Christina Schneegass
Ludwig Maximilian University of Munich, Munich, Germany
The pervasive availability of media in foreign languages is a rich resource for language learning. However, learners are forced to interrupt media consumption whenever comprehension problems occur. We present BrainCoDe, a method to implicitly detect vocabulary gaps through the evaluation of event-related potentials (ERPs). In a user study (N=16), we evaluate BrainCoDe by investigating differences in ERP amplitudes during listening and reading of known words compared to unknown words. We found significant deviations in N400 amplitudes during reading and in N100 amplitudes during listening when encountering unknown words. To evaluate the feasibility of ERPs for real-time applications, we trained a classifier that detects vocabulary gaps with an accuracy of 87.13% for reading and 82.64% for listening, identifying eight out of ten words correctly as known or unknown. We show the potential of BrainCoDe to support media learning through instant translations or by generating personalized learning content.
https://doi.org/10.1145/3313831.3376707
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)