Playing profiles of students in a mathematics game as revealed by eye gaze and log data

Abstract

Digital educational games are increasingly being used in instruction. In the technological era, they complement well traditional teaching methods and give educators valuable information about their students’ progress. Other technological devices (such as eye trackers) can be used to further complement the data regarding learning. This study investigates the ways students play a chosen digital mathematics game (the Number Navigation Game), based on game log and eye gaze data. Participants played the game on a computer with an eye tracker that logged where they were looking on the screen during gameplay. The eye gaze data was complemented with game log data (all the moves made in the game, and performance in the game) and used to compute measures of how broadly a player searches for a solution in the game, and how that relates with the performed moves. The k-Means clustering analysis indicates that there are two main player profiles: navigators, with a directed search of a solution, and explorers, with a broad search. For the available data, there was no significant correlation between the playing profile and game performance. Eye gaze data was found to give valuable information, complementary to the game log file. It can be used to predict performance in the game, but over-predicts good performance. Collecting rich data from multiple, complementary data sources in games or digital learning environments allows for detailed analyses and insights into the learning processes of players. This study only taps into the rich opportunities that eye gaze analysis is offering. More studies with large sets of participants are needed to further explore the benefits of using such technological tools.