Beyond engagement: an EEG-based methodology for assessing user’s confusion in an educational game

Abstract

Confusion is an emotion, which may occur when the learner is confronting inconsistence between new knowledge and existing cognitive structure, or reasoning for solving the puzzle and problem. Although confusion is not pleasant, it is necessary for the learner to engage in understanding and deep learning. Consequently, confusion assessment has attracted increased interest in e-learning. However, current studies have targeted no further than engagement detection and measurement, while there is lack of studies in investigating cognitive and emotional aspects beyond engagement in the context of game-based learning. To quantify confused states in logic reasoning in game-based learning, we propose an EEG-based methodology for assessing the user’s confusion using the OpenBCI device with 8 channels. In the complicated context of game play, it is difficult, and sometimes impossible, to collect the ground truth of the data in real tasks. To solve this issue, this work leverages cross-task and cross-subject methods to build a classifier, that is, training on the data of one standardized cognitive test paradigm (Raven’s test) and testing on the data of real tasks in game play (Sokoban Game). It provides a new possibility to create a classifier based on a small dataset. We also employ the end-to-end algorithm of deep learning in machine learning. Results showed the feasibility of this proposal in the task variation of the classifier, with an accuracy of 91.04%. The proposed EEG-based methodology is suitable to analyze learners’ confusion on the long game-play duration and has a good adaption in real tasks.