An Examination of Corrective, Reflective, and Rule-based Feedback in Chinese Classifier Acquisition in a CALL Environment

Abstract

To facilitate effective learning in a computer-assisted language learning (CALL) environment, it is essential for the system to aid learners to not only pinpoint correct answers, but also identify the right process of learning so as to efficiently overcome various levels of difficulty with optimized practicing items. This study investigates how and to what extent different types of feedback from the CALL system may promote the grammatical knowledge learning for L2 learners of Chinese. Students in the Elementary Chinese program at the Carnegie Mellon University participated in the experiment of the computer assisted language tutoring for learning Chinese classifiers. Three kinds of feedback, namely corrective feedback, reflective feedback, and rule-based feedback, were designed and the relative effectiveness of each on the learning of the planned grammatical knowledge was assessed with pre and posttests. The results show that participants in the rule-based feedback group surpassed those in reflective and corrective feedback groups in an immediate posttest, but participants in the reflective feedback group outperformed the other two groups in a two-week delayed posttest. It is concluded that reflective feedback can more effectively promote self-explanation and memory retention in Chinese classifier acquisition in a CALL environment. The findings provide important insights for the construction of a dynamic, interactive Chinese learning courseware with adequate task design and optimal feedbacks.