Validation of a Novel Transition Frequency Eigenanalysis Approach in the Analysis of Eye-Tracking Data for Understanding Viewing Patterns of Multiple Representations
Dr. Justin Shorb, Chemistry
Eye-tracking has been increasingly used in educational research in order to gain insight into how we interpret information. Current analysis methods are limited in that they solely interrogate fixations or transitions, with the latter limited to only two areas on a page. In order to understand more complex gaze patterns, it is necessary to be able to quantify dominant viewing behaviors that couple three or more fixation areas. A novel approach to analyzing eye-tracking data will be discussed along with sample data that highlight differences in viewing patterns between different readers. This analysis is done using eigenvalue/eigenvector decomposition, which is the base algorithm found in Factor Analysis and Principal Component Analysis. In our group, we are interested in using this analysis method to understand an individual’s comprehension of the complexity in chemistry representations. Making use of the triplet relationship, we would be able to replicate the expert/novice study conducted by Kozma and Russell in 1997 by observing the variations in attention which a person gives several representations to differentiate between experts and novices.
A recommended citation will become available once a downloadable file has been added to this entry.