&Bullet; physics 14, 59
Two research teams used eye-tracking techniques to learn how students approach complex physical problems.
Using eye tracking technology, two research teams examined the process by which students solve complex physical problems and found that both the nature of the problem and the student’s competence influence the way the student approaches the problem [1, 2] . The technology is not new to physics pedagogy (PER) research, but it has not been used on physics problems that involved understanding multiple concepts or interpreting both diagrams and equations. The results could influence the teaching of problem-solving skills.
Eye movements provide a convenient way of tracking a person’s attention. Technologies developed for eye tracking typically involve recording videos of a person’s eyes and then analyzing the footage to determine where they are looking at each moment. PER researchers have been using these techniques for about a decade to explore student approaches to problem solving. Most of this work, however, has dealt with single-concept problems rather than the multi-concept problems that are an integral part of physics courses. Two research teams have now applied eye tracking to more complicated problems.
In one study, Bashirah Ibrahim of Bahrain University and Lin Ding of Ohio State University tracked the eye movements of 22 students while they worked on four questions. Two of these questions required the analysis of a number of chronological events, e.g. B. the calculation of the energy and speed of a roller coaster when descending and ascending. The other two questions concerned simultaneous events, e.g. B. the determination of the translational and rotational momentum of a rod at the moment in which it is hit by a ball. All problems involved applying knowledge of at least two physical concepts and required that the student study both the wording of the question and a diagram. Students’ eyes were followed twice as they solved each problem: first during a “thinking phase” when they silently tackled the problem in their heads, and then during a “talking phase” when they spoke out loud about their methodology.
Ibrahim and Ding found that when solving the simultaneous problems, the students’ eyes moved back and forth between the text and the diagram more often than when solving the sequential ones. With sequential problems, students focused more on the diagrams than with simultaneous problems. The team found no connection between the eye movement pattern and whether the student answered the question correctly.
Based on the eye-tracking data and student comments during the interview phase of the study, the researchers hypothesized that students often misunderstood the concurrent problems as single events without grasping their tiered nature. The frequent swaps between text and diagram observed in these problems could be due to students needing help interpreting the diagrams, the researchers suggest. In the case of sequential problems, there may be times when students spent a long time focusing on the diagrams as they worked through the different phases that they correctly perceived.
The bottom line is that different types of problems lead to different approaches by students and therefore require different teaching techniques, says Ibrahim. “We cannot throw every problem into the same basket indiscriminately.”
In a second study, Chao-Jung Wu and Chia-Yu Liu from National Taiwan Normal University looked at the effects of students’ skills. They took about 200 students a graph reading test and invited those who scored in either the top or bottom third to take part in the eye tracking study, which 96 of them participated in. Students were given four questions that required them to interpret information and data in four different formats: a written question, a data table, a chart, and an equation. Students were then recorded and spoken their answers aloud while their eyes were followed.
Wu and Liu found a clear correlation between a student’s eye movements and their ability to read graphics. The eyes of those in the high proficiency group often moved between the four elements of the question, suggesting that they can understand and integrate the various representations of the information. In contrast, those in the group with low competencies mainly stuck to the text and the table.
Ibrahim and Ding’s observation that students have the same eye movement patterns – regardless of whether they can solve the problem – is “surprising” as it contradicts previous findings for single concept problems, says PER researcher Tianlong Zu of Lawrence University, Wisconsin. The PER researcher Pascal Klein from the University of Göttingen agrees. Klein is less surprised by the study of Wu and Liu, as prior research has linked expertise and visual attention to simpler problems. However, he notes that the results will help pinpoint exactly where students are having trouble. It is clear that students with lower physics proficiency will need better instruction on how to synthesize data from multiple sources at the same time. That ability is becoming increasingly important in today’s data-driven world, he says.
– Katherine Wright
Katherine Wright is the assistant editor of physics.
- B. Ibrahim and L. Ding, “Sequential and Simultaneous Synthesis of Synthesis Problems: A Comparison of Students’ Glance Transitions” Phys. Rev. Phys. Educ. Res.17th010126 (2021).
- C. Wu and C. Liu, “Eye Movement Study of Scientific Reasoning of Students with High and Low Prior Knowledge with Multiple Representations”. Phys. Rev. Phys. Educ. Res.17th010125 (2021).