GameDKT: Deep knowledge tracing in educational games

Despite the multiple deep knowledge tracing (DKT) methods developed for intelligent tutoring systems and online learning environments, there exists only a few applications of such methods in educational computer games. One key challenge is that a player may deploy several interweaved and overlapped skills during gameplay, making the assessment task nontrivial. In this research, we present a generalizable DKT approach called GameDKT that integrates state-of-the-art machine learning with domain knowledge to model the learners’ knowledge state during gameplay, in an attempt to monitor and trace their proficiency level for the different skills required for educational games. Our findings reveal that GameDKT approach could successfully predict the performance of players in the coming game task using the cross-validated CNN model with accuracy and AUC of roughly 85% and 0.913, respectively, thus outperforming the MLP baseline model by up to 14%. When the performance of players is forecasted for up to four game tasks in advance, results show that the CNN model can achieve more than 70% accuracy. Interestingly, this model seems to be better and faster at identifying local patterns and it could achieve a higher performance compared to RNN and LSTM in both one-step and multi-step prediction of learners’ performance in game tasks.