Automated classification of EEG signals for predicting students' cognitive state during learning

For distance learning applications, inferring the cognitive states of students, particularly, their concentration and comprehension levels during instruction, is important to assess their learning efficacy. In this paper, we investigated the feasibility of using EEG recordings generated from an off-the-shelf, wearable device to automatically classify the cognitive states of students as they were asked to perform a series of reading and question answering tasks. We showed that the EEG data can effectively predict whether a student is attentive or distracted as well as the student's reading speed, which is an important measure of reading fluency. However, the EEG signals alone are insufficient to predict how well the students can correctly answer questions related to the reading materials as there were other confounding factors, such as the students' background knowledge, that must be taken into consideration. We also showed that the accuracy in predicting the different cognitive states depends on the choice of classifier used (global, local, or multi-task learning). For example, the concentration level of a student can be accurately predicted using a local model whereas a global model that incorporates side information about the student's background knowledge is more effective at predicting whether the student will correctly answer questions about the materials they read.