A pilot study of data-driven learning approach in teaching Chinese vocabulary

Abstract

[EN] The Data-Driven Learning (DDL) approach advocates a shift from passive knowledge recipients to active researchers among learners. This is achieved by utilizing abundant and context-rich target language inputs in a bottom-up learning process (Johns, 1990). Despite the scarcity of empirical research on the implementation of DDL in Chinese as a Second Language (CSL) classrooms, this study conducted a teaching experiment focused on confusable words to explore the approach's effects and learners' attitudes. Five advanced-level CSL learners from diverse native language backgrounds participated in the study, being taught ten sets of confusable words over a five-week period. The first five lessons employed an indirect DDL method, while the latter five utilized a direct DDL method with Sketch Engine. To evaluate the effectiveness of the DDL approach, a pre-test was administered prior to the course, followed by a post-test, a questionnaire, and one-on-one interviews after the completion of the course. The results of the post-test revealed an average improvement of 24% compared to the pre-test with statistical significance. Additionally, learners exhibited a positive attitude towards the course, expressing a preference for learning vocabulary through collocations and showing a desire to observe pre-selected concordance lines under the guidance of the instructor.