Using chatbots to support EFL listening decoding skills in a fully online environment

Abstract

Aural decoding skill is an important contributor to successful EFL listening comprehension. This paper first described a preliminary study involving a 12-week undergraduate flipped decoding course, based on the flipped SEF-ARCS decoding model. Although the decoding model (N = 44) was significantly more effective in supporting students’ decoding performance than a conventional decoding course (N = 36), two main challenges were reported: teacher’s excessive workload, and high requirement for the individual teacher’s decoding skills. To address these challenges, we developed a chatbot based on the self-determination theory and social presence theory to serve as a 24/7 conversational agent, and adapted the flipped decoding course to a fully online chatbot-supported learning course to reduce the dependence on the teacher. Although results revealed that the chatbot-supported fully online group (N = 46) and the flipped group (N = 43) performed equally well in decoding test, the chatbot-supported fully online approach was more effective in supporting students’ behavioral and emotional engagement than the flipped learning approach. Students’ perceptions of the chatbot-supported decoding activities were also explored. This study provides a useful pedagogical model involving the innovative use of chatbot to develop undergraduate EFL aural decoding skills in a fully online environment.