Increased emotional engagement in game-based learning - A machine learning approach on facial emotion detection data

Abstract

Abstract It is often argued that game-based learning is particularly effective because of the emotionally engaging nature of games. We employed both automatic facial emotion detection as well as subjective ratings to evaluate emotional engagement of adult participants completing either a game-based numerical task or a non-game-based equivalent. Using a machine learning approach on facial emotion detection data we were able to predict whether individual participants were engaged in the game-based or non-game-based task with classification accuracy significantly above chance level. Moreover, facial emotion detection as well as subjective ratings consistently indicated increased positive as well as negative emotions during game-based learning. These results substantiate that the emotionally engaging nature of games facilitates learning.