Paper ID #15784Development of the Leadership Self-efficacy Scale for Engineering StudentsDr. So Yoon Yoon, Texas A&M University So Yoon Yoon, Ph.D., is a post-doctoral research associate at Texas A&M University. She received her Ph.D. and M.S.Ed.in Educational Psychology with specialties in Gifted Education and Research Methods & Measurement from Purdue University. Her work centers on P-16 engineering education research as a psychometrician, program evaluator, and institutional data analyst. As a psychometrician, she revised the PSVT:R (Purdue Spatial Visualization Tests: Visualization of Rotations) for
and faculty place oncommunication skills, the students’ perceptions of themselves as communicators, how thoseskills are developed within the wider curriculum, how proficient the students are upongraduation, and how these capabilities can be strengthened through improved pedagogicalmethods. Throughout the study, we use five different data collection techniques: (1) aninventory of the types and frequency of communication instruction and assignments through acontent analysis of syllabi; (2) two online student surveys, one administered at the beginning ofthe students’ undergraduate career and one given before graduation, to measure self-efficacy forcommunication; (3) a faculty survey to gauge the value instructors place on communication, aswell
. (2010). Measuring Engineering Design Self - Efficacy. Journal of Engineering Education, 99(1), 71-79.13. Kusurkar, R. A., Ten Cate, T. J., Vos, C. M. P., Westers, P., & Croiset, G. (2012). How motivation affects academic performance: A structural equation modelling analysis.14. Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-Motivation for Academic Attainment: The Role of Self-Efficacy Beliefs and Personal Goal Setting. American Educational Research Journal, 29(3), 663- 676.15. Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist, 25, 71-86.16. R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical
engineeringpersistence49,50. Performance/competence beliefs are broader than self-efficacy, which has beentraditionally measured as task-specific attainment51. Students’ beliefs about their ability toperform the practices of their discipline and understand the content of their discipline – whetherscience, math, or engineering – has an impact on their ability to see themselves as the kind ofperson who can legitimately participate in these areas52.Figure 1. Framework for students’ identification with engineering adapted from Hazari et al.16These three factors (recognition, interest, and performance/competence) comprise the identitymeasures developed in this work and are consistent with prior literature from psychology,sociology, science education, and engineering
and perceptions regarding engineering.Additionally, changes in teachers’ self-efficacy of teaching engineering and students’ attitudesabout science and engineering were measured. This article discusses the value of elementaryengineering education in rural communities.Keywords: Engineering education; professional development; elementary; rural schoolsIntroduction Science education in elementary (K-6) curriculum is often lacking and leads towidespread lack of preparation and misconceptions about fundamental science ideas in middleand high school students.1 Researchers have documented that elementary classroom scienceinstruction is typically limited and of low quality.2,3,4,5 Further, results from a 2013 nationalsurvey indicated that
whenselecting a test.6, 19, 21, 22, 23, 24, 25, 26 While each test measures a slightly different aspect of the broadtopic of spatial skills, many of them correlate highly with one another. Since this study calls for ameasure of general spatial skills, the authors chose a revised version of the PSVT:R test to assessparticipants’ spatial skills.Authors’ Previous Work Previous work by the authors indicates that individuals’ spatial ability differ by gender,age, and ethnicity.27 However, differences were not found on variables such as a student’sclassification (or year in school), early life experiences, and college major. Motivational factors,particularly domain-specific self-efficacy, are positively correlated with individuals’ spatial
isevident and supported by Table 2. Despite this lack of coherence, these studies have beenimportant first steps in exploring specific aspects of identity development in engineering. Closely related to identity but not explicitly stated, others have provided a review andanalysis of existing research on the measurement of the characteristics of engineering students inorder to illuminate factors that affect college enrollment and retention.12 The authors, Li,Swaminathan, and Tang, found that many researchers are specifically looking at the factors thathelp or hinder the matriculation of underrepresented groups into engineering. Marra, Rodgers,Shen, and Bogue conducted a multi-institution study on self-efficacy and women engineeringstudents.36
student preference 3,4 , self-efficacy5 and studentengagement6 . Although most studies have found no differences in measured learning gains 4,7,8 a fewhave9,10 .Although our previous work showed no differences in learning gains as measured by final exam scores 4 ,we wondered if a flipped classroom could create a more motivating classroom climate. One motivationtheory11 states that a student’s motivation to learn is based on three levers. The first levers is value. Dostudents see value in the content? The second lever is self-efficacy. Do students believe they can do wellin the class? Specifically, if a student has high efficacy expectancies, they believe that they are “capableof identifying, organizing, initiating and executing a course of action
forLearning Questionnaire (MSLQ), Metacognitive Awareness Scale (Schraw & Dennison), and theSTEM Questionnaires developed by the STEM team at the Higher Education Research Institute(HERI). A factor analysis was conducted on the pretest survey questions to determine whichquestions were most appropriate to represent the various constructs of interest including self-efficacy for learning, metacognitive self-regulation, peer learning, and help seeking behavior.Based on these data, a truncated scale was administered to students at posttest. Items used as partof the posttest include 14 items from the MSLQ and 4 items from the Metacognitive AwarenessScale (MAS). The posttest also included additional items from the HERI questionnaire as well ascourse
examined HSB within an undergraduate engineering context.Primary efforts are quantitative which, due to typical engineering demographics, limits the voiceof minority constituents. The purpose of this research is to develop a rich, empiricalunderstanding of engineering students’ lived experiences of HSB ensuring the perspective ofunderrepresented groups. Self-efficacy (SE) and self-theory of intelligence (STOI) wereexamined as inputs into HSB.This qualitative research is based on interviews of students’ perceptions and constant-comparative techniques drawn from grounded theory. A multi-approach sampling method wasused to ensure varied experiences, equal gender, and ethnic diversity. Results indicate adiversity of themes related to SE and STOI as
STEM-literacy for students majoring in engineering, thehumanities, or social sciences. Additionally, the course aims to positively impact students’ affectby attending to their motivation, attitudes, beliefs and self-efficacy towards STEM content andengineering as a creative profession. With fewer than 40% of college students intending onmajoring in STEM graduating, there is a need to address retention and graduation in highereducation1. Furthermore, as noted by the NAE2 and the ASEE3 it is important for all students toappreciate the central role of engineering in all facets of modern life. The civil engineering ideasdisseminated by the Structures course are vital to STEM majors and students majoring in thehumanities and social sciences alike
Framework concepts (motivation, confidence, learning, and professional identity).Through incorporating these concepts, the CSP naturally promotes active learning, introducesundergraduate research in freshman year, and develops peer and faculty mentors to support alearning environment.Table 1. Four concepts positively correlated to persistence in STEM are core to the CSP.Persistence Framework concept Pillar of the CSPMotivation: the intention or desire to pursue Using a personally relevant, societal granda goal 3,7,8 challenge (cancer) as a driving topic for educationConfidence or self-efficacy: the belief that Engagement in research from
in response to thestudents’ journal entries. Section 5 presents and discusses survey data collected from thestudents on the relative usefulness of the remediation measures. Finally, Section 6 includesconclusions taken from the work.2. Program Description and Cohort DemographicsThe SPIRIT Program (Scholarship Program Initiative via Recruitment, Innovation andTransformation) at WCU, funded by the National Science Foundation, aims to provide assistanceto academically gifted and financially needy students who are seeking degrees in engineering orengineering technology in the host department. The program7 promotes student self-efficacy andretention through intensive mentoring by four program directors, undergraduate research withfaculty guidance
encouraging collaboration andreducing competition may increase achievement. Cooperative learning is one such instructionalstrategy that has been shown to improve affective outcomes, such as self-efficacy in students.12Cooperative Learning Group learning can take a variety of forms, and many of the terms to describe these formsare used interchangeably (e.g., cooperative, collaborative and problem-based learning13). Thesegroup learning varieties have been found to increase student motivation and achievement, butdiffer in terms of level of task structure, the assignment of roles to group members, use of sharedmaterials, involvement of instructors, and built-in reflection on the process. One particular grouplearning structure used in the current
Instrument Designed to Investigate Elements of Science Students’ Metacognition, Self-Efficacy and Learning Processes: The SEMLI-S. International Journal of Science Education 30, 1701-1724, doi:10.1080/09500690701482493 (2008).22 Martin, A. J. Enhancing student motivation and engagement: The effects of a multidimensional intervention. Contemporary Educational Psychology 33, 239-269 (2008).
(recently) sexual minorities within higher educationSTEM programs. Likewise retention research highlighting additional corroborating factors instudent struggles, such as self-efficacy and cognitive attributes4,5,6, has informed the efforts ofsome of these support programs in affective and academic dimensions. Qualitative researchstrands that look at identity and marginalization have documented struggles from the studentperspective, noting how aspects of self can contribute to or come into conflict with one’sprogress and prosperity within a STEM major7,8,9. This research often employs a metaphor of“cultural mismatch” or “identity mismatch” to help extend the empathy and perspective ofpractitioners and those involved in the day to day of STEM in
Laura Hirshfield is a postdoctoral researcher and lecturer at the University of Michigan. She received her B.S. from the University of Michigan and her Ph.D. from Purdue University, both in chemical engineering. She then transitioned into the engineering education field by completing a post-doctoral appointment at Oregon State University investigating technology-aided conceptual learning. She is currently doing research on self-efficacy in project-based learning.Amanda Siebert-Evenstone, University of Wisconsin - MadisonGolnaz Arastoopour, University of Wisconsin - Madison Golnaz Arastoopour is a Ph.D. student in Learning Sciences at the University of Wisconsin-Madison. Before becoming interested in education, Golnaz
field of computing.Mr. Andrew Jackson, Purdue University, West Lafayette Andrew Jackson is currently pursuing a PhD in Technology through Purdue’s Polytechnic Institute. His previous middle school teaching experience informs his role as a graduate teaching assistant for TECH 120, an introductory course in design thinking. He recently completed his Master of Science in Technol- ogy Leadership and Innovation from Purdue University with a thesis investigating middle school engi- neering self-efficacy beliefs. His research interests are engineering self-efficacy, creativity, and decision making.Prof. Nathan Mentzer, Purdue University, West Lafayette Nathan Mentzer is an assistant professor in the College of Technology
persistence in an academic area is primarily influenced by twothings: expectancy for success and subjective task value. It has been a relatively consistentfinding that expectation for success (confidence or self-efficacy) will predict children’sachievement, while subjective task value (usefulness or enjoyableness) will predict children’spersistence and selection in any given subject.20In one application, Simpkins et al.21 explored the relationship between students’ interest andpersistence in science classes and students’ interest and understanding of science careers.Researchers concluded science activity predicted expectancy and subjective task value (confidentstudents also considered science careers) and proposed that exposure might increase
,” showeda drop of 1.75 (P value < 0.00001 using an unranked T-test), from an initial 6.36 to a final 4.61.Interestingly, the place of these scores almost exactly reverses Q2, going from the highest initialscore to the lowest final score. We have not seen previous studies on this drop in self-efficacy ata time of increasing knowledge in the literatures of writing or communication. We understandthis shift as a clear indicator of a transition stage between novice and expert, and as a step inprofessionalization.We also saw a small increase (+ 0.35, P value 0.0193 using an unranked T-test) in Q8, “Iunderstand how to reflect on the communication choices I make in light of context, purpose, andaudience.” These terms were used consistently in workshops
projects focusing on engaging stakeholders in forest management issues, surveys on public values of cultural ecosystem services, and psychographic market segmentation of sustainable tourism.Dr. Denise Wilson, University of Washington Denise Wilson is a professor of electrical engineering at the University of Washington, Seattle. Her research interests in engineering education focus on the role of self-efficacy, belonging, and other non- cognitive aspects of the student experience on engagement, success, and persistence and on effective methods for teaching global issues such as those pertaining to sustainability. c American Society for Engineering Education, 2016 Cross-Validation of a
dissociation from engineering but is more a measure of one’s “fit”14. FGS students may seetheir salient identity as separate from engineering, but they choose to associate (major in)engineering and thus take on engineering’s group affiliation. Social identity serves as theoverlying structure guiding our work. This theory serves to potentially bridge the gap betweenengineering identity and belongingness to engineering. Additionally, the role of social capitalfalls into this theory as it serves to moderate entrance into the engineering group and thedevelopment of feelings of belongingness in engineering. Identity, belongingness, and socialcapital will be used to measure the students’ engineering social identity for this study. Explicitframing of how we
mathematics (STEM) graduates1, and education and psychology research hasshown that motivation has an effect on student success in STEM fields2–4. As described by theFuture Time Perspective (FTP) theory, motivational attributes have been shown to positivelyaffect student achievement and persistence5. Additionally, Self-Regulated Learning (SRL) hasbeen positively linked with increased self-efficacy of undergraduates6. FTP and SRL have oftenbeen researched separately, but previous literature has reported that there is a link between thesetwo areas2,7–11. We seek to observe the student experience in terms of FTP and how FTP affectsstudent task-specific behavior in terms of SRL, thus investigating this link for engineeringstudents. This paper describes a
-cognitive factor are included.In the few studies that attempt to combine non-cognitive factors alongside cognitive ability in aneffort to explain college GPA, it has been shown that non-cognitive factors such as study skillsand effort explain significant variance in college GPA beyond cognitive ability[8], [9]. One studyhas shown that learning skills and study strategies alone can provide a 10% increase in predictivevalidity when added to cognitive-only models of academic performance[9]. Similarly, a recentmeta-analysis showed that non-cognitive factors such as conscientiousness, test anxiety, andacademic self-efficacy can explain as much variance in college GPA as high school GPA andSAT scores [10]. While these studies provide intriguing results
who start as freshmen in engineering complete theirbaccalaureate degrees in engineering1. Reasons for this attrition among engineering studentshave been studied for many years. Seymour and Hewitt2 found two main reasons for departuresfrom the sciences: disinterest or disappointment in field, and poor academic performance withsubsequent loss of self-efficacy. Haag et al.3 also found that poor academic advising,unapproachable faculty, and complicated engineering curricula were important institutionalcontributors to student attrition. Although poor academic performance may motivate somestudents to leave engineering, other students persist despite these academic setbacks. In thispaper, rather than focusing on students who leave engineering, we
studying earlier and outline the chapters for review). Self-knowledge also includes aspectsof motivation for learning. For example, is the learner pursuing the learning through an intrinsic(“this is interesting”) or extrinsic (“I want a good grade”) orientation, and what about thelearner’s self-efficacy? Research over the past 40 years has conclusively demonstrated the effectiveness of learningaccompanied by metacognition [see, for example: refs.17, 18, 19, 20]. Although few of these studieshave been based in engineering or science, the evidence seems clearly extendable to theselearning environments. As Pintrich13 states, “Because metacognitive knowledge in general ispositively linked to student learning, explicitly teaching metacognitive
,encouraging teacher-student dialogue, improving student motivation and self-esteem, bridgingthe gap between current and expected performance, and ultimately improving teaching.32Narciss33 identifies two components of feedback – the evaluative part, which assesses the qualityof the answer, and the informational part, which provides direction for progress. Shute1 reviewsa similar model, according to which feedback contains both verification and elaborationcomponents. A more informative feedback is found to be related to better performance, and insome cases, better motivation33. Whether or not more information in the feedback improvesstudent motivation depends on the student’s confidence in their own abilities (or self-efficacy)34.Different frameworks
better understanding of their early career work. Drawing from the PEARS data,Brunhaver4 showed that engineering graduates who were non-engineering focused four yearsafter earning their degree were different from their engineering focused peers in terms of certainundergraduate experiences (e.g., they were less likely to have participated in an internship or co-op) and level of technical interests. Moreover, while women and men graduates in this samplewere not different in terms of their current position (engineering or non-engineering), they weredifferent in terms of future plans. Women tended to have lower technical self-efficacy andinterests than did men, which helped to explain why they were more non-engineering focused intheir
from a single university instead of multiple institutions. Including more data from differentuniversities would give more validity to the results and increase the generalizability of the study.A second shortcoming was that due to small sample sizes, only two races were included in thestudy – White and Black. Other races/ethnicities, such as Hispanic, Asian or Pacific Islander, andAmerican Indian/Alaskan Native, were not included as they collectively represented less thanfive percent of the total population of participants. Furthermore, the data used did not containvariables such as marital status, SES, self-efficacy, and transfer credit/dual enrollment. Otherstudies have indicated that these variables may have an effect on first year grades of
numerous national and international conferences in the fields of education and women’s studies (AERA, AESA, & NWSA). In 2009, Beckett served as a Program Evaluator for the world renowned Apprenticeship in Ecological Horticulture at the Center for Agroecology and Sustainable Food Systems (CASFS) at UC Santa Cruz. She co-authored an evaluation of two decades of the apprenticeship program (Perez, Par, & Beckett, 2010). She served as the Program Evaluator for Apprenticeships in Sustainability Science and Engineering Design (ASCEND) at UC Santa Cruz in the 2014-2015 academic year, where she collaborated with the Program Director to build new assessment to measure STEM learning through ”audio diaries,” and piloted an