Constructing Knowledge-Based Feedback in the Context of an Interactive Spoken CALL Application

Abstract

Modern Computer Assisted Language Learning (CALL) systems use speech recognition to give students the opportunity to build up their spoken language skills through interactive practice with a mechanical partner. Besides the obvious benefits that these systems can offer, e.g. flexible and inexpensive learning, user interaction in this context can often be problematic. In this article, the authors introduce a parallel layer of feedback in a CALL application, which can monitor interaction, report errors and provide advice and suggestions to students. This mechanism combines knowledge accumulated from four different inputs in order to decide on appropriate feedback, which can be customized and adapted in terms of phrasing, style and language. The authors report the results from experiments conducted at six lower secondary classrooms in German-speaking Switzerland with and without this mechanism. After analyzing approximately 13,000 spoken interactions it can be reasonably argued that their parallel feedback mechanism in L2 actually does help students during interaction and contributes as a motivation factor.