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Huang, Ping-Yu; Tsao, Nai-Lung – Computer Assisted Language Learning, 2021
In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), Error Correction
Chang, Yu-Chia; Chang, Jason S.; Chen, Hao-Jan; Liou, Hsien-Chin – Computer Assisted Language Learning, 2008
Previous work in the literature reveals that EFL learners were deficient in collocations that are a hallmark of near native fluency in learner's writing. Among different types of collocations, the verb-noun (V-N) one was found to be particularly difficult to master, and learners' first language was also found to heavily influence their collocation…
Descriptors: Sentence Structure, Verbs, Nouns, Foreign Countries
Chan, Tun-pei; Liou, Hsien-Chin – Computer Assisted Language Learning, 2005
This study investigates the influence of using five web-based practice units on English verb-noun collocations with the design of a web-based Chinese-English bilingual concordancer (keyword retrieval program) on collocation learning. Thirty-two college EFL students participated by taking a pre-test and two post-tests, and responding to a…
Descriptors: Measures (Individuals), Internet, English (Second Language), Indexes

Schwind, Camilla B. – Computer Assisted Language Learning, 1995
Presents a framework for dealing with errors in natural language sentences within the context of automated second-language teaching. Using a feature grammar, it is possible to describe various types of errors in a uniform framework, clearly define an error, and analyze the error source. (24 references) (Author/CK)
Descriptors: Computer Assisted Instruction, Context Effect, Discourse Analysis, Error Analysis (Language)