineering identity, sense of belonging in the engineering major, and engineering self-efficacyquestions to compare to Time 1. We also included at Time 2 scales for voice, belonging to group, psychological closeness, socialworth, friendship

Conference Session

ERM: Persistence and Attrition in Engineering

Collection

2022 ASEE Annual Conference & Exposition

Authors

Cassie Wallwey, The Ohio State University; Giselle Guanes, The Ohio State University; Tyler Milburn, The Ohio State University; Jeremy Grifski, The Ohio State University

person’s information on an online socialnetwork can be accessed by others and can be shared, blocked, or used to identify them for the network’sown purposes, resulting in an increased perception of being excluded on these online social networks.Considering the context of exclusion in engineering, we have adapted this predictor to consider a person’sbeliefs of their self-efficacy related to engineering and how it affects their motivation to continue inengineering.Similarly to Bardakcı’s (2018) work, we are investigating how these beliefs can affect how a person mayfeel excluded from engineering if their beliefs about themselves do not align with what they believe isnecessary to be an engineer. Previous research has shown some students leave STEM

Conference Session

Joint Session: Entrepreneurially-Minded Learning in the Classroom

Collection

2022 ASEE Annual Conference & Exposition

Authors

Timothy Reissman, University of Dayton; Vinayak Vijayan, University of Dayton; Shanpu Fang; Megan Reissman, University of Dayton; Skyler Miller, University of Dayton

) group(N=52) was defined as those students achieving or exceeding a mean of 96% on ‘making’assignments, while the remainder (N=21) were defined as the Low Participation (LP) group. Bycreating this distinction, the impact of ‘making’ can be compared within the same course basedon student relative engagement in the learning process. Beyond summative results from thestudents, additional data was collected via pre- and post- surveys. The pre-survey gatheredinformation at the start of the semester on prior experiences related to the course and on studentperception of self-efficacy in engineering design related to the course. The post-survey gatheredinformation in the final week of the semester on time spent performing the assignments andagain on the

Conference Session

College Industry Partnerships Division Technical Session 2

Collection

2022 ASEE Annual Conference & Exposition

Authors

Adrian Gentry; Peter Bermel, Purdue University at West Lafayette (COE); Eric Holloway, Purdue University at West Lafayette (COE); Kerrie Douglas, Purdue University at West Lafayette (COE)

., 2020), minoritizedgender and sexual identities (Tatum, 2018), first generation undergraduate students (Garriott etal., 2017), people with disabilities (Pham et al., 2020), and low socioeconomic status individuals(Pulliam et al., 2017). Additionally, SCCT has been heavily used to understand the career pathsof historically underrepresented populations in STEM fields (Fouad & Santana, 2017; Hardin &Longhurst, 2016; Lent et al., 2018; Turner et al., 2019). SCCT explains how career choice is formed based on five key factors: self-efficacy,outcome expectations, personal interests, choice goals and actions, and performance domains andattainments. In SCCT, self-efficacy and career outcome expectations, in combination withenvironmental

Conference Session

Virtual and Augmented Reality Applications in Manufacturing Education

Collection

2022 ASEE Annual Conference & Exposition

Authors

John Liu, Massachusetts Institute of Technology; Emma Higgason, Massachusetts Institute of Technology; Emily Welsh, Massachusetts Institute of Technology; Joseph Wight; A. John Hart; Gabrielle Enns

activity measured affective outcomes and consisted oftwenty-one questions on a 7-point Likert scale (1- “not at all true of me” to 7 – “very true ofme”) adapted from the Motivated Strategies for Learning Questionnaire (MSLQ) [11]. Twelvequestions were written to check for three types of self-efficacy: an individual's belief in one’scapacity to learn the content (3 questions), apply the necessary skills to equipment (5 questions),and to perform well in the class (4 questions). Five questions checked the students' motivation tore-engage with the content and four questions measured their fear of making mistakes. Eachtheme was covered by multiple questions to measure the average over multiple questions tonormalize for variation in question phrasing

Conference Session

Student Division Technical 1: Diversity, Equity, Inclusivity (DEI)

Collection

2022 ASEE Annual Conference & Exposition

Authors

Cassandra McCall, Utah State University; Layla Araiinejad, Auburn University; Thomas Heaps, Utah State University; Wade Goodridge, Utah State University; Brooke Cochran, University of Colorado Boulder

students’ priorknowledge to create a more inclusive learning environment that values and respects students’individual needs and identities.Theoretical FrameworkThe framework that grounded our study is Tinto’s Model of Motivation Persistence [8], shown inFigure 1. In this model, Tinto describes motivation using three components: 1) self-efficacy (i.e.,a person’s belief that they can succeed in a specific situation or at a specific task); 2) sense ofbelonging (i.e., the extent to which a person perceives themselves as a valued member of acommunity); and 3) perceptions of curriculum (i.e., the perceived quality, value, and utility of acurriculum and its associated content). In this study, we apply Tinto’s Model to consider howchanges in assessment

Conference Session

NSF Grantees Poster Session

Collection

2022 ASEE Annual Conference & Exposition

Authors

Indira Chatterjee, University of Nevada, Reno; Kelsey Scalaro, University of Nevada, Reno; Ann-Marie Vollstedt, University of Nevada, Reno; Jeffrey Lacombe, University of Nevada, Reno; Adam Kirn, University of Nevada, Reno

to pursueopportunities like internships, research, etc.Engineering Education Research The mixed methods engineering educational research study that is part of the CREATEprogram, involves collecting quantitative survey (via the Intersectionality of Non-normativeIdentities in the Cultures of Engineering (InIce) instrument [17] to measure student future-oriented motivations, identities, and career and outcome expectations), and qualitative focusgroup data every semester. The research questions that are being addressed are: (1) How stronglyis the implementation of evidence-based programs and activities linked to academic success(based on GPA), increased graduation rate, and change in self-efficacy and engineering identity?(2) Which specific

Conference Session

International Division Technical Session 6: Monitoring, Evaluating and Research

Collection

2022 ASEE Annual Conference & Exposition

Authors

Maria Alves, Texas A&M University; Ahmarlay Myint, Texas A&M University; Zenon Medina-Cetina, Texas A&M University; Sonia Garcia, University of Georgia

college career. The goal of the course is tofamiliarize young students with the essentials of research methods/process; although students didnot report an increase in statistical knowledge, they expressed an interest in graduate school. Plans to Attend Grad School per Cohort 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2016 2017 2019 Grad School OtherFig. 8. Plans to attend grad school: 2016-2019 cohorts. The 2018 cohort was not asked about their future plans. C. Self-efficacy and Self-identity Eleven survey questions focused on the students’ self-identity and self-efficacy, and alleleven questions