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A GIS Based Laboratory in Transportation Engineering: Self-Efficacy as a Predictor of Students’ Learning

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Conference

2013 ASEE Annual Conference & Exposition

Location

Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013

ISSN

2153-5965

Conference Session

Civil Engineering Division Poster Session

Tagged Division

Civil Engineering

Page Count

16

Page Numbers

23.49.1 - 23.49.16

Permanent URL

https://peer.asee.org/19063

Download Count

30

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Paper Authors

biography

Ghulam H. Bham University of Alaska, Anchorage

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Dr. Ghulam H. Bham is a faculty member at the Civil Engineering Department at the University of Alaska, Anchorage. He received his M.S. and Ph.D. from the University of Illinois at Urbana-Champaign. His research interests include modeling and simulation of driver behavior in transportation systems, traffic operations and control, traffic safety, and engineering education.

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biography

Dan Cernusca Missouri University of Science & Technology

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Dr. Dan Cernusca is Instructional Design Specialist in the Department of Global Learning at the Missouri University of Science and Technology. He received his Ph.D. degree in Information Science and Learning Technologies in 2007 from University of Missouri – Columbia. He also holds a BS and a Ph.D. from the University of Sibiu in Romania with a specialization in manufacturing technologies and respectively cutting-tools design. His research interests include design-based research in technology-enabled learning contexts, technology-mediated problem solving, applications of dynamic modeling for learning of complex topics, and the impact of epistemic beliefs on learning with technology.

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Abstract

A GIS Based Laboratory in Transportation Engineering: Self-Efficacy as a Predictor of Students’ LearningThe focus of this paper is to identify and validate an exploratory model that links students’performance in a Geographic Information Systems (GIS) laboratory based learning system tomajor predictors based on students’ perceptions and beliefs. Structural equation modelingapproach was adopted in this study. The instructional process in the laboratory was similar to ashort-term training course, therefore, first, academic self-efficacy and its determinants, perceivedmotivation and perceived task complexity, were examined as significant predictors of students’performance outcomes. Second, the interest was to determine if the major change in theinstructional process during the last two semesters of the laboratory, the link of the laboratorytopic to the semester long highway design project, produced significant difference in students’self-efficacy.The results indicate that both perceived motivation and perceived complexity are significantpredictors for students’ academic self-efficacy. Higher self-efficacy produced the desiredoutcome as a result of better alignment of the complexity of the laboratory activity with the levelof expectations of students possibly based on their experiences in other similar courses. Higherself-efficacy typically results in higher chances that students will successfully engage in similarfuture activities. This outcome is of great interest as the GIS laboratory was set as a distributedlearning activity implemented as stand-alone laboratories in several courses throughout the civilengineering curricula. The model confirms that the instructional design interventions over thesemester increased students’ motivation. The data used for the analyses were obtained from alongitudinal study of student performance in a GIS laboratory over four semesters.  

Bham, G. H., & Cernusca, D. (2013, June), A GIS Based Laboratory in Transportation Engineering: Self-Efficacy as a Predictor of Students’ Learning Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/19063

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