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Using a Novel Gravity Model for Ranking and Assessment of Educational Games

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016





Conference Session

NSF Grantees Poster Session II

Tagged Topic

NSF Grantees Poster Session

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Paper Authors


Qichao Wang Virginia Tech

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Qichao Wang is a PhD student in the Transportation Infrastructure and Systems Engineering program at Virginia Tech. He holds a Bachelor of Engineering in Traffic Engineering from Nanjing Tech University, P.R.China (2014). His research interests include Game-aided Pedagogy, 3D visualization, traffic control, multi-agent system, machine learning and optimization.

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Montasir Abbas P.E. Virginia Tech

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Dr. Montasir Abbas is an Associate Professor in the Transportation Infrastructure and Systems Engineering at Virginia Tech. He holds a Bachelor of Science in Civil Engineering from University of Khartoum, Sudan (1993), a Master of Science in Civil Engineering from University of Nebraska-Lincoln (1997), and a Doctor of Philosophy in Civil Engineering from Purdue University (2001).

Dr. Abbas has wide experience as a practicing transportation engineer and a researcher. He was an Assistant Research Engineer and the Corridor Management Team Leader at Texas Transportation Institute (TTI), where he has worked for four years before joining Virginia Tech. Dr. Abbas conducted sponsored research of more than $720,000 as a principal investigator and more than $750,000 as a key researcher at TTI. After joining Virginia Tech, he has conducted over $2,400,000 worth of funded research, with a credit share of more than $1,750,000.

Dr. Abbas is an award recipient of $600,000 of the Federal Highway Administration Exploratory and Advanced Research (FHWA EAR). The objective of the FHWA EAR is to “research and develop projects that could lead to transformational changes and truly revolutionary advances in highway engineering and intermodal surface transportation in the United States.” The award funded multidisciplinary research that utilizes traffic simulation and advanced artificial intelligence techniques. He has also conducted research for the National Cooperative Highway Research Program on developing “Traffic Control Strategies for Oversaturated conditions” and for the Virginia Transportation Research Council on “evaluation and recommendations for next generation control in Northern Virginia.”

Dr. Abbas developed Purdue Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals (PRO-TRACTS) during his Ph.D. studies at Purdue University, bridging the gap between adaptive control systems and closed-loop systems. He has since developed and implemented several algorithms and systems in his areas of interest, including the Platoon Identification and Accommodation system (PIA), the Pattern Identification Logic for Offset Tuning (PILOT 05), the Supervisory Control Intelligent Adaptive Module (SCIAM), the Cabinet-in-the-loop (CabITL) simulation platform, the Intelligent Multi Objective Control Algorithms (I-MOCA), the Traffic Responsive Iterative Urban-Control Model for Pattern-matching and Hypercube Optimal Parameters Setup (TRIUMPH OPS), the Multi Attribute Decision-making Optimizer for Next-generation Network-upgrade and Assessment (MADONNA), and the Safety and Mobility Agent-based Reinforcement-learning Traffic Simulation Add-on Module (SMART SAM). He was also one of the key developers of the dilemma zone protection Detection Control System (D-CS) that was selected as one of the seven top research innovations and findings in the state of Texas for the year 2002.

Dr. Abbas served as the chair of the Institute of Transportation Engineers (ITE) traffic engineering council committee on “survey of the state of the practice on traffic responsive plan selection control.” He is also a member of the Transportation Research Board (TRB) Traffic Signal Systems committee, Artificial Intelligence and Advanced Computing Applications committee, and the joint subcommittee on Intersection. In addition, he is currently a chair on a task group on Agent-based modeling and simulation as part of the TRB SimSub committee. He also serves as a CEE faculty senator at Virginia Tech.

Dr. Abbas is a recipient of the Oak Ridge National Lab Associated Universities (ORAU) Ralf E. Powe Junior Faculty Enhancement Award and the G. V. Loganathan Faculty Achievement Award for Excellence in Civil Engineering Education. He is also a recipient of the TTI/Trinity New Researcher Award for his significant contributions to the field of Intelligent Transportation Systems and Traffic Operations.

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Teaching introductory transportation engineering subjects can be very challenging. These courses usually include diverse topics and can involve students from different background and interest levels. Keeping students engaged and focused requires changes in traditional delivery methods. It also requires the design of exercises that are especially targeted to address certain concepts. Game-aided pedagogy has been proposed to stimulate students’ interest and increase the efficiency of their learning. Our research team developed multiple games that were designed to target different concepts in the transportation fields. These games deliver appropriate amount of information density and accessibility, and utilize multimedia and hypermedia contents. We have developed a novel gravity model to relate students learning to information density, ability of students to absorb knowledge, and difficulty of delivery, and have previously demonstrated the model with one of the games.

In this paper, we expand and illustrate the use of the educational gravity model to assess and compare different games used in game-aided teaching. A description of two different games is included in this paper. Each game uses refined 3D vivid scenes to attract and stimulate students. Gameplay data collected include students’ responses in each game level. Both games use client-server architecture to interact with students and store their gameplay data to assess the students’ learning outcomes. We capture the effectiveness of each game by calibrating the gravity factor in each model. Each game has a naturally different gravity factor that could be associated with the game’s appeal and capability to transfer knowledge. We attempt to shed more light into this concept and the potential for its use in ranking and evaluating newly developed games in terms of their pedagogical value.

Wang, Q., & Abbas, M. (2016, June), Using a Novel Gravity Model for Ranking and Assessment of Educational Games Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27123

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