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A Knowledge-Delivery Gravity Model to Improve Game-Aided Pedagogy

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2015 ASEE Annual Conference & Exposition


Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015





Conference Session

NSF Grantees’ Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count


Page Numbers

26.60.1 - 26.60.14



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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 3D visualization, smartphone-based traffic information system, multi-agent system, 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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Lisa D. McNair Virginia Tech

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Lisa D. McNair is an Associate Professor of Engineering Education at Virginia Tech, where she also serves as co-Director of the VT Engineering Communication Center (VTECC). Her research interests include interdisciplinary collaboration, design education, communication studies, identity theory and reflective practice. Projects supported by the National Science Foundation include exploring disciplines as cultures, interdisciplinary pedagogy for pervasive computing design; writing across the curriculum in Statics courses; as well as a CAREER award to explore the use of e-portfolios to promote professional identity and reflective practice.

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A Knowledge-Delivery Gravity Model to Improve Game-Aided PedagogyTeaching materials have evolved from mere text to multi- and hyper-media contents,leading to substantial growth in both information density and accessibility. One of theaccompanying challenges with this evolution is the growing need to accurately quantifythe degree of students’ stimulation and engagement in this new environment. Game-aided pedagogy can stimulate students’ interest and can complement their individuallearning styles. It can also provide them with the appropriate amount of informationdensity and accessibility, utilizing multimedia and hypermedia contents.This paper introduces a knowledge-delivery gravity model to measure the level ofdelivery of information contents. The output of this model is the intensity of knowledgepassed from educators to students. Information density, ability of students to absorbknowledge, and difficulty of delivery (i.e., knowledge inaccessibility) are considered inthis model as impact factors. The knowledge delivery gravity model’s output is definedas the product of information density and the ability to absorb information divided bythe difficulty of delivery with a coefficient factor. Difficulty is defined as the reciprocalof accessibility.A multi-level web game is designed to enhance students’ understanding of certainconcepts in transportation engineering (driver behavior in dilemma zone), and is usedas a platform for testing our proposed concept. Our objective is to increase the students’engagement and decrease the difficulty of knowledge-delivery. The game can simulatetraffic operation scenarios and collect users’ gameplay data using refined 3D scenes.Vivid scenes attract students and multi-level design increases the appeal of the gameand thus can stimulate students. Gameplay data collected from users can monitorstudents’ responses and gather their understanding of the delivered knowledge. Thisgame has a “client” part and a “server” part. The client part interacts with students’operation and renders scenes, while the server part stores students’ gameplayinformation and responds with different game levels accordingly. The client part wasprogrammed with unity 3D and C# language together with HTML and Javascript. Theserver part was achieved by and MS Access database. The output of this gamecan be used to assess the students’ learning outcomes.Students’ learning outcomes and the intensity of knowledge delivered are positivelycorrelated. The result of this research can be used to quantify the knowledge-deliverygravity model’s parameters, which can in turn be used to guide the revision anddevelopment of the next generation game-aided pedagogy.

Wang, Q., & Abbas, M., & McNair, L. D. (2015, June), A Knowledge-Delivery Gravity Model to Improve Game-Aided Pedagogy Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23401

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