Evaluating the Effectiveness of Dynamic Traffic Animations: Case Study in Transportation Engineering Education
Publication: Journal of Professional Issues in Engineering Education and Practice
Volume 139, Issue 3
Abstract
Development of learning tools is critical for improving engineering education and teaching difficult engineering concepts. Representations are learning tools that can help students understand conceptual systems by providing insight to a concept, problem, or system through explicit cues. Researchers at the University of Idaho have developed structured activities involving animations that are representations of traffic simulations designed as part of the Mobile Signal Timing Training (MOST) project for teaching traffic signal timing. In this study, the effectiveness of the MOST animations were evaluated through a pre-/postcomparative case study. Overall, the MOST animations were successful in improving student understanding of timing parameters involved in actuated control at signalized intersections. The MOST activities were more effective than comparison methods in facilitating student learning for concepts of minimum green time, maximum green time, and duration of the green indication. Students also showed an improved understanding of the relationship between cycle length and delay and passage time, but not more so than comparison students. Results indicate that animations are effective in improving student understanding of concepts involving dynamic processes or reactions.
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References
Bham, G. H., Cernusca, D., Luna, R., and Manepalli, U. R. R. (2011). “Longitudinal evaluation of a GIS laboratory in a transportation engineering course.” J. Prof. Issues Eng. Educ. Pract., 137(4), 258–266.
Bransford, J. D., Brown, A. L., and Cocking, R. R., eds. (1999). How people learn: Brain, mind, experience, and school, Committee on Developments in the Science of Learning, National Research Council, National Academies, Washington, DC.
Brennan, T. M., Day, C. M., Sturdevant, J. R., and Bullock, D. M. (2012). “Visual Education Tools to Illustrate Coordinated System Operation.” J. Trans. Res. Board, 2259(1), 59–72.
Chi, M. T. H. (2009). “Active–constructive–interactive: A conceptual framework for differentiating learning activities.” Top. Cognit. Sci., 1(1), 73–105.
Greenspan, S. I. (2003). The clinical interview of the child, American Psychiatric, Arlington, VA.
Jamieson-Noel, D. L., and Winne, P. H. (2003). “Comparing self-reports to traces of studying behavior as representations of students’ studying and achievement.” Ger. J. Educ. Psychol., 17(3–4), 159–171.
Kyte, M. (2009). “MOST: A hands on approach to traffic signal timing education.” 〈http://www.webs1.uidaho.edu/most/〉 (May 1, 2012).
Kyte, M., Dixon, M., Abdel-Rahim, A., and Brown, S. (2009). “A process for improving the design of transportation curriculum materials with examples.”, Transportation Research Board, Washington, DC, 18–27.
Larkin, J., and Simon, H. (1987). “Why a diagram is (sometimes) worth ten thousand words.” Cognit. Sci., 11(1), 65–99.
Liao, C., Liu, H. X., and Levinson, D. M. (2009). “Simulating transportation for realistic engineering education and training.”, Transportation Research Board, Washington, DC, 12–21.
McDermott, L. C. (1984). “Research on conceptual understanding in mechanics.” Phys. Today, 37(7), 24–32.
NVivo 9.1.106.0 [Computer software]. (2011). Cambridge, MA, QSR.
Scaife, M., and Rogers, Y. (1996). “External cognition: How do graphical representations work?” Int. J. Hum. Comput. Stud.1071–5819, 45(2), 185–213.
Shaffer, P. S., and McDermott, L. C. (2005). “A research-based approach to improving student understanding of the vector nature of kinematical concepts.” Am. J. Phys., 73(10), 921–931.
Sobek, D. K. (2001). “Understanding the importance of intermediate representations in engineering problem-solving.” Proc., American Society for Engineering Education Annual Conf., ASEE01, American Society for Engineering Education, Washington, DC.
Trowbridge, D., and McDermott, L. (1981). “Investigation of student understanding of the concept of acceleration in one dimension.” Am. J. Phys., 49(3), 242–253.
Tversky, B., Morrison, B., and Betrancourt, M. (2002). “Animation: Can it facilitate?” Int. J. Hum. Comput. Stud., 57(4), 247–262.
Van Hattum-Janssen, N., and Lourenco, J. M. (2008). “Peer and self-assessment for first-year students as a tool to improve learning.” J. Prof. Issues Eng. Educ. Pract., 134(4), 346–352.
VisSim [Computer software]. (2011). Westford, MA, Visual Solutions (VSI).
Winne, P. H., and Jamieson-Noel, D. L. (2002). “Exploring students’ calibration of self reports about study tactics and achievement.” Contemp. Educ. Psychol., 27(4), 551–572.
Yin, R. K. (2003). Case study research: Design and methods, 3rd Ed., Sage, Thousand Oaks, CA.
Zhu, S., Xie, F., and Levinson, D. (2011). “Enhancing transportation education through online simulation using an agent-based demand and assignment model.” J. Prof. Issues Eng. Educ. Pract., 137(1), 38–45.
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© 2013 American Society of Civil Engineers.
History
Received: Jun 15, 2012
Accepted: Jan 2, 2013
Published online: Jan 4, 2013
Discussion open until: Jun 4, 2013
Published in print: Jul 1, 2013
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