Understanding Students Process for Solving Engineering Problems Using Eye Gaze Data

Youyi Bi

Tahira N. Reid


It is well known that engineering is considered one of the more demanding fields of study to embark on. In mechanical engineering, courses such as thermodynamics, statics, mechanics of materials and others are perceived as challenging by students. Several factors impact students’ ability to solve problems presented in these courses, including the ability to visualize the abstract concepts presented to them. Exams and homework assignments are among the standard tools used to assess students’ performance and comprehension of course material. Student ability is determined by the quality of the written answers and by how well they document the process used to solve a problem. However, they provide only limited opportunities to reveal the viewing strategies used that may give additional insight into how students initially approach the given problem. In the present study, we use a within-subject experimental design to investigate the relationship between spatial visualization abilities of students and how students solve specific problems in the area of mechanics of materials. We employ a non-invasive eye-tracker to record participants’ eye movements during each problem solving task. According to the eye-mind hypothesis, people look at what they are thinking about. Participants were asked to solve several problems in the field of mechanics of materials, and the diagram of each problem was shown on a computer display. The data collected included: participants’ fixation time, fixation counts, and scan paths of the critical areas of each diagram.

The data were correlated with students’ performance on Purdue Spatial Visualization Test and solid mechanics problems. The preliminary results show differences in the eye gaze data of high and low performance participants and provide insight into students’ problem-solving strategies and difficulties, offering instructors new facts to adopt appropriate teaching methods for different students.

This publication uses Eye Tracking which is fully integrated into iMotions Lab

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