Using automatic speech recognition technology to enhance EFL learners' oral language complexity in a flipped classroom

The present study examined the effects of using automatic speech recognition (ASR) technology on oral complexity in a flipped English as a Foreign Language (EFL) course. A total of 160 undergraduates were enrolled in a 14-week quasi-experiment. The experimental group (EG) and the control group (CG) were taught with a flipped approach, but the EG students needed to undertake an additional pre-class task with ASR technology. In each unit, all students' in-class task performance was recorded, based on which the metrics of oral complexity were coded and computed. A two-way between- and within-subjects repeated measures design was conducted to examine the effects of the group factor, the time factor and the group x time interaction effects. The results showed that the EG students performed statistically better than their counterparts in the CG on lexical complexity and syntactic complexity. Moreover, significant improvement in phrasal complexity was witnessed over time in both groups. Significant group x time interaction effects were witnessed on overall complexity or subordination complexity. The gradients of the EG trajectories of the two metrics were greater than those of the CG. However, on phrasal complexity, the interaction effect was not significant. Implications for practice or policy: EFL teachers could integrate ASR technology into pre-class tasks to improve students' oral English complexity. EFL teachers need to be aware that phrasal complexity may be more sensitive to flipped EFL instruction than overall complexity and subordination complexity. Course developers could integrate ASR technology in fostering EFL learners' overall complexity and subordination complexity.