Modeling Learners to Early Predict Their Performance in Educational Computer Games

Abstract

Data mining approaches have proven to be successful in improving learners´interaction with educational computer games. Despite the potential of predictive modelling in providing timely adaptive learning and gameplay experience, there is a lack of research on the early prediction of learners' performance in educational games. In this research, we propose an early predictive modelling approach, called GameEPM, to estimate learners' final scores in an educational game for promoting computational thinking. Specifically, the GameEPM approach models the sequence of learners' actions and then uses a limited sequence of the actions to predict the final score of the game for each learner. The findings from our initial trials show that our approach can accurately and robustly estimate the learners' performance at the early stages of the game. Using less than 50% of learners' action sequences, the cross-validated deep learning model achieves a squared correlation higher than 0.8 with a relative error of less than 8%, outperforming a range of regression models like linear regression, random forest, neural networks, and support vector machines. An additional experiment showed that the validated deep learning model can also achieve high performance while tested on an independent game dataset, showing its applicability and robustness in real-world cases. Comparing the results with traditional machine learning methods revealed that, in the validation and application phases, up to 0.30 and 0.35 R 2 gain is achieved in favor of the deep learning model, respectively. Finally, we found that while the lengths of action sequences influence the predictive power of the traditional machine learning methods, this effect is not substantial in the deep learning model.

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“…For instance, a solution that solely uses arrows to navigate the game, without the NPC catching the mouse, can still collect many Small_cheese features and accordingly achieve high scores. In educational games, a high score can be obtained through either a random strategy or an appropriate strategy, such as parallel thinking [82].…”

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“…al. in Kuripan, Kertoharjo Village, showed that 17 the Tic Tac Toe game (Hooshyar et al, 2023). Based on research by Farida & Rini, 2013 the game Tic Tac Toe in the field of mathematics can increase learning motivation in cycle I by 83.88% and by 85.85% for cycle II.…”