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The Effectiveness of an Adaptive Serious Game for Digital Logic Design

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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

26.1523.1 - 26.1523.9



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Paper Authors


Ying Tang Rowan University

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Ying Tang received the B.S. and M.S. degrees from the Northeastern University, P. R. China, in 1996 and 1998, respectively, and Ph.D degree from New Jersey Institute of Technology, Newark, NJ, in 2001. She is currently a Professor of Electrical and Computer Engineering (ECE) at Rowan University, Glassboro, NJ. Her research interests include virtual reality and augmented reality, artificial intelligence, and modeling and scheduling of computer-integrated systems. Dr. Tang is very active in adapting and developing pedagogical methods and materials to enhance engineering education. Her most recent educational research includes the collaboration with Tennessee State University and local high schools to infuse cyber-infrastructure learning experience into the pre-engineering and technology-based classrooms, the collaboration with community colleges to develop interactive games in empowering students with engineering literacy and problem-solving, the integration of system-on-chip concepts across two year Engineering Science and four year ECE curricula, and the implementation of an educational innovation that demonstrates science and engineering principles using an aquarium. Her work has resulted in over 100 journal and conference papers and book chapters.

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Kauser Jahan Rowan University

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Kauser Jahan, is a Professor of Civil and Environmental Engineering at Rowan University. She received her B.S.C.E. from the Bangladesh University of Engineering and Technology, an MSCE from the University of Arkansas, Fayetteville and a Ph.D. from the University of Minnesota, Minneapolis. Her passion as an educator and mentor has been recognized by many professional organizations over the years. She is the recipient of the Gloucester County Women of Achievement Award, Lindback Foundation Teaching Award, the NJ ASCE Educator of the Year award, the Gary J. Hunter Excellence in Mentoring Award, the ASEE Environmental Engineering Division Meritorious Service Award, the ASEE Women in Engineering Division Sharon A. Keillor Award and the WEPAN Women in Engineering Initiative Award. She has been instrumental in establishing the Attracting Women into Engineering, the Engineers on Wheels and Engineering Clinics for Teachers programs at Rowan University. She has served as the Institutional Representative and Advisory Board Chair for the Women's Professional Network at Rowan University for six years and currently is an advisory board member of the New Jersey Chapter of the American Council on Education (ACE) Office of Women in Higher Education (OWHE).

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Talbot Bielefeldt Clearwater Program Evaluation

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Educational program evaluator since 1995, working with a variety of federal, state, local, and corporate education initiatives. Current projects include evaluations of school/community and school/university grants focused on STEM education.

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The Effectiveness of An Adaptive Serious GameABSTRACTMost students benefit more deeply from guided learning than discovery learning. Even so, not all students are alike.Some of them are highly self-motivated, and they benefit most from the approach that gives them the freedom andopportunities to discovering a realm of knowledge on their own. Others prefer coaching and a more structuredapproach. For students who lack motivation and prior knowledge in particular, practice alone in problem solving withpoor strategies, unproductive ideas and very little guidance will just get them nowhere. Instead, they will end upfrustrated and give up on the task. Nonetheless, instructors are often faced with limited resources to provide “just-in-time” instructional support that is tailored to individual and small group needs.This need of “just-in-time” support becomes even prominent in Gridlock, a domain-specific game that engagesstudents to apply their digital logic design skill for a solutions that fixes the malfunctioning traffic light controller fora 4-way intersection. The Gridlock was piloted in fall 2011 and fall 2012. In fall 2011, A pre- and post- conceptualsurvey was given to 18 students in the experimental group who use the game system and to 21 students in the controlgroup who use the traditional labs to rate their proficiency on 19 core concepts of Digital I (scores ranged from zero tofive (unfamiliar to proficient)). An independent sample t-test revealed no difference between experimental and controlgroups at pre-test (p=0.774) but a significant difference between these groups at post-test (p=0.028). The resultsdemonstrated that students in the game learning had greater proficiency with the course concepts. However, as far asthe learning support is concerned, students voiced their different views of what the game system should provide.Some felt that the current roadmap was just right to provide necessary assistance in identifying domain knowledge. Ina survey given to 20 students in the experimental group about their game behaviors, 10 of them chose road map as themost helpful tool in navigating the game labs, since road map, in their words, “gives an idea of what information touse when I am lost”, “hints necessary concepts”, and “provides formulas to carry out whatever tasks I must”. Othersconsidered the expert guidance is too shallow, and should be more detailed with additional question prompts closelytied to each milestone. Without a direct purpose for and required responses from students to the interventions,particularly KWS, students were not focused on the learning modeled in such support as deeply as we had hoped. Outof the 20 students, 15 chose KWS as the least useful tool since “I don’t usually track my progress in such a way as theone (KWS) presented in the games”, “I like to write the problems down in my notebook”, and “typing about circuits(in KWS) is too difficult”. The similar responses were received from the fall 2012 implementation. Those responsesclearly ratify that every pupil has their own needs and their own particular way of learning. If there were a way inwhich our game system can understand such differences and provide support accordingly, the resulting system couldbe more efficient and effective in promoting successful learning.In the past year, we have been exploring solution feasibility to this critically important challenge, and improveGridlock with an adaptive learning engine that assess what really happens when a student’s capacity is sabotaged inproblem solving and to provide the help that is tailored to his/her needs. However, the data that is necessary for themapping of the student’s knowledge level to differentiated coaching is often uncertain and only available throughobservation of the learning process itself. Therefore, any attempt towards developing an accurate mapping solution,which is part of our work focus, must involve some algorithmic components that will allow the decision makingprocess to (1) accumulate its past experience to a pertinently defined set of data structures, and at the same time, (2)exploit the “knowledge” captured in the data set towards improving the overall system performance. The idea isimplemented using a k-nearest neighbor (KNN)-based close-loop control as depicted in Fig. 1. The proposed systemincorporates classification and feedback into the existing Gridlock game. Each player is evaluated at individual gamestages on their understanding of the material. The results of the evaluation as well as other factors that representhis/her behavior and understanding are classified to determine if the player masters the required material beforeproceeding to the next section of the game. Such classification is cascaded from one game stage to another,reinforcing the system understanding of student domain knowledge. If a student is found lacking in some area of theinformation, he/she is presented immediate feedback and detailed help as for how to acquire the knowledge. KNN-based VR Game System Instruction Expert Module Database Expert Model Pedagogical Knowledge Model Past “knowledge” Database Student Model Observed evidence Pedagogy Module Other action measure KNN Classifier Prompt-based Differentiated instructions Gridlock KWS (i.e., a set of progressive prompts or cues) TA2S Roadmap Student Actions General guideline Student Module Existing VR Game System Fig . 1: The system architecture of the KNN-based adaptive serious game systemTogether with the implementation, a thorough evaluation plan is conducted as well to help investigatorsanswer three important questions as listed below. This paper reports the findings of this assessment. To what extent is the adaptive features in the VR games useful to student learning To what extent does the VR games with adaptive features play in fostering student interests in engineering problem-solving To what extent is the student learning improved by the VR game experience in general

Tang, Y., & Jahan, K., & Bielefeldt, T. (2015, June), The Effectiveness of an Adaptive Serious Game for Digital Logic Design Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24861

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