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WORK IN PROGRESS: A Computer-Aided Design Intelligent Tutoring System Teaching Strategic Flexibility

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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

Computers in Education Division Poster Session

Tagged Division

Computers in Education

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Paper Authors


Yang Hu Washington State University

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Yang Hu obtained her Bachelor degree in major of applied chemistry in 2005. Then she continued a graduate study in polymer physics and chemistry from 2005 to 2008. After working for a year as a recycled material product manager, she came to the U.S. began the graduate study at Washington State University. She got her Master Degree in Mechanical Engineering in 2013. She currently is a Ph.D. candidate in Computer Science. She is interested in applying Reinforcement learning method to build intelligent tutorial systems that are adaptive to multiple solutions.

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Matthew E. Taylor Washington State University

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Matthew E. Taylor graduated magna cum laude with a double major in computer science and physics from Amherst College in 2001. After working for two years as a software developer, he began his Ph.D. work at the University of Texas at Austin with an MCD fellowship from the College of Natural Sciences. He received his doctorate from the Department of Computer Sciences in the summer of 2008, supervised by Peter Stone. Matt then completed a two year postdoctoral research position at the University of Southern California with Milind Tambe and spent 2.5 years as an assistant professor at Lafayette College in the computer science department. He is currently an assistant professor at Washington State University in the School of Electrical Engineering and Computer Science and is a recipient of the National Science Foundation CAREER award. Current research interests include intelligent agents, multi-agent systems, reinforcement learning, transfer learning, and robotics.

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Taking a Computer-Aided Design (CAD) class is a prerequisite for Mechanical Engineering freshmen at many universities, including ours. The traditional way to learn CAD software is to follow examples and exercises in a textbook. However, using written instruction is not always effective because textbooks usually support single strategy to construct a model. Missing even one detail may cause the student to become stuck, potentially leading to frustration. To make the learning process easier and more interesting, we designed and implemented a tutorial for an open source CAD program, FreeCAD, to teach students how to use Boolean operations to construct complex objects from multiple simple shapes. Instead of teaching a single method to construct a model, the program first automatically learns all possible ways to construct a model and then can teach the student multiple ways to make the 3D model. Learning multiple potential solutions has been shown to encourage students to develop the tools they need to solve new problems. This study compares textbook learning with learning from two variants of our intelligent tutoring system. The textbook approach is considered the baseline. In the first tutorial variant, subjects were given minimal guidance and were asked to construct a model in multiple ways. Subjects in the second tutorial group were given two guided solutions to constructing a model and then asked to demonstrate a third solution when constructing the same model. Rather than directly providing instructions, participants in the second tutorial group were expected to independently explore and were only provided feedback when the program determined he/she had deviated too far from a potential solution. The three groups are compared by measuring the time needed to 1) successfully construct the same model in a testing phase, 2) use multiple methods to construct the same model in a testing phase, and 3) construct a novel model.

Hu, Y., & Taylor, M. E. (2016, June), WORK IN PROGRESS: A Computer-Aided Design Intelligent Tutoring System Teaching Strategic Flexibility Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27208

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