Estimation of degree of retention and subjective difficulty of four-choice English vocabulary questions using a wearable device

In this study, we develop a system that estimates the degree of retention and subjective difficulty of four-choice questions on English vocabulary attempted by learners on e-learning platforms. Our system uses a support vector machine (SVM) with sensor data obtained by JINS MEME and MyBeat for the estimation. In conventional face-to-face classes, teachers can read the learning attitudes of the students and make changes to the lecture accordingly. Similarly, in e-learning scenarios where there is no instructor, if the degree of retention and subjective difficulty experienced by the learner can be estimated, there is a possibility that more appropriate learning content and efficient learning support can be presented, thereby maintaining high levels of motivation. Therefore, in this study, we conducted an experiment to record sensor data from wearable devices during the execution of four-choice tasks related to English words and evaluated the accuracy of estimation using our proposed method.