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Measuring Self-Efficacy in Engineering Courses – Impact of Learning Style Preferences

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Insights for Teaching ECE Courses - Session I

Tagged Division

Electrical and Computer

Page Count

13

DOI

10.18260/1-2--33092

Permanent URL

https://peer.asee.org/33092

Download Count

329

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Paper Authors

biography

Safeer M. Khan P.E. Arkansas Tech University

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Muhammad Khan received Ph. D. degree in Electrical and Computer Engineering from the University of North Carolina at Charlotte, Charlotte, NC, USA in 2013. He is an Assistant Professor in the Department of Electrical Engineering at Arkansas Tech University. Previously, he was an Assistant Professor in the Department of Electronics Technology at University of Arkansas, Fort Smith. His research interests include signal processing for audio and acoustics, industrial automation and robotics, non-destructive evaluation, engineering/integrated STEM education and K-12 and higher education collaboration.

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Mohamed Ibrahim Arkansas Tech University

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Mohamed Ibrahim, PhD
Associate Professor of Curriculum and Instruction
College of Education
Arkansas Tech University
(479) 964-0583 ext. 2452

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Nansong Wu Arkansas Tech University

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Nansong Wu received M.S. and Ph.D. degrees in Electrical and Computer Engineering from Florida International University, Miami, FL, USA, in 2008 and 2012; and M.S. in Engineering Management in 2017. He is an assistant professor in the Department of Electrical Engineering, Arkansas Tech University. His research interests include embedded systems, image processing, wireless communication, and IoT applications.

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Abstract

Self-efficacy is an important outcome of engineering education as it relates to students' feeling, thoughts, motivations and behaviors. The key element of self-efficacy construct is a self-belief in one's abilities and has been described in detail in terms of Bandura's Social Cognitive Theory. Measuring self-efficacy of students in engineering courses is an important element of evaluating the overall effectiveness of engineering education. Traditional methods of judging student learning outcomes include quizzes, homework, exams, and course projects, with a primary focus on measuring student skills. It is important that along with mastering the skills, students should also possess self-belief that they will be able to perform required tasks with those skills. An important research question is: How to measure self-efficacy in engineering courses? This paper addresses this question by highlighting the results of a longitudinal study conducted on students in engineering modeling and design (junior-level) courses at Arkansas Tech University. This course is selected because the teaching method is based on project-based learning activities. Using the collected data, we have analyzed the effect of learning style preference on the perception of self-efficacy. Previous research has demonstrated that students have different preferred learning styles and they approach learning new information in different ways. Our collected data includes student responses on their learning styles including lectures/discussions, books/related written material, video/movies/media, hands-on activities, and a hybrid method. Paired Sample t-tests and one-way analysis of variance (ANOVA) are used to analyze the collected data. These methods allow us to determine any statistically significant differences between the self-efficacy scores at the start and end of the course. We also determine the impact of learning style preference on students’ perception of self-efficacy. Based on the collected data so far, results indicate that the self-efficacy of students improved equally, using project-based learning techniques, regardless of their learning style preferences.

Khan, S. M., & Ibrahim, M., & Wu, N. (2019, June), Measuring Self-Efficacy in Engineering Courses – Impact of Learning Style Preferences Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33092

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