한국언어과학회

The English article system has been known one of the most challenging components of English grammar for foreign learners to master, which needs continuous learning and feedback through assessments. However, current cognitive diagnostic models(CDMs) show some serious limitations: heavy reliance on large scale data, inability to model skill hierarchies and insufficient flexibility to efficiently handle small but repeated measurements. The purpose of the current study was to examine the potential of Bayesian network-based cognitive diagnostic modeling(BN-CDM) as an alternative to the current CDMs. A group of 124 college students(98 females and 26 males) joined a weekly 10-min learning session of the English article system throughout a semester and were administered a series of diagnostic tests. The test data were analyzed using conventional CDMs and BN-CDM. The results show that BN-CDM can handle small but repeated test data much more efficiently than conventional CDMs with a full consideration of hierarchical structures of the subject domain. The study also discusses some pedagogical implications of the results.