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Download full textBailey, Daniel; Lee, Andrea Rakushin – TESOL International Journal, 2020
Different genres of writing entail various levels of syntactic and lexical complexity, and how this complexity influences the results of Automatic Writing Evaluation (AWE) programs like Grammarly in second language (L2) writing is unknown. This study explored the use of Grammarly in the L2 writing context by comparing error frequency, error types…
Descriptors: Grammar, Computer Assisted Instruction, Error Correction, Feedback (Response)
Direct linkHenshaw, Florencia – Language Teaching Research, 2012
Proponents of Processing Instruction (VanPatten, 2005) claim that learners benefit most when presented with both referential and affective structured input activities. Following a classic pretest-posttest design, the present study investigates the role of these two types of activities on the learning of the Spanish subjunctive. Groups differed…
Descriptors: Pretests Posttests, Scores, Spanish, Spanish Speaking
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
Schirmeier, Matthias K.; Derwing, Bruce L.; Libben, Gary – Brain and Language, 2004
Two types of experiments investigate the visual on-line and off-line processing of German ver-verbs (e.g., verbittern "to embitte"). In Experiments 1 and 2 (morphological priming), latency patterns revealed the existence of facilitation effects for the morphological conditions (BITTER-VERBITTERN and BITTERN-VERBITTERN) as compared to the neutral…
Descriptors: Language Processing, Morphology (Languages), Semantics, German
Peer reviewedPijls, Fieny; And Others – Instructional Science, 1987
Discusses grammar and spelling instruction in The Netherlands for students aged 10-15 and describes an intelligent computer-assisted instructional environment consisting of a linguistic expert system, a didactic module, and a student interface. Three prototypes are described: BOUWSTEEN and COGO for analyzing sentences, and TDTDT for conjugating…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Developed Nations, Dutch
Peer reviewedSchwind, 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)
PDF pending restorationOrnan, Uzzi – Association for Literary and Linguistic Computing Bulletin, 1978
The ability of the computer to generate output not included in the input may be used for linguistic as well as for computational input. The ability to accept linguistic data and process it according to a certain program seems to be a promising field for investigation. Progress in this field may strengthen the assumption that the computer can be…
Descriptors: Computational Linguistics, Computer Assisted Instruction, Computer Programs, Educational Technology
