Reading-While-Listening vs. Reading-Only in A Second Language at Different Language Proficiencies: an Eye-Tracking Study

Abstract

Reading-while-listening (R/L) has a facilitation effect on second language (L2) reading comprehension after longitudinal R/L training from empirical studies. However, most empirical evidence provides limited insight into how the auditory input affects readers’ language processing. When R/L was examined using eye movement metrics, a hindrance effect has been reported for L1 readers, and its facilitation effect on comprehension disappears for advanced-level L2 readers (Conklin et al., 2020). To study R/L’s effect on less adept L2 learners, this study compared the comprehension accuracy and eye movements of intermediate and elementary-level L2 readers of English between reading-only (R/O) and R/L modes. 22 university students in Macao completed a vocabulary test and reading comprehension tasks. Participants were assigned to either an intermediate-level group (n = 11) or an elementary-level group (n = 11) based on vocabulary test performance. Both groups completed the tasks while their eye movements were captured by a Tobii eye tracker. Results showed there was no significant difference between R/L and R/O in comprehension for the participant groups. Mixed model analyses of variance revealed significant main effects of reading mode (R/L or R/O) in total fixation durations and total visit durations, suggesting R/L facilitated processing of the text in both levels of participants. Significant interactions between the reading mode and participants’ language level showed that the facilitation was significantly greater for elementary-level L2 readers. Hence, we preliminarily established the accuracy of a continuum model that summarizes the differing effect of auditory input on readers across language proficiency levels.

Publication
In AsiaTEFL 2022 (also presented at The LELPG Conference 2023 at the University of Edinburgh)
Yingjia Wan
Yingjia Wan
Master’s student in Natural Language Processing

My research interests lie in LLM reasoning, debiasing, multimodality, and cognition-inspired NLP.