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Identifying Factors That Influence Engineering Students' Outcome Expectancy and Learning Self-Efficacy in a Flipped CS1 Course

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Computing and Information Technology Division (CIT) Technical Session 1

Tagged Division

Computing and Information Technology Division (CIT)

Tagged Topic

Diversity

Page Count

13

DOI

10.18260/1-2--43428

Permanent URL

https://peer.asee.org/43428

Download Count

143

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Paper Authors

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Ashish Aggarwal University of Florida Orcid 16x16 orcid.org/0000-0002-8365-3810

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Ashish Aggarwal is an Instructional Assistant Professor of Computer Science in the Department of Engineering Education at the Herbert Wertheim College of Engineering, University of Florida. His research focuses on Computer Science Education and Learning Analytics where he studies the effectiveness of different learning approaches on students’ learning outcomes and performance in programming courses.

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Griffin Pitts University of Florida

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Griffin Pitts is currently an undergraduate computer science student with the University of Florida’s Herbert Wertheim College of Engineering. As a student, Griffin conducts research within multiple disciplines, furthering the impact of machine learning and artificial intelligence. He has been awarded by the University of Florida’s Center for Undergraduate Research and intends on attending graduate school in his future.

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Sage Bachus University of Florida

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Sage Bachus is a fourth-year Mechanical Engineering and Pre-Med student at the Herbert Wertheim College of Engineering, University of Florida. His main research focus is in learning analytics and developing a way to better understand the underlying intricacies of how students learn and perform.

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Sarah Rajkumari Jayasekaran University of Florida

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Sarah Jayasekaran (Dr J) is an instructional assistant professor at the University of Florida. She has a Master's in Structural Engineering and a Ph.D. in Civil Engineering from the University of Florida (UF). She is originally from the city of Chennai, India. Dr. J came to the United States to pursue her passion for teaching. Her research interest includes smart cities, smart concepts in education, student retention, and curriculum development.

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Saira Anwar Texas A&M University Orcid 16x16 orcid.org/0000-0001-6947-3226

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Saira Anwar is an Assistant Professor at Department of Multidisciplinary Engineering, Texas A &M University. Dr. Anwar has over 13 years of teaching experience, primarily in the disciplines of engineering education, computer science and software engineering. Her research focuses on studying the unique contribution of different instructional strategies on students' learning and motivation. Also, she is interested in designing interventions that help in understanding conceptually hard concepts in STEM courses. Dr. Anwar is the recipient of the 2020 outstanding researcher award by the School of Engineering Education, Purdue University. Also, she was the recipient of the "President of Pakistan Merit and Talent Scholarship" for her undergraduate studies.

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Abstract

As the importance of learning computational skills increases for undergraduate engineering students, it is important to explore the factors that influence their confidence in their ability to learn and their perception of their expected performance in a course. In technical and problem-solving-based courses, students often have preconceived beliefs about their abilities and performance. In the context of programming and for this study, we define learning self-efficacy as students' confidence in their ability to solve problems and learn to program. We also define outcome expectancy through students' perceptions of their expected final grades within a course. Students' learning self-efficacy and outcome expectancy are fundamental motivation constructs that may affect their participation in a course and influence their approach toward learning and performance. Although in the past, researchers have studied motivational and other factors like prior programming experience, self-regulation, level of practice, and task value, to predict students’ performance within a course, the literature is scarce on factors that underpin engineering students’ motivational beliefs related to learning programming. In this analysis, we explore the influence of prior programming experience (PPE), academic standing (GPA), and gender on students' learning self-efficacy and outcome expectancy. We analyze the data of 600 engineering students enrolled in a CS1 course and find that gender and PPE are statistically significant factors that influence students' learning self-efficacy. We also find that learning self-efficacy and GPA are statistically significant predictors of outcome expectancy. We believe these results will help advance our understanding of engineering students' motivational beliefs and help instructors identify specific groups of students that may need additional support and assistance.

Aggarwal, A., & Pitts, G., & Bachus, S., & Jayasekaran, S. R., & Anwar, S. (2023, June), Identifying Factors That Influence Engineering Students' Outcome Expectancy and Learning Self-Efficacy in a Flipped CS1 Course Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43428

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