Automatic detection and interpretable analysis of learners’ cognitive states based on electroencephalogram signals

Abstract

The development of higher-order thinking skills (HOTS) in learners is one of the educational objectives of the 21st century. Detecting learners’ higher and lower cognitive states based on electroencephalogram (EEG) signals is crucial for promoting the development of HOTS. In this study, we investigated the feasibility of using an EEG-based deep learning (DL) model to classify higher and lower cognitive states and employed eXplainable Artificial Intelligence (XAI) techniques to examine the neural mechanisms associated with the cognitive states. To trigger learners’ cognitive states, both high-level and low-level learning activities were developed based on a revised Bloom's taxonomy and the Interactive-Constructive-Active-Passive (ICAP) framework and used with 22 subjects whose EEG signal was recorded and detected with the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model. The CNN-LSTM model yielded a remarkable recognition accuracy of around 99 %. Finally, we proposed LIME-Brain Area (LIME-BA), an improvement of Local Interpretable Model-agnostic Explanation (LIME), to identify the distinctive attributes of brain area activities for different levels of cognitive states. According to the XAI interpretable analysis, we found that the frontal and temporal areas were activated in a higher cognitive state, and the occipital and parietal regions were activated in a lower cognitive state. This study provides further evidence for educators to design cognitive-guided instructional activities to enhance learners’ development of HOTS.