Student Engagement Profiles in Discrete-time Signals and Systems Courses

Daria Gerasimova; Jill K. Nelson; Margret Hjalmarson

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Electrical and Computer Division Technical Session 2
Tagged Division: Electrical and Computer
Tagged Topic: Diversity
Page Count: 14
DOI: 10.18260/1-2--31005
Permanent URL: https://peer.asee.org/31005
Download Count: 441

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Paper Authors

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Daria Gerasimova George Mason University

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Daria (Dasha) Gerasimova is a Ph.D. student in Education at George Mason University. She studies primarily Mathematics Education, Educational Psychology, and Quantitative Research Methodology (with a particular emphasis on Applied Educational Measurement). Her interests in the substantive area of research include exploring the nature of student engagement in STEM and the interplay of engagement with motivational factors, learning outcomes, and instruction. In the area of methodology, she is interested in the processes of instrument development and validation.

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Jill K. Nelson George Mason University

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Jill Nelson is an associate professor in the Department of Electrical and Computer Engineering at George Mason University. She earned a BS in Electrical Engineering and a BA in Economics from Rice University in 1998. She attended the University of Illinois at Urbana-Champaign for graduate study, earning an MS and PhD in Electrical Engineering in 2001 and 2005, respectively. Dr. Nelson's research focus is in statistical signal processing, specifically detection and estimation for applications in target tracking and physical layer communications. Her work on target detection and tracking is funded by the Office of Naval Research. Dr. Nelson is a 2010 recipient of the NSF CAREER Award. She is a member of Phi Beta Kappa, Tau Beta Pi, Eta Kappa Nu, and the IEEE Signal Processing, Communications, and Education Societies.

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Margret Hjalmarson George Mason University

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Margret Hjalmarson is a Professor in the Graduate School of Education at George Mason University. Her research interests include engineering education, mathematics education, faculty development and mathematics teacher leadership.

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Abstract

Student engagement has received growing attention in the education research community [1] primarily due to its power in predicting student learning outcomes [2], [3]. However, engagement in electrical engineering undergraduate courses remains largely unexplored. In this study, we developed student engagement profiles in an undergraduate discrete-time signals and systems (DTSS) course and investigated potential differences in achievement among the profiles.

The data for this study were collected from 82 students in two offerings of the same DTSS course. The sample was predominantly male and racially diverse; most students were in their junior or senior years. Achievement was measured by final course points. At the end of the semester, students completed an engagement survey. The survey was designed by the researchers with respect to the theoretical framework of Fredricks, Blumenfeld, and Paris, which included three engagement dimensions: behavioral, cognitive, and emotional [2]. Only behavioral and emotional scales were used for the analysis; the cognitive scale showed low internal consistency. Exploratory factor analyses with the principal axis factoring as an extraction method and direct oblimin as a rotation method were conducted on the behavioral and emotional scales. The results revealed that the behavioral scale includes five factors (listening, note taking, asking questions, answering questions, and participating in group work), and the emotional scale includes three factors (emotions, interest, and attitudes toward group work).

To determine student engagement profiles, we conducted a cluster analysis based on the eight identified factors. The hierarchical cluster analysis suggested a four-cluster solution, which was confirmed by k-means. Students in the first profile (N=21) – Passive Learners – took notes intensively but were relatively unengaged based on other factors. Students in the second profile (N=26) – Absorbers – were characterized by their unwillingness to ask or answer questions in class while being actively engaged according to other factors. Students in the third profile (N=10) – Collaborators – were unwilling to take notes and answer questions in class but were engaged otherwise. Lastly, students in the fourth profile (N=25) – Engaged Learners – were highly engaged based on all factors. To explore the identified clusters with respect to achievement, we conducted an ANOVA analysis. It showed no significant differences between the clusters in achievement, F(3,82)=1.031, p=0.384.

The results of this study may help instructors understand what types of learners are in their classes and adjust their instruction accordingly. It may also point to diverse ways in which instructors may structure activities to engage students.

"Regular Presentation Preference"

[1] S. L. Christenson, A. L. Reschly, and C. Wylie, Handbook of research on student engagement. Springer Science & Business Media, 2012. [2] J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, “School engagement: Potential of the concept, state of the evidence,” Rev. Educ. Res., vol. 74, no. 1, pp. 59–109, 2004. [3] S. R. Jimerson, E. Campos, and J. L. Greif, “Toward an understanding of definitions and measures of school engagement and related terms,” Calif. Sch. Psychol., vol. 8, pp. 7–27, 2003.

Citation
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Gerasimova, D., & Nelson, J. K., & Hjalmarson, M. (2018, June), Student Engagement Profiles in Discrete-time Signals and Systems Courses Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31005

TY - CPAPER
AB - Student engagement has received growing attention in the education research community [1] primarily due to its power in predicting student learning outcomes [2], [3]. However, engagement in electrical engineering undergraduate courses remains largely unexplored. In this study, we developed student engagement profiles in an undergraduate discrete-time signals and systems (DTSS) course and investigated potential differences in achievement among the profiles.

The data for this study were collected from 82 students in two offerings of the same DTSS course. The sample was predominantly male and racially diverse; most students were in their junior or senior years. Achievement was measured by final course points. At the end of the semester, students completed an engagement survey. The survey was designed by the researchers with respect to the theoretical framework of Fredricks, Blumenfeld, and Paris, which included three engagement dimensions: behavioral, cognitive, and emotional [2]. Only behavioral and emotional scales were used for the analysis; the cognitive scale showed low internal consistency. Exploratory factor analyses with the principal axis factoring as an extraction method and direct oblimin as a rotation method were conducted on the behavioral and emotional scales. The results revealed that the behavioral scale includes five factors (listening, note taking, asking questions, answering questions, and participating in group work), and the emotional scale includes three factors (emotions, interest, and attitudes toward group work).

To determine student engagement profiles, we conducted a cluster analysis based on the eight identified factors. The hierarchical cluster analysis suggested a four-cluster solution, which was confirmed by k-means. Students in the first profile (N=21) – Passive Learners – took notes intensively but were relatively unengaged based on other factors. Students in the second profile (N=26) – Absorbers – were characterized by their unwillingness to ask or answer questions in class while being actively engaged according to other factors. Students in the third profile (N=10) – Collaborators – were unwilling to take notes and answer questions in class but were engaged otherwise. Lastly, students in the fourth profile (N=25) – Engaged Learners – were highly engaged based on all factors. To explore the identified clusters with respect to achievement, we conducted an ANOVA analysis. It showed no significant differences between the clusters in achievement, F(3,82)=1.031, p=0.384.

The results of this study may help instructors understand what types of learners are in their classes and adjust their instruction accordingly. It may also point to diverse ways in which instructors may structure activities to engage students.

&quot;Regular Presentation Preference&quot;

[1] S. L. Christenson, A. L. Reschly, and C. Wylie, Handbook of research on student engagement. Springer Science &amp; Business Media, 2012.
[2] J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, “School engagement: Potential of the concept, state of the evidence,” Rev. Educ. Res., vol. 74, no. 1, pp. 59–109, 2004.
[3] S. R. Jimerson, E. Campos, and J. L. Greif, “Toward an understanding of definitions and measures of school engagement and related terms,” Calif. Sch. Psychol., vol. 8, pp. 7–27, 2003.

AU - Daria Gerasimova
AU - Jill K. Nelson
AU - Margret Hjalmarson
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Student Engagement Profiles in Discrete-time Signals and Systems Courses
UR - https://peer.asee.org/31005
DO - 10.18260/1-2--31005
ER -