摘要：As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to
learn English. Researchers have noted that extensive reading is an effective way to improve a person's command
of English. Choosing suitable articles in accordance with a learner's needs, interests and ability using an elearning
system requires precise learner profiles. This paper proposes a personalized English article
recommending system, which uses accumulated learner profiles to choose appropriate English articles for a
learner. It employs fuzzy inference mechanisms, memory cycle updates, learner preferences and analytic
hierarchy process (AHP) to help learners improve their English ability in an extensive reading environment. By
using fuzzy inferences and personal memory cycle updates, it is possible to find an article best suited for both a
learner¡¯s ability and her/his need to review vocabulary. After reading an article, a test is immediately provided
to enhance a learner¡¯s memory for the words newly learned in the article. The responses of tests can be used to
explicitly update memory cycles of the newly-learned vocabulary. In addition, this paper proposes a
methodology that also implicitly modifies memory cycles of words that were learned before. By intensively
reading articles recommended through the proposed approach, learners comprehend new words quickly and
review words that they knew implicitly as well, thereby efficiently improving their vocabulary volume.
Analyses of learner achievements and questionnaires have confirmed that the adaptive learning method
presented in this study not only enhances the English ability of learners but also helps maintaining their learning
interest.
关键词：Intelligent tutoring systems, English learning, Fuzzy inference, Analytic hierarchy process
