Conference Session

Motivation and Self-Efficacy

Collection

2013 ASEE Annual Conference & Exposition

Authors

Hannah Kolar, Arizona State University; Adam R Carberry, Arizona State University; Ashish Amresh, Arizona State University

Tagged Divisions

Educational Research and Methods

ToolAbstractThis study was based around the creation of a tool to measure students computing self-efficacy. The tool was an eight-question survey that was validated using content andcriterion-related validity. Content validity was conducted to make sure that the questionsrelated to each other and related to the subject of computing self-efficacy. Criterion-related validity allowed us to validate that our tool could test people with different levelsof computing skills based on previous experience. The study allowed us to furthervalidate our tool as well as analyze the computing self-efficacy of 270 students inscience, technology, engineering, and mathematics (STEM) majors.IntroductionUniversities play a key role in creating future innovations and providing

Conference Session

Motivation and Self-Efficacy

Collection

2013 ASEE Annual Conference & Exposition

Authors

Rachel McCord, Virginia Tech; Holly M Matusovich, Virginia Tech

Tagged Divisions

Educational Research and Methods

as repeating questions thatwere reverse coded. The final Motivation section for the Phase 1 Survey contains 25 questions that cover 8motivation constructs: extrinsic and intrinsic motivation, interest, attainment value, cost value,identification with academics, self-efficacy and instrumentality. All constructs are measured on a7-point Likert scale ranging from not true at all (1) to very true (7).Developing the Learning Strategies Section To develop an appropriate survey to measure learning strategies used in collegethermodynamics courses, we started with a literature review to identify existing learningstrategies instruments. The following learning strategies inventories were considered for the

Conference Session

Motivation and Self-Efficacy

Collection

2013 ASEE Annual Conference & Exposition

Authors

Philip Reid Brown, Virginia Tech; Holly M Matusovich, Virginia Tech

Tagged Divisions

Educational Research and Methods

task, the expected outcome of a task16-18 and belief about one’s abilityto perform a task.24 To clarify our terms, we consider a theory is a big-picture idea of how aphenomenon works (expectancy-value theory offers an explanation of the entire process ofchoosing to perform a task) and a construct to be a single, measureable component of a theory(e.g., self-efficacy). The pursuit of a career in engineering and the completion of an engineering degree canboth be thought of as tasks, and research around them lends itself to motivation theories.Applications of motivation theories to tasks that are ultimately relevant to career choice includestudies using motivation to study enrollment and persistence in engineering programs21,26,student

Conference Session

Motivation and Self-Efficacy

Collection

2013 ASEE Annual Conference & Exposition

Authors

Robert Melendy Ph.D., George Fox University

Tagged Divisions

Educational Research and Methods

instrument items measure anunderlying (latent) construct. Confirmatory factor analysis indicates that these two scales areindependent, thus adding to the construct validity of this instrument. The paper concludes with adiscussion concerning how students’ SE and OE beliefs are postulated to affect students’problem solving skills of upper-division electrical and mechanical engineering problems.IntroductionCalculus, linear algebra, and differential equations are a foundational and distinguishing analyticcourse of study central to any four year engineering curriculum. Engineering students’ beliefs intheir ability to successfully apply the mathematical concepts from these courses to their upper-division course work (i.e., students’ self-efficacy) was

Conference Session

Retention and Persistence in Engineering

Collection

2013 ASEE Annual Conference & Exposition

Authors

Maria-Isabel Carnasciali, University of New Haven; Amy E Thompson, University of New Haven; Terance Joshua Thomas, University of New Haven

Tagged Divisions

Educational Research and Methods

ofstudy.Influences upon the Choice of Major DecisionSocial cognitive career theory (SCCT) as proposed by Lent et al.5 hypothesizes that behavior(choice of career) is a function of the dynamic interplay between beliefs and environmentalconditions. General social cognitive theory suggests that self-efficacy beliefs determine whetheran action will be pursued, how much effort will be given to that pursuit, the persistence in theface of obstacles and ultimately the performance level of the action.6 In 1996, Lent, Brown andHackett,7 proposed a concentric model of environmental layers that surround the person andform the context for his or her career behavior. Furthermore, a person with interest in a particularcareer path is unlikely to pursue that path if the

Conference Session

Retention and Persistence in Engineering

Collection

2013 ASEE Annual Conference & Exposition

Authors

Corey T Schimpf, Purdue University, West Lafayette; George D Ricco, Purdue University, West Lafayette; Matthew W. Ohland, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

Classification System for Engineering Students Characteristics Affecting College Enrollment and Retention,” Journal of Engineering Education, vol. 98, pp. 361-376, 2009.5 Marra, R.M., Rodgers, K.A., Shen D. & Bogue B., “Women Engineering Students and Self-Efficacy: Multi-Year, Multi-Institution Study of Women Engineering Student Self-Efficacy,”Journal of Engineering Education, vol. 98, pp. 27-38, 2009.6 Hartman, M., & Hartman, M. "Leaving Engineering: Lessons from Rowan University? s College of Engineering'” Journal of Engineering Education, vol. 95, pp. 49-61, 2006.7 Bestfield-Sacre, M., Moreno, M., Shuman, L.J. & Atman, C.J., “Gender and Ethnicity Differences in Freshman Engineering Student Attitudes: A

Conference Session

Retention and Persistence in Engineering

Collection

2013 ASEE Annual Conference & Exposition

Authors

Geoff Potvin, Clemson University; Zahra Hazari, Florida International University; Leidy Klotz, Clemson University; Allison Godwin, Clemson University; Robynne M. Lock, Department of Engineering and Science Education, Clemson University; Jennifer Dawn Cribbs, Western Kentucky University; Nicole Barclay, Clemson University

Tagged Divisions

Educational Research and Methods

carrying out the tasks as part ofthe role, and their beliefs in their ability to perform in the role. In fact, previous work14,16 hasshown that the performance and competence domains are not statistically independent and,instead, load together in factor analyses. Thus, there are three statistically distinct aspects ofone's identity in a subject: interest, recognition, and performance/competence.Another framework with a long and venerated history of use in understanding engineeringstudent career choice is the social cognitive career theory17,18. This framework, implementing thesocial cognitive theories of Bandura19 in the domain of career choice, uses two affectiveconstructs in particular: self-efficacy beliefs (which has some overlap with the

Conference Session

Teaching with Technology

Collection

2013 ASEE Annual Conference & Exposition

Authors

Alejandra J. Magana, Purdue University, West Lafayette; Michael L Falk, Johns Hopkins University; Mike Reese, Johns Hopkins University; Camilo Vieira, Purdue University

Tagged Divisions

Educational Research and Methods

takingmultiple non-CPMSE computing courses. Also, although students’ perceptions regarding utilityand intention of use did not show significant increase from the pretest to the posttest, they did notdecrease either. And both of them showed a reasonable positive score during the pretest (Utility= 3.43, Intention of Use = 2.78).The results of this study can be explained through the lens of the literature in self-efficacy.Previous research about student self-efficacy has identified that students’ confidence in theirabilities to complete a variety of tasks, specifically mathematical-related tasks in courses at thecollege level, predicted their future interests in mathematics courses 39. We believe that this mayalso be the case with exposure to

Conference Session

Engineering Identity 1

Collection

2013 ASEE Annual Conference & Exposition

Authors

Lorraine N. Fleming, Howard University; Kalynda Chivon Smith, Howard University; Dawn G. Williams, Howard University; Leonard B. Bliss, Florida International University

Tagged Divisions

Educational Research and Methods

programs, based on Tinto’s theory of retention. The second survey, theEngineering Fields Questionnaire was constructed and validated as described in Lent, et al.33 toprobe students’ self-efficacy, outcome expectations, and distal and proximal contextualinfluences. Participants’ demographic data was also collected.Semi-structured interviews. The one-on-one semi-structured interview design was astandardized list of questions that allowed for additional probing when deemed necessary. Thesemi-structured interviews were aligned with the survey and allowed for the collection ofspecific information related to engineering education, particularly identity development. Theresearchers were conscious of the participants’ perspective and oftentimes adjusted

Conference Session

First Year Engineering

Collection

2013 ASEE Annual Conference & Exposition

Authors

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

Tagged Divisions

Educational Research and Methods

addition, they shared some of the positive experiences that characterizedtheir first year of engineering.The interviews were coded by an engineering education researcher who followed the stepssuggested by Patton10. First, all the interviews were read to determine possible codingcategories. Then, the interviews were again read with the purpose of “coding in a systematicway” (p.463). Finally, categories were determined by looking at the “recurring regularities”(p.465), or patterns, in the data. This process was used to determine categories for eachparticipant, and then to determine categories that were common across participants.ResultsWhen developing the survey, we had classified items into the following categories: self-efficacy,knowledge of the

Conference Session

Novel Pedagogies 2

Collection

2013 ASEE Annual Conference & Exposition

Authors

Liu Junhua, Singapore University of Technology and Design; Yue Zhang, Singapore University and Technology and Design; Justin Ruths, Singapore University of Technology and Design; Diana Moreno, Singapore University of Technology and Design (SUTD); Daniel D. Jensen, U.S. Air Force Academy; Kristin L. Wood, Singapore University of Technology and Design (SUTD)

Tagged Divisions

Educational Research and Methods