Identifying key visual-cognitive processes in students’ interpretation of graph representations using eye-tracking data and math/machine learning based data analysis

Abstract

We present a mathematical and computational analysis, partially based on machine learning techniques, of the visual scan-paths obtained during a graph interpretation task which allows us to identify when the problem solver succeeds in solving the problem with a fair degree of accuracy, and helps to understand the visual-cognitive processes at work during the problem solving task.