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

Women in Engineering Division Technical Session 5

Collection

2018 ASEE Annual Conference & Exposition

Authors

Jennifer A Gatz, Stony Brook University ; Angela M Kelly, Stony Brook University; Monica Bugallo, Stony Brook University

Tagged Topics

Diversity

Tagged Divisions

Women in Engineering

be measured in terms of gradeperformance and intellectual development during the college years [22]. While ability has beenpositively associated with college persistence, commitment to the goal of completion is the mostinfluential factor in determining persistence [22]. A feeling of success and congruence in theacademic environment may lead to increased motivation to study, which may lead to betterperformance, increased academic self-efficacy, and institutional commitment [23]. Learningcommunities are a way to combine academic and social aspects of an institution to help increaseacademic performance and retention, particularly in the transition from high school to college[24]. Learning communities that include mentoring encourage personal

Conference Session

They're Not "Soft" Skills!

Collection

2011 ASEE Annual Conference & Exposition

Authors

Brook Sattler, University of Washington; Ashley Ann Thompson, University of Washington; Jennifer A. Turns, University of Washington; Deborah Kilgore, University of Washington

Tagged Divisions

Educational Research and Methods

to measure their progress against their own goals as well asagainst their peers’ progress, which in turn impacts the types of goals they set.Positive Relationship between Goals and Self-EfficacyIn engineering education, self-efficacy is important when considering issues of recruitment andpersistence of students, especially underrepresented students.15 Students with higher self-efficacytend to have higher academic achievement, because they set higher goals.14 Bandura defines self-efficacy as ―the conviction that one can successfully execute the behavior required to produce theoutcomes.‖ 16 Relative to goal setting and monitoring, social learning theory articulates a causalrelationship between self-efficacy and goals since ―goals increase

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

NSF Grantees: RED 2 / Civil Eng

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Madeline Polmear, University of Florida; Denise Rutledge Simmons P.E., University of Florida; Nicholas Anthony Clegorne

Tagged Topics

NSF Grantees Poster Session

data analysis and synthesisprocess and to solicit input from the engineering education community on the initialconceptualization.Figure 2: Preliminary grounded theory modelNext Steps and Future DirectionsThe findings from the student interviews and preliminary model are being used to inform thedevelopment of an instrument. The instrument will include measures related to power, sharedprocess of leadership, transformational leadership skills, self-efficacy, and motivation to expandour understanding of how undergraduate engineering students perceive and engage in leadershipbased on constructs that were salient in the qualitative phase.AcknowledgmentsThe authors gratefully acknowledge the National Science Foundation for supporting this workunder

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

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Melanie B Butler, Mount St. Mary's University; Rosina Bolen; DINA YAGODICH, Frederick Community College; Aubrey Allen Smith, Montgomery College; Christine McCauslin; Isaac N Mills, Mount Saint Mary College; Jeffrey Simmons; Kraig E Sheetz

Tagged Topics

Diversity, NSF Grantees Poster Session

Asian Black White Agree Agree Agree Agree Self-Efficacy I am confident that I will be 4.7 ± 0.7 4.7 ± 0.6 4.6 ± 0.7 4.5 ± 0.9 4.7 ± 0.6 4.8 ± 0.6 4.7 ± 0.5 4.7 ± 0.7 able to transfer to a 4-year institution. Self-Efficacy I am aware of the 3.9 ± 1.1 3.9 ± 1.1 3.9 ± 1.2 3.7 ± 1.2 3.9 ± 1.1 4.0 ± 1.0 4.1 ± 1.0 3.5 ± 1.3 procedures involved in transferring to a 4-year institution. Self-Efficacy I know how I can get more 4.2 ± 1.0 4.2 ± 1.0 4.2 ± 1.0 4.1

Conference Session

Robotics

Collection

2018 ASEE Annual Conference & Exposition

Authors

Abhidipta Mallik, New York University; S.M. Mizanoor Rahman, New York University; Sheila Borges Rajguru, New York University; Vikram Kapila, New York University

Tagged Divisions

Pre-College Engineering Education

to their students formany years. Some individual teachers may find it challenging to engage in robotics-aided STEMeducation due to their lack of required TPACK self-efficacy (see [5,9] for details about TPACKself-efficacy). Moreover, all robotics-aided STEM lessons are not the same, i.e., their difficultylevels may vary due to variations in the required TPACK. Specifically, while some lessons maybe more complicated from the design or programming (technology) point of view, others may becomplicated from the teaching, learning, or assessment (pedagogical) point of view, and theincorporation of robots (technology) may also impact the pedagogy. Thus, it is important toconcentrate on investigating the TPACK framework for individual teacher and

Conference Session

CoNECD Session : Day 2 Slot 6 Technical Session 2

Collection

2021 CoNECD

Authors

Ekundayo Shittu, George Washington University; Jennifer Dashiell-Shoffner, North Carolina A&T State University; Hyung Nam Kim, North Carolina A&T State University

Tagged Topics

CoNECD Paper Submissions, Diversity

constructs in the population. The constructs are all positively correlated, withmagnitude of correlation corresponding to the size of the bubble. This is shown by the checkedbubbles intersecting any two pairs of measures in Figure 2. It is evident that Anticipatory Cognitionis correlated and significant to several of the measures, but lacks significance against stereotypethreat, isolation, extant knowledge and future anticipation. For example, the weaker theparticipants infer the stereotype threat, the higher is their attention and focus to solving theirresearch problem. It is also evident from this Figure that Academic Self Efficacy is predominantlycorrelated

Conference Session

ERM Technical Session 8: Survey and Instrument Development

Collection

2019 ASEE Annual Conference & Exposition

Authors

Melissa G. Kuhn, Old Dominion University; Joanna K. Garner, Old Dominion University; Shanan Chappell, Old Dominion University

Tagged Divisions

Educational Research and Methods

structure of a sample ofstudent ambassadors who completed the measure at the outset of the academic year.MethodsA review of literature revealed existing resources measuring undergraduate engineering students’motivation and self-efficacy, future intentions, and engineering-related beliefs. These include theLongitudinal Assessment of Engineering Self-Efficacy (LAESE) [5], the Project to AssessClimate in Engineering (PACE) survey [13], the Laanan Transfer Students Questionnaire (L-TSQ) [6], the National WEPAN pilot climate survey [11] Academy of Engineering Changing the 2Conversation survey [12], Assessing Women and Men in Engineering (AWE

Conference Session

Minorities in Engineering Division Technical Session 4

Collection

2019 ASEE Annual Conference & Exposition

Authors

Nicole M. Colston, Oklahoma State University; Sherri L. Turner, University of Minnesota, Twin Cities; Gale Mason Chagil, Culture Inquiry Consulting, LLC; Sue C. Jacobs Ph.D., Oklahoma State University; Sarah Johnson, Oklahoma State University

Tagged Topics

Diversity

Tagged Divisions

Minorities in Engineering

activities” (CareerExploration Skills).The SCDI has been used in studies of adolescent, college student, and post-high school youngadult career development [e.g., 27, 28, 29], including studies of the career development of NativeAmerican young people. Career exploration, as measured by the SCDI, has been positivelyrelated to interests and efficacy among Native American young people [30].The Career-Related Parent Support Scale [31] is a 27-item instrument that was used to measurestudents’ self-reports of their parents’ support in the four areas of self-efficacy information(Instrumental Assistance (IA), Career-Related Role Modeling (CM), Emotional Support (ES),and Verbal Encouragement (VE)) identified by Bandura [32]. IA is the tangible help provided

Conference Session

Design Across Curriculum 1

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Christopher Rennick, University of Waterloo; Carol Hulls P.Eng., University of Waterloo; Andrew Gryguć, University of Waterloo

Tagged Divisions

Design in Engineering Education

affect, is self-efficacy asdescribed in Bandura’s Social Cognitive Theory [5]. According to this theory, peoples’ beliefs intheir capabilities vary across domains and situations, and can develop through 4 mechanisms: 1. Mastery experiences: achieving success on a challenging task 2. Social modeling: seeing similar people achieve success 3. Social persuasion: being convinced by others that one can succeed; and 4. Physical and emotional statesSelf-efficacy can have significant impacts on student resilience, persistence, and attitude during aproblem solving session; as Bandura describes: “How people perceive the structuralcharacteristics of their environment—the impediments it erects and the opportunity structures itprovides

Conference Session

NSF Grantees’ Poster Session

Collection

2015 ASEE Annual Conference & Exposition

Authors

Ashley Guy, University of Texas at Arlington; Alan Bowling, University of Texas at Arlington; Panayiotis S. Shiakolas, University of Texas, Arlington

Tagged Topics

Diversity, NSF Grantees Poster Session

large gains over pre-vious curricula 39 . Jara found that students in Automatics and Robotics at the Universityof Alicante significantly improved their efficacy and performance following a “learning bydoing” approach using a remote robotic laboratory called RobUALab 42 . Cannon positivelyreviewed a University of Minnesota robotics day camp for middle school youth designed toinspire minorities and women to pursue careers in STEM through hands-on learning 24 . Thiswork aims to provide additional support for these findings. This work is based on the hypothesis that in addition to engagement, the proposed ap-proach will also positively affect students’ academic success by boosting self-efficacy, theperceived ability to complete a task and reach

Conference Session

Outreach and Beyond in the Chemical Engineering Classroom

Collection

2019 ASEE Annual Conference & Exposition

Authors

Byron Hempel, University of Arizona; Paul Blowers, University of Arizona; Kasi M. Kiehlbaugh, University of Arizona

Tagged Topics

Diversity

Tagged Divisions

Chemical Engineering

co-teaching, classroom technologies, active learning in the classroom, and various classroom-based affective inter- ventions targeted at fostering self-efficacy, belongingness, metacognitive learning strategies, and growth mindset affect outcomes such as student retention and success, particularly during the freshman and sophomore year. Her field of research is undergraduate engineering education. Dr. Kiehlbaugh com- pleted her BS and MS at the University of Arizona and her PhD at UC Berkeley. She is now a Research Assistant Professor in the College of Engineering at her undergraduate alma mater. c American Society for Engineering Education, 2019 1 Scalable and Practical

Conference Session

Engineering Economy Division Technical Session 1

Collection

2016 ASEE Annual Conference & Exposition

Authors

K. Jo Min, Iowa State University; John Jackman, Iowa State University; Michelle Zugg, Iowa State University

Tagged Topics

Diversity

Tagged Divisions

Engineering Economy

, 1995, Generalized Self-Efficacy Scale. In J. Weinman, S. Wright, and M.Johnston, Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35-37). Windsor, UK:NFER-NELSON.10 Cohen J., 1988, Statistical Power Analysis for the Behavioral Sciences (2nd ed.), Lawrence Earlbaum Associates,New Jersey.11 Shi, W. and K. J. Min, 2014, “Product Remanufacturing: A Real Options Approach,” IEEE Transactions onEngineering Management, Vol. 61, pp. 237-250, 2014.12 Shi, W. and K. J. Min, 2014, “Product Remanufacturing and Replacement Decisions Under Operations andMaintenance Cost Uncertainties,” The Engineering Economist – Special Issue on Engineering Economics inReliability, Replacement and Maintenance, Part 1, Vol. 59

Conference Session

Hands-on Laboratory and Design Experiences in Environmental Engineering

Collection

2012 ASEE Annual Conference & Exposition

Authors

Sandeep Sathyamoorthy, Tufts University; Linda Jarvin, Paris College of Art; Michael Hollis, U.S. Air Force Academy; John Anthony Christ, U.S. Air Force Academy; C. Andrew Ramsburg, Tufts University

Tagged Divisions

Environmental Engineering

Pre PBL lab ScoreFigure 5: Measuring the impact of the PBL lab on learner self efficacy based on material taught but notmastered (left panel) and mastered (right panel). In both cases, the PBL has a significant impact on studentswho reported lower self efficacies prior to the PBL lab Page 25.105.10Table 1: Water Treatment System - Basis of DesignInfluent Quality: Turbidity 500 NTU UV Transmittance 10% (at 254 nm)Table 2: Water Treatment System Design Criteria and SpecificationsTreatment Capacity: 5 gallons in 30-min1 (10 gph)Surface Loading

Conference Session

Understanding Our Students I

Collection

2012 ASEE Annual Conference & Exposition

Authors

Kyle B. Reed, University of South Florida

Tagged Divisions

Educational Research and Methods

students who had hobbies related to engineering and studentswho had pre-engineering classes had significantly higher self-efficacy measures than studentswithout these interests or extra classes in first year students. A survey of first year engineeringstudents’ self-efficacy beliefs found that students’ motivation to succeed in the course and theirunderstanding of the material were ranked as the most influential factors that would contribute totheir success in the course10. Ponton et al.13 suggest that professors can enhance a student’s self-efficacy by developing skills, peer interaction, encouraging students, and explaining copingstrategies, all of which are important for practicing engineers.Self-efficacy can be difficult to measure since it is

Conference Session

Electrical and Computer Division Technical Session 2

Collection

2017 ASEE Annual Conference & Exposition

Authors

Joshua Alexéi García Sheridan, Virginia Polytechnic Institute and State University; Seungmo Kim, Virginia Polytechnic Institute and State University; Richard M. Goff, Virginia Tech Department of Engineering Education; Vuk Marojevic, Virginia Tech; Nicholas F. Polys, Virginia Polytechnic Institute and State University; Ayat Mohammed, Virginia Tech; Carl B Dietrich P.E., Virginia Polytechnic Institute and State University

Tagged Divisions

Electrical and Computer

-surveys created and conducted throughQualtrics software, a set of nine items adapted from the Motivational Strategies for LearningQuestionnaire (MSLQ) (Pintrich & De Groot, 1990) were presented to students to quantitativelyreport their confidence regarding their future performance in and learning of cognitive andspectrum sharing radio communications. A mean score was calculated from an average of allitems, which were 7-point Likert scale questions. Analyses of results from the survey wereconducted in SPSS. It should be noted that the item pool for self-efficacy demonstrated a verystrong level of reliability in measuring the construct. The Cronbach’s alpha calculated for themeasure was 0.872 in the pre-tutorial survey and 0.925 in the post

Conference Session

Student Division (STDT) Technical Session 2: Student Success and Resources

Collection

2023 ASEE Annual Conference & Exposition

Authors

Katherine Drinkwater, Duke University; Karis Boyd-Sinkler, Duke University; Rebecca Simmons, Duke University

Tagged Divisions

Student Division (STDT)

institution in theSoutheast United States. Given the exploratory nature of the study, a novel survey tool wascreated that focused on: residual time, club participation, design skills before and after clubparticipation, design self-efficacy, and demographic information, see Appendix A. This researchstudy was approved by the IRB at Duke University (protocol #2023-0178). 1) Survey DesignFor the purpose of transparency, we defined engineering clubs as a subset of clubs whosemembership is primarily engineers, the subject matter is technical, and/or they are a pre-professional organization for engineers. The engineering school at Duke University gives clubsthis designation. We divide engineering clubs into three categories: competition design teams

Conference Session

Women in Engineering Division Technical Session - Understanding and Changing Engineering Culture

Collection

2016 ASEE Annual Conference & Exposition

Authors

Mary E Fitzpatrick, University of Wisconsin Madison College of Engineering; Manuela Romero, University of Wisconsin, Madison; Jennifer Sheridan, University of Wisconsin, Madison

Tagged Topics

ASEE Diversity Committee, Diversity

Tagged Divisions

Women in Engineering

, 2016Changes in Undergraduate Engineering College Climate and Predictorsof Major Commitment: Results from Climate Studies in 2008 and 2015Abstract This paper presents results of two cross-sectional investigations of educational andinterpersonal climate in a college of engineering at a large mid-western university. In 2008 andin 2015 we deployed a survey ("Project to Assess Climate in Engineering”) to undergraduateengineering students. In each survey year, just over 1000 eligible students participated andresponded to items contributing to scales rating their professors, teaching assistants, collegeresources, confidence (self-efficacy) in engineering, student interactions, perceptions ofengineering, and commitment to an engineering major

Conference Session

Understanding Our Students

Collection

2011 ASEE Annual Conference & Exposition

Authors

Ida B. Ngambeki, Purdue University, West Lafayette; Demetra Evangelou, Purdue University, West Lafayette; Matthew W. Ohland, Purdue University, West Lafayette; George D. Ricco, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

’ interest inengineering, students’ social orientation and motivation, the barriers and supports theyencounter, their self-efficacy, and their satisfaction with their major.Students’ satisfaction with their major was measured using Nauta’s validated Major SatisfactionScale27 that contains items such as “I often wish I hadn’t gotten into this major” and “I feel goodabout the major I have selected.” Chen’s General Self-efficacy Scale28 tailored to engineeringwas used to measure students’ self-efficacy. This scale contains items including “Compared toother people, I can do most tasks very well” and “I am confident in my ability to solveengineering problems”. Social influence was measured using the Social Influence Scale 29. Theother scales used were

Conference Session

NSF Grantees Poster Session

Collection

2010 Annual Conference & Exposition

Authors

Lisa Schneider, Cornell University; Maria Terrell, Cornell University Math Dept.

nine workshops per semester rather thanfourteen, and one problem per workshop rather than two.During the Fall 2009 semester we ran a pilot pre- and post-test administration of the first draft ofboth assessment instruments – one measuring students’ abilities to use mathematics in appliedproblem-solving (MAI); and the other to gauge students' self-efficacy perceptions related tostudying engineering and to learning and applying mathematics (EMPS). The instrumentdevelopment and pilot-test administration processes are described in the following sections.Instrument Development:Mathematics Applications Inventory (MAI)The Mathematics Applications Inventory, MAI, is intended to measure the level at which firstyear undergraduate engineering students can

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

Engineering Design Graphics Division Technical Session 4: A Potpourri of Ideas

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Daniel P. Kelly, Texas Tech University; Jeremy V. Ernst, Embry-Riddle Aeronautical University; Aaron C. Clark, North Carolina State University at Raleigh; Erik Schettig, North Carolina State University at Raleigh

Tagged Topics

Diversity

Tagged Divisions

Engineering Design Graphics

, including student scoreon the pretest three-dimensional modeling self-efficacy (3DSE) assessment, gender, age, andwhether or not the student had a parent with professional engineering backgrounds. The three-dimensional self-efficacy instrument consisted of nine questions, each being a 7-point Likerttype item, designed to measure students’ self-efficacy related to modeling three-dimensionalobjects [11]. Logistic regression could not identify for which subgroups of students the variableswere most significant. For these reasons, machine learning analytics software was used toexamine the predictors, and their interactions, that led to persistence in engineering degreeprograms. Machine learning has gained popularity over recent years due to its ability

Conference Session

Developing Teaching and Mentoring Skills

Collection

2018 ASEE Annual Conference & Exposition

Authors

Hyun Hannah Choi, University of Illinois, Urbana-Champaign; Yuting W. Chen, University of Illinois at Urbana-Champaign; A. Mattox Beckman Jr., University of Illinois, Urbana-Champaign; Lucas Anderson, University of Illinois at Urbana-Champaign; Blake Everett Johnson, University of Illinois, Urbana-Champaign; Matthew D. Goodman, University of Illinois, Urbana-Champaign; Chris Migotsky, University of Illinois; Nicole Johnson-Glauch

Tagged Topics

Diversity

Tagged Divisions

Graduate Studies

join our GTA training.Program EvaluationAligned with the goals of the program to improve teaching ability and based on the assumptionthat students may not see the connection between teaching and transferable professional skills,this program evaluation was designed to: 1) measure changes in students’ perceptions of theirconfidence in teaching and 2) estimate changes in students’ viewpoints toward teaching as anopportunity to enhance transferable professional skills. To these ends, we administered twosurveys before and after the course: the STEM GTA Teaching Self-Efficacy Scale 5 and a modifiedskills perception inventory. 6 This section discusses the demographics of the students whoparticipated in this evaluation and their responses to the

Conference Session

International Division Technical Session 7

Collection

2014 ASEE Annual Conference & Exposition

Authors

Irene B Mena, Pennsylvania State University, University Park; Thomas A. Litzinger, Pennsylvania State University, University Park; Sarah E. Zappe, Pennsylvania State University, University Park

Tagged Divisions

International

these scales had a strong internal consistency (see results below). Finally, t-testswere conducted on each of the subscales, for both surveys, to determine any significantdifferences in experiences or perceptions between international and domestic students.ResultsIn this section, we describe results from the first-year survey and from the second-year survey.First-year surveyOur first-year survey consisted of seven scales: 1) Self-efficacy: This scale consisted of eight items, all related to students’ perceived levels of self-efficacy. It had the goal of revealing students’ levels of confidence in their abilities to succeed in engineering. 2) Knowledge of the engineering profession: The five items in this scale asked students

Collection

2021 ASEE St. Lawrence Section Conference

Authors

Matthew Jordan Ford, Cornell University; Soheil Fatehiboroujeni, Cornell University; Hadas Ritz, Cornell University

. 1c.One measure of whether or not an activity supports student agency is the diversity of solutionsgenerated by students [3]. We analyzed 36 reports from the final guided-inquiry lab and coded theexperimental procedure on five key decisions such as the type of experiment performed, specimengeometry, and measurement method. We identified 29 unique approaches to the problem, with noone approach accounting for more than three submissions.Analysis of student outcomes. Student outcomes were measured by a survey of students’attitudes and self-efficacy administered directly after every lab activity except for the first one.The fraction of students endorsing statements related to a sense of agency increased dramaticallybetween the “traditional” labs and

Conference Session

STEM Issues

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Laura Kasson Fiss, Michigan Technological University; John L. Irwin, Michigan Technological University; Sarah Tan, Michigan Technological University

Tagged Topics

Diversity

Tagged Divisions

Engineering Technology

, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy forlearning and performance, critical thinking, and metacognitive self-regulation; 2) the Change-Readiness Assessment [10] which assess 7 subscales, including adventurousness, confidence,adaptability, drive, optimism, resourcefulness, and tolerance for ambiguity; 3) PersistenceMeasures [11] which measures 3 responses including graduate study, career, and intent to changemajor; and 4) the Longitudinal Assessment in Engineering Self-Efficacy [12] which providesresults in six subscales, including self-efficacy, sense of belonging, and career expectations. Allof the questions are related to the course and/or learning environment. These questionnairesemploy 7-point Likert

Conference Session

NSF Grantees Poster Session I

Collection

2016 ASEE Annual Conference & Exposition

Authors

Kimberly Grau Talley P.E., Texas State University, San Marcos; Araceli Martinez Ortiz, Texas State University, San Marcos; Clara Novoa, Texas State University, San Marcos; Vedaraman Sriraman, Texas State University, San Marcos

Tagged Topics

Diversity, NSF Grantees Poster Session

. Instead, the researchers are customizing a University Seminar (US 1100) section, whichis an introduction to the university freshman seminar course, specifically for engineering andengineering technology majors while exploring research questions related to the development ofstudent design self-efficacy. This paper presents this work in progress including preliminaryresults from pre- and post-project engineering design self-efficacy measures of the initial cohort,lessons learned, and plans for future work.BackgroundThe Texas State STEM Rising Stars project is using a three-sided organizing framework, asshown in Figure 1, to guide the interventions and its associated research plan. This framework isbased upon Swail’s geometric model for student

Conference Session

Life After Graduation

Collection

2017 ASEE Annual Conference & Exposition

Authors

Bernhard Schadl, Stanford University; Sheri Sheppard, Stanford University; Helen L. Chen, Stanford University

Tagged Divisions

Educational Research and Methods

-efficacy as described earlier (see Theoretical Background section). In the development of theEMS, this construct was adapted to capture a student’s confidence in his or her abilities ingenerating and gathering new ideas – labeled as Innovation Self-Efficacy. In a similar way, astudent’s confidence in his or her abilities to design and develop new technical prototypes,products or services was included and measured in a variable named Engineering task self-efficacy. For both types of self-efficacy, students were asked to rate their levels of confidencein several innovation- or engineering-related activities. All of those activities were measuredon a five-point Likert scale from “Not confident” (0) to “Extremely confident” (4). For eachtype, the