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WIP: The Predictive Power of Engineering Undergraduate Students’ Academic Self-efficacy and Test Anxiety for Their Academic Performance in a Dynamics Course

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Mechanical Engineering Technical Session: Dynamics I

Tagged Division

Mechanical Engineering

Page Count

12

DOI

10.18260/1-2--35575

Permanent URL

https://peer.asee.org/35575

Download Count

62

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Paper Authors

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Daeyeoul Lee Purdue University, West Lafayette

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Daeyeoul Lee is a PhD student in Learning Design and Technology Program at Purdue University. He is a research assistant in the School of Engineering Education at Purdue University. His research focuses on self-regulated learning, motivation, online learning, Massive Open Online Course, and digital technology.

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Jeffrey F. Rhoads Purdue University, West Lafayette

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Jeffrey F. Rhoads is a Professor in the School of Mechanical Engineering at Purdue University and is affiliated with both the Birck Nanotechnology Center and Ray W. Herrick Laboratories at the same institution. He received his B.S., M.S., and Ph.D. degrees, each in mechanical engineering, from Michigan State University in 2002, 2004, and 2007, respectively. Dr. Rhoads’ current research interests include the predictive design, analysis, and implementation of resonant micro/nanoelectromechanical systems (MEMS/NEMS) for use in chemical and biological sensing, electromechanical signal processing, and computing; the dynamics of parametrically-excited systems and coupled oscillators; the thermomechanics of energetic materials; additive manufacturing; and mechanics education. Dr. Rhoads is a Member of the American Society for Engineering Education (ASEE) and a Fellow of the American Society of Mechanical Engineers (ASME), where he serves on the Design Engineering Division’s Technical Committees on Micro/Nanosystems and Vibration and Sound, as well as the Design, Materials, and Manufacturing (DMM) Segment Leadership Team. Dr. Rhoads is a recipient of numerous research and teaching awards, including the National Science Foundation’s Faculty Early Career Development (CAREER) Award; the Purdue University School of Mechanical Engineering’s Harry L. Solberg Best Teacher Award (twice), Robert W. Fox Outstanding Instructor Award, and B.F.S. Schaefer Outstanding Young Faculty Scholar Award; the ASEE Mechanics Division’s Ferdinand P. Beer and E. Russell Johnston, Jr. Outstanding New Mechanics Educator Award; and the ASME C. D. Mote Jr., Early Career Award. In 2014 Dr. Rhoads was included in ASEE Prism Magazine’s 20 Under 40.

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Edward J. Berger Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0003-0337-7607

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Edward Berger is an Associate Professor of Engineering Education and Mechanical Engineering at Purdue University, joining Purdue in August 2014. He has been teaching mechanics for over 20 years, and has worked extensively on the integration and assessment of specific technology interventions in mechanics classes. He was one of the co-leaders in 2013-2014 of the ASEE Virtual Community of Practice (VCP) for mechanics educators across the country. His current research focuses on student problem-solving processes and use of worked examples, change models and evidence-based teaching practices in engineering curricula, and the role of non-cognitive and affective factors in student academic outcomes and overall success.

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Jennifer DeBoer Purdue University, West Lafayette

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Jennifer DeBoer is currently Assistant Professor of Engineering Education at Purdue University. Her research focuses on international education systems, individual and social development, technology use and STEM learning, and educational environments for diverse learners.

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Abstract

Self-regulated learning (SRL) is an important factor that positively affects students’ academic performance in general academic settings [1]. This factor has received increasing attention from the engineering and technology communities as of late [2] because it potentially provides useful information on how to adapt instructional strategies to help improve the students’ academic performance and retention [3]. A large number of engineering students who leave engineering majors and transfer to another major [4] do so because of factors such as a comparatively low GPA [5] and a lack of self-efficacy [4]. Dynamics is widely perceived as one of the most difficult sophomore-level courses by engineering students [6]. Although two motivational components of SRL, self-efficacy which is context-specific [7] and test anxiety, play a vital role in difficult university courses, studies of these constructs in mechanics education are rare. While some previous studies [8] found a significantly positive correlation between engineering students’ academic self-efficacy and their academic performance, others [9] did not, which showed inconsistent results. In addition, although a negative relationship between self-efficacy and test anxiety found in general academic settings provides a new insight on how to support them such as an intervention program using a cognitive behavioral treatment for test anxiety [10], a little has been known about it in mechanics education. This work-in-progress paper examines the effects of engineering undergraduate students’ academic self-efficacy and test anxiety on their grade point average (GPA) in a dynamics course using Pintrich’s SRL model [11] as a theoretical framework. The study encompasses two research questions: (i) What is the relationship between engineering undergraduate students’ academic self-efficacy and test anxiety in a dynamics course? and (ii) To what extent do engineering undergraduate students’ academic self-efficacy and test anxiety predict their academic performance in a dynamics course? Data for this study were collected during the Spring and Fall 2019 semesters in a dynamics course at a large Midwestern university (approximately 600 students). A survey was used to measure self-efficacy and test anxiety at the beginning of the course. Students were provided a small incentive to participate. Eight self-efficacy items and five test anxiety items from the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich, Smith, Garcia, and McKeachie [12] were used. To address the first research question, Pearson’s correlation analysis was employed. Likewise, to address the second research question, multiple linear regression analysis was used. Though data collection and analysis are incomplete to date, we hypothesize that there will be a negative relationship between engineering undergraduate students’ academic self-efficacy and test anxiety. In addition, we believe based on preliminary analyses that academic self-efficacy and test anxiety will significantly predict academic performance. The findings of this study will contribute to the growing body of empirical evidence related to the role of academic self-efficacy and test anxiety in engineering education settings. This work-in-progress paper examines the effects of engineering undergraduate students’ academic self-efficacy and test anxiety on their grade point average (GPA) in a dynamics course using Pintrich’s (2004) SRL model as a theoretical framework. The study encompasses two research questions: (i) What is the relationship between engineering undergraduate students’ academic self-efficacy and test anxiety in a dynamics course? and (ii) To what extent do engineering undergraduate students’ academic self-efficacy and test anxiety predict their academic performance in a dynamics course? Data for this study were collected during the Spring and Fall 2019 semesters in a dynamics course at a large Midwestern university (approximately 600 students). A survey was used to measure self-efficacy and test anxiety at the beginning of the course. Students were provided a small incentive to participate. Eight self-efficacy items and five test anxiety items from the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich, Smith, Garcia, and McKeachie (1993) were used. To address the first research question, Pearson’s correlation analysis was employed. Likewise, to address the second research question, multiple linear regression analysis was used. Though data collection and analysis are incomplete to date, we hypothesize that there will be a negative relationship between engineering undergraduate students’ academic self-efficacy and test anxiety. In addition, we believe based on preliminary analyses that academic self-efficacy and test anxiety will significantly predict academic performance. The findings of this study will contribute to the growing body of empirical evidence related to the role of academic self-efficacy and test anxiety in engineering education settings.

References [1] P. R. Pintrich, & E. V. De Groot, “Motivational and self-regulated learning components of classroom academic performance,” Journal of educational psychology, vol. 82, no. 1, pp. 33-40, 1990. [2] H. R. Shih, W. Zheng, E. J. Leggette, & G. Skelton, “Enhancing student performance by promoting Self-regulated learning,” Proceedings of the ASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011, Denver, CO, USA, November 11-17, 2011, American Society of Mechanical Engineers Digital Collection, 2011. pp. 469-477 [3] S. Y. Chyung, A. J. Moll, & S. A. Berg, “The role of intrinsic goal orientation, self-efficacy, and e-learning practice in engineering education,” Journal of Effective Teaching, vol. 10, no. 1, pp. 22-37, 2010. [4] B. N. Geisinger, & D. R. Raman, “Why they leave: Understanding student attrition from engineering majors,” International Journal of Engineering Education, vol. 29, no. 4, pp. 914-925, 2013. [5] L. E. Bernold, J. E. Spurlin, & C. M. Anson, “Understanding our students: A longitudinal‐study of success and failure in engineering with implications for increased retention,” Journal of Engineering Education, vol. 96, no.3, pp. 263-274, 2007. [6] M. A. Magill, “Classroom models for illustrating dynamics principles, part I-particle kinematics and kinetics,” Proceeding of American Society of Engineering Education Annual Conference and Exposition, Milwaukee, WI, USA, June 15-18 (pp. 15–18). Washington, DC: ASEE, 1997. lee [7] A. Bandura, Self-efficacy: The exercise of control. New York: W. H. Freeman and Company, 1997. [8] C. M. Vogt, “Faculty as a critical juncture in student retention and performance in engineering programs,” Journal of Engineering Education, vol. 97, no. 1, pp. 27-36, 2008. [9] M. Alias, Z. A. Akasah, & M. J. Kesot, “Relationships between locus of control, self-efficacy, efforts and academic achievement among engineering students,” in 2016 The 3rd International Conference on Industrial Engineering and Applications, ICIEA 2016, Hong Kong, April 28-30, 2016, pp.1-5. [10] E. Bresó, W. B. Schaufeli, & M. Salanova, “Can a self-efficacy-based intervention decrease burnout, increase engagement, and enhance performance? A quasi-experimental study,” Higher Education, 61(4), pp. 339-355, 2011. [11] P. R. Pintrich, “A conceptual framework for assessing motivation and self-regulated learning in college students,” Educational psychology review, vol. 16, no. 4, pp. 385-407, 2004. [12] P. R. Pintrich, D. A. Smith, T. Garcia, & W. J. McKeachie, “Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ),” Educational and psychological measurement, vol. 53, no. 3, pp. 801-813, 1993.

Lee, D., & Rhoads, J. F., & Berger, E. J., & DeBoer, J. (2020, June), WIP: The Predictive Power of Engineering Undergraduate Students’ Academic Self-efficacy and Test Anxiety for Their Academic Performance in a Dynamics Course Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35575

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