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Johanna Milord, University of Missouri - Columbia; Fan Yu, University of Missouri - Columbia; Sarah Lynn Orton P.E., University of Missouri - Columbia; Lisa Y. Flores, University of Missouri - Columbia; Rose M. Marra, University of Missouri - Columbia

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data set measures students’ social cognitions over the course of theSpring 2020 semester in a set of 8 engineering courses using the same group of students beforeand after the unexpected transition to remote learning.BACKGROUNDThis study seeks to determine if the sudden transition to remote learning impacted students’engineering self-efficacy and outcome expectations. If these social cognitions were impacted,then student’s performance, persistence, and approach/avoidance behavior may also be impacted.To understand the basis of the study, the following section reviews the relevant background onsocial cognitions.Social CognitionsBandura’s [2, 3] social cognitive theory postulates that the social cognitions of self-efficacy andoutcome

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Bruce D. DeRuntz, Southern Illinois University - Carbondale; Harvey Henson, Southern Illinois University - Carbondale; Tom Withee, Goshen Education Consulting Inc.; Olivia Hood, Leadership Development Program

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first step in leadership development. By their responses they have shown an accurateself-awareness, honesty, and self- discipline. They have demonstrated that they can lead themselves.GrowthStudent’s growth of their leadership was examined through instruments that measured theirLeadership Self-Efficacy (LSE) and Motivation to Lead (MTL). LDP students showed the mostimprovement in efficacy after one year of the program. Similarly, LDP students’ motivationappear to remain consistent throughout the program.Combining this with results from the control group, suggest that LDP students come into theprogram with higher motivation than their peers but develop higher efficacy because of theprogram. Future surveys will incorporate a retrospective pre

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Aliya Mahmud, Georgia Institute of Technology: IDREEM Lab; Timothy Sawchuk; Ethan Hilton, Louisiana Tech University; Robert L. Nagel, James Madison University; Julie S. Linsey, Georgia Institute of Technology

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  related problems.  For  students  specifically,  makerspaces  provide  opportunities  for  hands‐on  experience  in problem  solving,  design,  prototyping,  and  manufacturing.  Given  the  collaborative‐learning  nature  of makerspaces, and how prevalently they’re used by students, the question posed is how does makerspace involvement impact student performance. In this longitudinal study, student performance is qualified by experimental measurements of idea generation ability and engineering design self‐efficacy (EDSE).  Method The data presented here is a part of a 5‐year longitudinal study (removed).  In this paper we focus impact to idea generation.  The participants of this study were freshman and senior undergraduate students from

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Sharon Miller, Indiana University - Purdue University Indianapolis; Steven Higbee, Indiana University - Purdue University Indianapolis

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, Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering. 2012.[5] American Educational Research Association, American Psychological Association, and National Council on Measurment in Education, Standards for Educational and Psychological Testing. 2014.[6] R. J. Jenson, A. N. Petri, A. D. Day, K. Z. Truman, and K. Duffy, “Perceptions of Self- Efficacy among STEM Students with Disabilities,” Journal of Postsecondary Education and Disability, vol. 24, no. 4, pp. 269–283, 2011.[7] Şe. Purzer, “The Relationship Between Team Discourse, Self-Efficacy, and Individual Achievement: A Sequential Mixed-Methods Study,” Journal of Engineering Education, vol. 100, no. 4, pp. 655

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Robin A. M. Hensel, West Virginia University; Joseph Dygert, West Virginia University; Melissa Lynn Morris, University of Nevada - Las Vegas

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]. Students who ultimately leave engineeringbefore their second year often begin their engineering journey with unrealistic views of theirability and the difficulty of the journey. Typically, they underestimate the demands of the major(and career) and overestimate their ability to succeed in the major with little extra effort [2], [3],[5]. This paper compares pre- and post-measures of characteristics believed to be influential orrelated to academic success and student retention in STEM fields for three cohorts (2017, 2018,and 2019) of the AcES program.2.0 MethodologyThree survey instruments: the Grit assessment [6], [7], the Longitudinal Assessment ofEngineering Self-Efficacy (LAESE) survey [8], [9], and the Motivated Strategies for

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Tiffiny Antionette Butler, Worcester Polytechnic Institute; Katherine C. Chen, Worcester Polytechnic Institute; Kimberly Lechasseur, Worcester Polytechnic Institute

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their educational success. Quantitative methods are used in this study to assess students’ self-efficacy; a baseline ispresented here with plans to measure changes over time during students’ participation asCoMPASS Scholars. We administered a baseline survey to incoming CoMPASS Scholars usingthe Longitudinal Assessment of Engineering Self-Efficacy (LAESE). The LAESE is a validatedinstrument developed by the Assessing Women in Engineering project with NSF support (HRD0120642, HRD 0607081). This instrument has been validated to measure the self-efficacy ofundergraduate students studying engineering, their feelings of inclusion, and outcomesexpectations [4] - [7]. In addition, a satisfaction tracker was used to solicit student feedback

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Indira Chatterjee, University of Nevada, Reno; Kelsey Scalaro, University of Nevada, Reno; Ann-Marie Vollstedt, University of Nevada, Reno; Jeffrey C. LaCombe, University of Nevada, Reno; Julia M. Williams, Rose-Hulman Institute of Technology; Adam Kirn, University of Nevada, Reno

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students to enter graduate school. Quantitative measurableoutcomes will include increased student retention; increased cohort self-efficacy and identitystatistics; higher-than-average graduation rate for the cohorts through evidence-based programs;and successful placement in industry or graduate school. CREATE will have a broad impact onlow-income, academically talented students in two key ways (1) Support of 32 students withscholarships; and (2) Implementation and assessment of academic and professional developmentsupport mechanisms that are tuned to the needs of these students. Both impacts achievestate/federal strategic workforce diversification goals. Qualitative measurable outcomes willinclude attaining academic and personal goals; increased

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S. Patrick Walton, Michigan State University; Lisa Linnenbrink-Garcia, Michigan State University

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psychological processes(students’ feelings of belonging, their motivation in engineering (self-efficacy, value, cost), andtheir development of an identity as an engineer) and how these processes are in turn associatedwith persistence in engineering. We are studying these research questions in the context of theCoEng at Michigan State University (MSU). Figure 1: Conceptual Model of Research Design. The project is examining which early (first-year) and later institutional supports predict students’ growth of important psychological processes and whether such growth mediates improvements in student persistence.Procedure and Data Collection: To date (including work prior to the current RIEF project), wehave collected longitudinal data from six cohorts of

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Melissa Dean, STEMWorks, LLC; James Van Haneghan, STEMWorks, LLC; Susan Pruet, STEMWorks, LLC; James Duke, STEMWorks, LLC.

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own words.This instrument was developed to measure indicators of impact on the SCCT constructs ofoutcome expectations and self-efficacy. Figure 2: Outcomes and Subscales of the Pre/Post Test. Note: * indicates significant differences favoring Academy Cadets.To supplement the pre/post assessment we collected qualitative data through interviews andstudent reflection journals. At the end of each day of the Academy, students were givenreflection prompts about the day’s activities. Students kept an electronic journal which captureda record of all their responses to each prompt. These journals were analyzed and comparedagainst the findings from the pre/post survey to better understand student attitudes towardSTEM, big ideas students took

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Joni M. Lakin, University of Alabama; Daniela Marghitu, Auburn University; Edward W. Davis, Auburn University; Virginia A. Davis, Auburn University

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-Camp Surveys. The quantitative surveys included measures of science andengineering interest and self-efficacy developed for this age group. [33] [34] Example items areprovided in Table 3. The scale for each ranged from 1 (not at all true) to 3 (somewhat true) to 5(very true). Given the limited sample, we used the Wilcoxon signed-rank test for a paired samplecomparison. [35] This nonparametric test compares the magnitude of pre-to-post changes acrossparticipants to determine if the positive changes are consistently larger than any negativechanges.At the beginning of camp, students also rated their career and life values on a survey instrumentcommonly used for career planning. [36] Examples are included in Table 3. The scale rangedfrom 1 to 4: 1

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Sara A. Atwood, Elizabethtown College; Shannon Katherine Gilmartin, Stanford University; Anna M. Mostoller, Elizabethtown College; Helen L. Chen, Stanford University; Sheri Sheppard, Stanford University

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participation rates relatedto academic cohort (e.g., junior, senior), gender, underrepresented minority (URM) status, first-generation, and low-income status, as well as a subset of identities at the intersection of thesegroups (gender + URM; first-generation + low-income). A logistic regression model furtherexamined factors such as GPA, engineering task self-efficacy, field of engineering, andinstitution type.We found that amongst the students in our dataset, 64.8% of the seniors had “worked in aprofessional engineering environment as an intern/co-op” (41.1% of juniors, 64.7% of 5th years).Significantly less likely (p<0.05) to have internship experiences were men compared to women(52.9% vs 58.3%), URM students compared to their majority

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Catherine E. Douglas, University of California, Los Angeles; Scott Brandenberg, University of California, Los Angeles; Anabella Gonzalez

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students (URES) suffer 60% attrition in their freshmencohort leading to only 40% earning a B.S. degree in engineering. Three key reasons are poorteaching and advising; the difficulty of the engineering curriculum; and a lack of “belonging” withinengineering. Each, in some way, erodes a student’s self-efficacy, or confidence in his or her ability toperform [1]. The American Society of Engineering Educators conducted two recent national studieson freshmen engineering cohort retention: Going the Distance and reported the following B.S.degree completion outcomes by ethnicity: Asian Americans-66.5%, Caucasian-59.7% /Hispanic/Latino-44.4% , Native American-38.6%, African American-38.3%, and All Females-61%. [2]The attrition problem is concentrated in

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Cynthia Kay Pickering, Arizona State University; Caroline Vaningen-Dunn, Arizona State University; Maria A. Reyes, Phoenix College

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. trends in CIT/STEM student student self-efficacy, CIT/STEM student self-efficacy, enrollment, enrollment, retention, self-efficacy, enrollment,Obj 1.2 Establish retention, completion, and completion, and transfer. retention, completion,strategies for enlisting transfer (SERCT). and transfer.industry partnerships that Obj 3.3 Establishbecome self-sustaining Obj 2.3 Establish Leadership Leadership strategies Obj 4.3 Establish strategies committed to committed to Leadership strategiesObj 1.3 Use STEM-ESS accelerating Latinx student accelerating Latinx committed toto strengthen

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Nicole Evans McIntyre, University of California, Berkeley; Catherine T. Amelink, Virginia Polytechnic Institute and State University

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research projects, mentoring, boot camp, professionaldevelopment, and community building events. Analysis of quantitative evaluation datademonstrates that, despite the remote format, interns had a very positive internship experienceand highly satisfying mentoring relationships with graduate students. Most notably, theinternship significantly enhanced students’ confidence to succeed as a student in science andengineering, and self-efficacy in their research skills. This paper and poster presentation willprovide a model for similar NSF funded programs pursuing an online format. The administrativeteam expects such transitions to become increasingly common for various reasons, including theneed to adapt to unexpected health and environmental barriers

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Mike Borowczak, University of Wyoming; Andrea Carneal Burrows Borowczak, University of Wyoming; Mason Johnson, University of Wyoming

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indicateshigher levels of teaching self-efficacy and outcome expectancy.A set of four questions was designed to test participants’ knowledge of computer science beforeand after attending the PD training as well as their awareness of WySLICE’s computer sciencestandards. Participants’ pre- and post- survey responses to one of the four questions wereanalyzed - namely, ”Which of the following best describes Computer Science?”Differences in self-efficacy and outcome expectancy pre- and post-PD training were determinedby analyzing the results of a paired samples t-test comparing participants’ pre- and post- surveyscores for the 21 of 24 participants completing both the pre and post survey - the three of the 24participants who did not complete the post-survey

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Zahraa Krayem Stuart, Stony Brook University; Monica Bugallo, Stony Brook University; Kathleen Dinota, Stony Brook University; Hechuan Wang, Stony Brook University; Amanda Esposito, Stony Brook University

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and qualitative data were collected throughout the sessions (N=90) to measure impact.Participants were administered pre- and post-questionnaires at every session. The set of pre- and post-questionswere exactly the same and used to assess participants’ engineering knowledge and interest. At the end of theAcademy, participants were sent via an email a post-experience survey to evaluate their engineering self-efficacy related to their interest in engineering majors and careers and their comprehension of engineeringconcepts explained during the Academy. The survey was created and validated by engineering faculty.Pre and post multiple-choice questions administered throughout the Academy included: 1. What does the term ‘dimensions’ mean when

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Lorelie Imperial, University of Florida; Corey Payne; Kent J. Crippen, University of Florida; Maria Korolev, University of Florida; Philip J. Brucat, University of Florida; Chang-Yu Wu, University of Florida

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additional courses [18], [19]. Interest and success build self-efficacy, an expectancy belief, that is defined as “beliefs in one’s capabilities to organize andexecute the courses of action required to produce given attainments'' [20]. Self-efficacy has beenshown to be one of the strongest predictors of academic achievement for undergraduates [21]. MethodologyThis study employed a causal-comparative, single group research design. A purposeful sample of281 participants taking the first semester general chemistry laboratory course for engineers wereconsented as participants. Demographics were determined based upon an initial survey whereparticipants indicated their major, gender identity and ethnicity. URM

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Katerina Goseva-Popstojanova, West Virginia University; Robin A.M. Hensel, West Virginia University

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institutionalizationdecisions, providing a practical model for other institutions, and supporting future programmodification to provide the best possible experience for students. Since cohort 1 studentsreceived ACCESS scholarships for the first time in fall 2020, however, data is not yet available.Analysis of measures of student success and persistence, self-efficacy, and motivation within thecybersecurity field will be topics of future papers.6.0 ChallengesWhile the restrictions related to the COVID-19 pandemic presented challenges related torecruitment and programming activities, the ACCESS project team quickly adapted to the newreality and organized online meetings, an award ceremony, panels and seminars, and created anonline private group to support effective

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Mark Wong, Contra Costa College; Sergio Alexander Alvarez, Contra Costa College; Jonathan Alexander Canel, Contra Costa College; Phiroze Jhalman Duggal; Yesenia Rodriguez Moreno ; Daven Ng ; Nathalie Zaldivar; Chao Liu, Contra Costa College; Jeff Kamalian, Contra Costa College; Seti Sidharta, Contra Costa College

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, June 18-21, 2006, ASEE Conferences, 2006. pp. 11.1451.1 - 11.1451.7.[2] P. M. Leggett-Robinson, N. Davis, and B. Villa, "Cultivating STEM Identity and Belongingthrough Civic Engagement: Increasing Student Success (Self-efficacy and Persistence) for theTwo-Year College STEM Student," Science Education and Civic Engagement, vol. 10, no. 1,Winter 2018. [Online]. Available:https://new.seceij.net/articletype/projectreport/cultivatingstemidentityandbelonging/. [AccessedFeb. 28, 2021].[3] Chen, X., and M. Soldner (2013). STEM Attrition: College Students’ Paths Into and Out of STEMFields. National Center for Education Statistics, U.S. Department of Education[4] LaForce, Melanie; Noble, Elizabeth; Blackwell, Courtney. 2017. "Problem-Based Learning (PBL

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Lauren J. Donovan, Stony Brook University; Monica Bugallo, Stony Brook University; Thomas Woodson, Stony Brook University; Candice June Foley, Suffolk County Community College; Shanise N. Kent, University at Albany, State University of New York; Bonita London, Stony Brook University; Stacie Swingle Nunes, SUNY New Paltz; Maurie McInnis, Stony Brook University

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aligned with their STEM career). PIC is a core concept ofidentity research, emphasizing that when individuals perceive a close connection between theirself-concept and their career goals, they are more likely to maintain motivation, interest andpersist in that domain, even when they experience difficulty [22]. Further, the data suggest thatPIC leads to higher sense of belonging to the University and it’s members. Kim, London andcolleagues also demonstrate that interest in a STEM career and sense of belonging in one’sUniversity both predicted STEM self-efficacy among students (confidence in one’s ability tomanage STEM academic tasks). STEM self-efficacy in turn predicted higher STEM achievementin classes through students’ second year of college

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Muhammad Dawood, New Mexico State University; Ehtesham Shareef, New Mexico State University; Rachel Boren, New Mexico State University; Germain Degardin, New Mexico State University; Melissa J. Guynn, New Mexico State University; Patti Wojahn, New Mexico State University

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Strategies on Performance in General Chemistry Courses,” Journal of Chemical Education, 2013, 90, 961-7.12. Credé, M., and Kuncel, N. R., (2008), “Study Habits, Skills, and Attitudes The Third Pillar Supporting Collegiate Academic Performance,” Perspectives on Psychology Science, Vol. 3, n. 6, pp. 425-453.13. Elliott, Timothy R.; Godshall, Frank; Shrout, John R.; Witty, Thomas E. (1990), “Problem-solving appraisal, self-reported study habits, and performance of academically at-risk college students.” Journal of Counseling Psychology, Vol 37(2), Apr 1990, 203- 20714. Ogden, N., Evans, S., Thurlow, G. (2012), “Student Self-Efficacy and Attitudes Following Integration of Study Strategy Information into Course Content.” Paper 12

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Tracy Camp, Colorado School of Mines; Christine Liebe, Colorado School of Mines; Heather Thiry

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alerting them of the opportunity to apply toPATHS. Staff in Mines Admissions and Financial Aid also steer high-potential prospects to thePATHS website and encourage them to apply. The PATHS scholarship opportunity was alsopromoted by surrounding community college staff and faculty, as well as Colorado high schoolCS teachers.PATHS scholars provide K-14 outreach based on their interests (e.g., visiting their previous highschool or providing K-12 CS presentations). K-14 outreach provide the PATHS scholars withvaluable experience and provide the high school students role models with whom they canidentify (especially female and URG CS@Mines ambassadors). K-14 outreach has proven to aidthe development of self-efficacy, retention, and recruitment for

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Marcia A. Mardis, Florida State University; Faye R. Jones, Florida State University

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/competencymodel/competency- models/advanced-manufacturing.aspx.[15] C. C. Chen, P. G. Greene, and A. Crick, "Does entrepreneurial self-efficacy distinguish entrepreneurs from managers?," Journal of Business Venturing, vol. 13, pp. 295-316, 1998.[16] J. Cheng, "Intrapreneurship and exopreneurship in manufacturing firms: An empirical study of performance implications," Journal of Enterprising Culture, vol. 9, no. 2, pp. 153-171, 2001.[17] E. J. Douglas and J. R. Fitzsimmons, "Intrapreneurial intentions vs.entrepreneurial intentions: Distinct constructs with different antecedents," Small Business Economics, vol. 41, no. 1, pp. 115-149[Online]. Available: http://www98.griffith.edu.au/dspace/bitstream

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Christina Grigorian, California Polytechnic State University, San Luis Obipso; Michelle Kerfs, California Polytechnic State University, San Luis Obispo; Jocelyn Paula Gee, California Polytechnic State University, San Luis Obispo; James M. Widmann, California Polytechnic State University, San Luis Obispo; John Chen P.E., California Polytechnic State University, San Luis Obispo; Brian P. Self, California Polytechnic State University, San Luis Obispo

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structure of the Brief Self-Control Scale: Discriminant validity of restraint and impulsivity. Journal of Research in Personality, 46(1):111-115, 2012.[4] Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994), Losing control: How and why people fail at self-regulation. San Diego, CA, US: Academic Press.[5] Feldmann, S. C., and Martinez-Pons, M., (1995), The relationship of self-efficacy, self- regulation, and collaborative verbal behavior with grades, Preliminary Findings, Psychological Reports, 77:971-978.[6] McCrae, R. R., and John, O. P., (1992), An introduction to the five-factor model and its applications, Journal of Personality, 60(2):175-215.[7] Duckworth, A. L., Peterson, C., Matthews, M. D

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Patrick Cunningham, Rose-Hulman Institute of Technology; Rachel McCord Ellestad, University of Tennessee at Knoxville; Holly M. Matusovich, Virginia Polytechnic Institute and State University; Cheryl Carrico P.E., E4S, LLC

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eliminating the time and cost of travel, this project will enable populations thatmight otherwise be limited in attendance such as professional-track faculty, teaching focused faculty,community college faculty, adjunct faculty.IntroductionThe Skillful Learning Institute (SLI) is preparing a virtual short course experience for 25-30 engineeringeducators to expand the explicit engagement of engineering students in their metacognitive development,which is currently lacking. Metacognition is instrumental in being able to independently assess and directone’s learning - a lifelong skill to propel ongoing growth and development. As such, metacognition isimportant for engineers because it empowers them (i.e., builds their agency and self-efficacy) to

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Noemi V. Mendoza Diaz, Texas A&M University; Russ Meier, Milwaukee School of Engineering; Deborah Anne Trytten, University of Oklahoma; So Yoon Yoon, University of Cincinnati; Janie M. Moore, Texas A&M University; Andrea M. Ogilvie P.E., Texas A&M University; Mark Weichold, Texas A&M University

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engineering classes. Institutions might also discover theneed for introductory computational thinking courses that previously were not included in thecurriculum.ECTD results will also allow instructors to understand how their student cohorts function acrossthe broad areas of computational thinking. By using the results, the instructors can focusclassroom and assessment activities to help students mature computational thinking factors thatare less developed. The long-term impact would be classroom instruction that helps increasestudent self-efficacy and improve student enculturation into the engineering profession. ReferencesCattell, R. B. (1966). The scree test for the number of factors. Multivariate

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Leen-Kiat Soh, University of Nebraska - Lincoln; Gwen Nugent, University of Nebraska - Lincoln; Wendy Smith, University of Nebraska - Lincoln; Guy Trainin, University of Nebraska - Lincoln; John T. Sutton, ResultED LLC; Kent Steen, Lincoln Public Schools

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of our PD program in terms of the design of our courses and Saturday workshopsbased on feedback from the teachers. There will also be efforts in facilitating and sustaining acommunity of practice for the teachers.AcknowledgementsThe authors would also like to acknowledge the other collaborators on the project and teachingassistants and instructors of the two summer courses and the Saturday workshops. This workwas supported by the National Science Foundation under grant no. 1837476.ReferencesPeteranetz, Markeya, Shiyuan Wang, Duane Shell, Abraham E. Flanigan, and Leen-Kiat Soh. 2018. “Examining the Impact of Computational Creativity Exercises on College Computer Science Students’ Learning, Achievement, Self-Efficacy, and Creativity

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Laura J. Carroll, University of Michigan; Lea K. Marlor, University of Michigan; Cynthia J. Finelli, University of Michigan; Matthew Charles Graham ; Madison E. Andrews, University of Texas at Austin; Jenefer Husman, University of Oregon; Michael J. Prince, Bucknell University; Maura J. Borrego, University of Texas at Austin

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second-year STEM courses. Theworkshop is designed to promote active learning and strategies to reduce student resistance toactive learning [16]. We developed student and instructor surveys to assess the workshops’ impact. Thestudent survey focuses on instructors’ use of active learning, instructors’ use of the associatedstrategies to reduce student resistance, and students’ responses to active learning [17]. Theinstructor survey measures instructors’ intentions and motivation (value and self-efficacy) forusing active learning as well as strategies to reduce student resistance to active learning [18]. We assessed three pilot offerings of the workshop by measuring instructors’ attitudestoward active learning before and after

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Michael Jason Babcock, Whatcom Community College; Tommaso Alessandro Vannelli, Whatcom Community College; Dan Hanley, Western Washington University; Eric Davishahl, Whatcom Community College

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and the extent to which they view themselves as a “STEM person”.  Slightly modified version of the Chemistry Motivation Questionnaire (Glynn & Koballa, 2005), which includes 30 items that measure the following six student factors: Intrinsic Motivation, Extrinsic Motivation, Self-Efficacy, Self-Determination, Goal-Orientation, Anxiety-Related Motivation.  The Sense of Belongingness scale [8], which is part of the National Survey of Student Engagement, used by Higher Education Research Institute at UCLA and the Center for Post-Secondary Research and Planning at Indiana University. This instrument operationalizes "belongingness" in a number of different contexts, including

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Ying Tang, Rowan University; Ryan Hare, Rowan University

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completing the lab assignment(Cohen’s d = −0.004).Figure 4: Comparison of pre and post-test results on a relevant content test for students who didand did not use Gridlock.Students were also administered a self-efficacy survey that asked questions in three categories: 1.Confidence in ability, with questions such as ”Do you feel that you have the skills necessary tosucceed in engineering”; 2. Feelings of belonging, with questions such as ”Do you feel that youthink in the same way as other students in your engineering department”; and 3. Feelings ofalienation, with questions such as ”Do you feel alienated from engineering at your university”.Students then rated on a scale of 1 to 7, 1 being strongly disagree and 7 being strongly agree.Table 2 shows