Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Electrical and Computer
Student engagement has received growing attention in the education research community  primarily due to its power in predicting student learning outcomes , . 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 . 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"
 S. L. Christenson, A. L. Reschly, and C. Wylie, Handbook of research on student engagement. Springer Science & Business Media, 2012.  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.  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.
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. https://peer.asee.org/31005
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