The Data-Driven Learning (DDL) approach advocates for learners to transition from passive knowledge recipients to active researchers by using typical, large-scale, and context-rich target language inputs to drive a bottom-up learning process (Johns, 1990). Given the scarcity of empirical research on applying DDL in Chinese as a second language (CSL) classrooms, this study designed a teaching experiment for confusable words to explore the effects and learners' attitudes towards the approach. The course recruited five CSL learners with different native languages at the advanced proficiency level, and taught ten sets of confusable words over a five-week period. The first five lessons used an indirect DDL method, while the latter five used a direct DDL method with Sketch Engine. Prior to the course, a pre-test was conducted, and after completion, a questionnaire, post-test, and interviews were administered. The post-test showed an average improvement of 24% over the pre-test, and the Wilcoxon signed-rank test indicated a statistically significant improvement in applying DDL to learning confusable words. Furthermore, learners had a positive attitude towards the course, generally favoring learning vocabulary through collocations, and preferred to observe pre- selected concordance lines under the guidance of the instructor.