Eye-Track Modeling of Problem-Solving in Virtual Manufacturing Environments

Abstract

Problem-solving focuses on defining and analyzing problems, then finding viable solutions through an iterative process that requires brainstorming and understanding of what is known and what is unknown in the problem space. With rapid changes of economic landscape in the United States, new types of jobs emerge when new industries are created. Employers report that problem-solving is the most important skill they are looking for in job applicants. However, there are major concerns about the lack of problem-solving skills in engineering students. This lack of problem-solving skills calls for an approach to measure and enhance these skills. In this research, we propose to understand and improve problem-solving skills in engineering education by integrating eye-tracking sensing with virtual reality (VR) manufacturing. First, we simulate a manufacturing system in a VR game environment that we call a VR learning factory. The VR learning factory is built in the Unity game engine with the HTC Vive VR system for navigation and motion tracking. The headset is custom-fitted with Tobii eye-tracking technology, allowing the system to identify the coordinates and objects that a user is looking at, at any given time during the simulation. In the environment, engineering students can see through the headset a virtual manufacturing environment composed of a series of workstations and are able to interact with workpieces in the virtual environment. For example, a student can pick up virtual plastic bricks and assemble them together using the wireless controller in hand. Second, engineering students are asked to design and assemble car toys that satisfy predefined customer requirements while minimizing the total cost of production. Third, data-driven models are developed to analyze eye-movement patterns of engineering students. For instance, problem-solving skills are measured by the extent to which the eye-movement patterns of engineering students are similar to the pattern of a subject matter expert (SME), an ideal person who sets the expert criterion for the car toy assembly process. Benchmark experiments are conducted with a comprehensive measure of performance metrics such as cycle time, the number of station switches, weight, price, and quality of car toys. Experimental results show that eye-tracking modeling is efficient and effective to measure problem-solving skills of engineering students. The proposed VR learning factory was integrated into undergraduate manufacturing courses to enhance student learning and problem-solving skills.