Personalized Learning Plans for Prerequisite Materials in a Senior-level Traffic Engineering Course

Vikash V. Gayah; Sarah E Zappe; Stephanie Cutler

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Conference: 2020 ASEE Virtual Annual Conference Content Access
Location: Virtual On line
Publication Date: June 22, 2020
Start Date: June 22, 2020
End Date: June 26, 2021
Conference Session: Making it Sticky: Ways to Reinforce Prerequisite Knowledge
Tagged Division: Civil Engineering
Page Count: 14
DOI: 10.18260/1-2--35051
Permanent URL: https://peer.asee.org/35051
Download Count: 458

Paper Authors

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Vikash V. Gayah Pennsylvania State University

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Dr. Vikash V. Gayah is an associate professor in the Department of Civil and Environmental Engineering at The Pennsylvania State University (joined 2012). He received his B.S. and M.S. degrees from the University of Central Florida (2005 and 2006, respectively) and his Ph.D. degree from the University of California, Berkeley (2012). Dr. Gayah’s research focuses on urban mobility, traffic operations, traffic flow theory, traffic safety and public transportation. His research approach includes a combination of analytical models, micro-simulations and empirical analysis of transportation data. He has authored over 50 peer-reviewed journal articles, over 50 refereed conference proceedings, and numerous research reports to sponsors. He has worked on research contracts valued at more than $5 million, sponsored by the Pennsylvania, Washington State, Montana and South Dakota Departments of Transportation, US Department of Transportation (via the Mineta National Transit Research Consortium and the Mid-Atlantic Universities Transportation Center), Federal Highway Administration, National Cooperative Highway Research Program and National Science Foundation.

Dr. Gayah currently serves as an editorial advisory board member of Transportation Research Part C: Emerging Technologies, an editorial board editor of Transportation Research Part B: Methodological, an associate editor for the IEEE Intelligent Transportation Systems Magazine (an international peer-reviewed journal), a handling editor for the Transportation Research Record and is a member of the Transportation Research Board’s Committee on Traffic Flow Theory and Characteristics (AHB 45), where he serves as a paper review coordinator. He has been recognized with multiple awards for his research and teaching activities, including the Dwight D. Eisenhower Transportation Fellowship, Gordon F. Newell Award for Excellence in Transportation Science, University of California Transportation Center Student of the Year Award, New Faculty Award by the Council of University Transportation Centers, the Cunard, Fred Burggraf and D. Grant Mickle outstanding paper awards by the Transportation Research Board, Harry West Teaching Award by the Department of Civil and Environmental Engineering at Penn State, Outstanding Teaching Award by the Penn State Engineering Alumni Society, and Faculty Early Career Development (CAREER) Award by the National Science Foundation.

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Sarah E Zappe Pennsylvania State University

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Dr. Sarah Zappe is Research Professor and Director of Assessment and Instructional Support in the Leonhard Center for the Enhancement of Engineering Education at Penn State. She holds a doctoral degree in educational psychology emphasizing applied measurement and testing. In her position, Sarah is responsible for developing instructional support programs for faculty, providing evaluation support for educational proposals and projects, and working with faculty to publish educational research. Her research interests primarily involve creativity, innovation, and entrepreneurship education.

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Stephanie Cutler Pennsylvania State University

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Dr. Stephanie Cutler has degrees in Mechanical Engineering, Industrial and Systems Engineering, and a PhD in Engineering Education from Virginia Tech. She is an Associate Research Professor and the Director of Assessment and Instructional Support in the Leonhard Center at Penn State.

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Abstract

The purpose of this project was to improve a senior-level elective course on Traffic Operations at the Pennsylvania State University. Previous experiences with the course suggested that students generally have very poor recollection of various topics in statistics, which are required to appropriately analyze field data. Because of these deficiencies in prerequisite material, a significant portion of the course (about 4-5 of 30 lecture sessions) was spent reviewing background material. Doing so reduced the time available to spend on new material related to the course topic, broke momentum during the semester, and tended to be disengaging for students who did have sufficient background knowledge.

Through a project with the Leonhard Center for Enhancement of Engineering Education within the College of Engineering, data-driven methods were developed to assess student competencies on this background material and provide personalized instructional modules to address any deficiencies. A pre-test on the prerequisite statistics material was created based on six unique topics that were deemed important to the course material. Students were asked to take the pre-test during the first week of the course and their responses were used to provide them with targeted outside-of-the-classroom learning modules based on the specific material that they struggled with. Specifically, each question was associated with a unique module and a student only had to complete a module if they missed the associated question. These modules were comprised of reading assignments, YouTube videos and additional practice problems. Students were asked to turn in the additional problems for class credit. However, the problems themselves were not graded but were used to ensure students completed the necessary activities. During the fourth week, students were then asked to take a post-test to assess their knowledge of the prerequisite material, as well as their improvement on the topics that were most challenging.

The results suggested that the targeted modules helped improve performance on the prerequisite material by about 40% on average and that student confidence in their abilities increased by about 45%. Moreover, moving the prerequisite material outside of the classroom helped free up additional time to cover topics related to the course material that could not be accommodated before, including signal coordination and actuated signal control. In general, this strategy appears to be effective and can be applied to any course to help address issues with prerequisite knowledge. This study is limited due to the relatively low sample size (total of 81 students), lack of a comparison group to compare traditional methods of teaching prerequisite material, and potential confounding factors that might have influenced results. However, the large improvements in performance and short-time frame that this was implemented in may help limit some of these impacts.

Citation
Format

Gayah, V. V., & Zappe, S. E., & Cutler, S. (2020, June), Personalized Learning Plans for Prerequisite Materials in a Senior-level Traffic Engineering Course Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35051

TY - CPAPER
AB - The purpose of this project was to improve a senior-level elective course on Traffic Operations at the Pennsylvania State University. Previous experiences with the course suggested that students generally have very poor recollection of various topics in statistics, which are required to appropriately analyze field data. Because of these deficiencies in prerequisite material, a significant portion of the course (about 4-5 of 30 lecture sessions) was spent reviewing background material. Doing so reduced the time available to spend on new material related to the course topic, broke momentum during the semester, and tended to be disengaging for students who did have sufficient background knowledge.

Through a project with the Leonhard Center for Enhancement of Engineering Education within the College of Engineering, data-driven methods were developed to assess student competencies on this background material and provide personalized instructional modules to address any deficiencies. A pre-test on the prerequisite statistics material was created based on six unique topics that were deemed important to the course material. Students were asked to take the pre-test during the first week of the course and their responses were used to provide them with targeted outside-of-the-classroom learning modules based on the specific material that they struggled with. Specifically, each question was associated with a unique module and a student only had to complete a module if they missed the associated question. These modules were comprised of reading assignments, YouTube videos and additional practice problems. Students were asked to turn in the additional problems for class credit. However, the problems themselves were not graded but were used to ensure students completed the necessary activities. During the fourth week, students were then asked to take a post-test to assess their knowledge of the prerequisite material, as well as their improvement on the topics that were most challenging.

The results suggested that the targeted modules helped improve performance on the prerequisite material by about 40% on average and that student confidence in their abilities increased by about 45%. Moreover, moving the prerequisite material outside of the classroom helped free up additional time to cover topics related to the course material that could not be accommodated before, including signal coordination and actuated signal control. In general, this strategy appears to be effective and can be applied to any course to help address issues with prerequisite knowledge. This study is limited due to the relatively low sample size (total of 81 students), lack of a comparison group to compare traditional methods of teaching prerequisite material, and potential confounding factors that might have influenced results. However, the large improvements in performance and short-time frame that this was implemented in may help limit some of these impacts.

AU - Vikash V. Gayah
AU - Sarah E Zappe
AU - Stephanie Cutler
CY - Virtual On line
DA - 2020/06/22
PB - ASEE Conferences
TI - Personalized Learning Plans for Prerequisite Materials in a Senior-level Traffic Engineering Course
UR - https://peer.asee.org/35051
DO - 10.18260/1-2--35051
ER -