Predicting Reading Performance based on Eye Movement Analysis with Hidden Markov Models

Abstract

Reading is an essential medium for learning, but it is challenging to measure learners’ cognitive processes during reading. Eye-tracking, as an approach in multimodal learning analytics (MmLA), can provide fine-grained data that reflect cognitive processes during reading. In this study, we investigated whether eye movements could predict passage reading performance in addition to language proficiency and cognitive abilities. In particular, we assessed learners’ eye movement pattern and consistency through a novel method, Eye Movement analysis with Hidden Markov Models (EMHMM), in addition to traditional eye movement measures. We found that longer saccade length predicted faster reading speed Also, higher English proficiency predicted faster reading speed through the mediation of longer saccade length. In contrast, reading comprehension accuracy was best predicted by a more consistent eye fixation at the beginning of reading engagement, which may result from a better developed visual routine due to higher reading expertise. These findings have important implications for ways to assess and facilitate learners’ reading through eye movement measures and to examine factors influencing reading performance. The methods adopted could further the development of MmLA and serve as an empirical example of understanding learners’ cognitive processes through collecting and modeling critical learner-centered metrics in novel modalities.