Exploring Gaze Behavior to Assess Performance in Digital Game-Based Learning Systems
This addresses the need for more accurate performance assessment in digital game-based learning systems, which are used in industries like military and medical, though it is incremental as it builds on existing eye-tracking methods.
The study tackled the problem of traditional explicit interaction measures in digital game-based learning systems being unreliable due to guessing, by proposing a novel implicit eye-tracking metric that correlates strongly with in-game performance, as validated in an experiment with 25 participants.
The recent growth of sophisticated digital gaming technologies has spawned an \$8.1B industry around using these games for pedagogical purposes. Though Digital Game-Based Learning Systems have been adopted by industries ranging from military to medical applications, these systems continue to rely on traditional measures of explicit interactions to gauge player performance which can be subject to guessing and other factors unrelated to actual performance. This study presents a novel implicit eye-tracking based metric for digital game-based learning environments. The proposed metric introduces a weighted eye-tracking measure of traditional in-game scoring to consider the mental schema of a player's decision making. In order to validate the efficacy of this metric, we conducted an experiment with 25 participants playing a game designed to evaluate Chinese cultural competency and communication. This experiment showed strong correlation between the novel eye-tracking performance metric and traditional measures of in-game performance.