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Distribution of Characteristic Ways That Students Think about the Future in Large Enrollment Engineering Classes

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Applied Frameworks

Tagged Division

Educational Research and Methods

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Paper Authors


Catherine Mcgough Spence Clemson University

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Catherine McGough is currently a graduate research assistant in Engineering and Science Education at Clemson University. She obtained her B.S. in Electrical Engineering from Clemson University in 2014. Her research interests are in undergraduate engineering student motivations and undergraduate engineering problem solving skill development and strategies.

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Lisa Benson Clemson University

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Lisa Benson is a Professor of Engineering and Science Education at Clemson University, with a joint appointment in Bioengineering. Her research focuses on the interactions between student motivation and their learning experiences. Her projects involve the study of student perceptions, beliefs and attitudes towards becoming engineers and scientists, and their problem solving processes. Other projects in the Benson group include effects of student-centered active learning, self-regulated learning, and incorporating engineering into secondary science and mathematics classrooms. Her education includes a B.S. in Bioengineering from the University of Vermont, and M.S. and Ph.D. in Bioengineering from Clemson University.

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In this research study, we seek to answer the question: What is the distribution of characteristic FTP types among undergraduate engineering students in large enrollment classes? In an ideal classroom, instructors would appeal to students’ individual motivations as a way to personalize instruction. Although this seems like an unrealistic goal, particularly in large enrollment classrooms, researchers are working to address the challenge of understanding students’ motivation for academic achievement. One way to understand students’ individual motivations is to look at their future goals and how those goals affect what they do now, or their Future Time Perspective (FTP). We used a validated survey instrument to assess students’ FTP and a K-means cluster analysis to identify the distribution of characteristic FTP types among the study participants.

Participants in this study were sophomore undergraduate civil , electrical , and mechanical engineering students in large enrollment classrooms (> 50:1 student to instructor ratio) at three universities (n=490 participants). The survey consists of demographic questions, contact information, and 30 items intended to measure 6 FTP constructs: 1) effect of the future on present actions, 2) perceived instrumentality of a task for future goals, 3) clarity of future goals, 4) value of the future, 5) time attitude towards future possible selves, and 6) perceptions of the future in engineering. To test internal consistency reliability of the survey, we ran maximum likelihood factor analysis with Promax rotation and calculated Cronbach’s alpha for each of the factors. Next, to identify the characteristic FTP types in the classroom, we ran a K-means cluster analysis, appropriate for large sample sizes where the number of clusters (k=3) is hypothesized based on theory or previous studies.

Results from one class (n=275), an introductory, large enrollment computer programming course at a large land-grant public research university in mid-western US, showed that the instrument is valid for this population. The cluster analysis for this class resulted in three groups with factor means consistent with the characteristic FTP types based on theory and previous studies. The distribution of these characteristic FTP types in this classroom indicated that a majority of the students have well defined goals that extend deep into the future. The factor means showed that students in this class have a moderately high perceived instrumentality (5.1 out of 7) for the class, indicating that they found information they are learning to be relevant to their well-defined future goals and that they consider completing the class an important step in reaching their future goals. A small portion of the class has moderately well-defined future goals but low (2.8 out of 7) attitudes about the future.

This paper will describe the same analysis for the other three universities, and compare distributions of the characteristic FTP types by institution, major, class, and demographics. Results will help inform instructors of large enrollment classes how engineering students in these classes are thinking about the future. The thorough description of research methods will allow replication of this study, so that instructors who wish to do so may utilize this survey in their own classes. By focusing on students in large enrollment classes, our research can provide instructors some insight into what is motivating their students, so that they may personalize their instruction to align with their students’ motivations.

Spence, C. M., & Benson, L. (2017, June), Distribution of Characteristic Ways That Students Think about the Future in Large Enrollment Engineering Classes Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28187

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