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Discussion and Conclusion
As this study has shown, some modest gains can potentially be made in OTI by relying on
VanPatten’s principles of input processing. First, automatic analysis of the Sentence
Location Principle helps identify tokens in which the target language structure is near the
beginning of the sentence, where it is likely to be processed more easily. Second,
identifying lexical redundancy of grammatical information makes it possible to easily
generate fill-in-the-blank and similar exercises, as well as providing a method for
generating exercises with more than one target language structure per document. Lastly,
automatic analysis of the First Noun Principle (8) makes it possible to avoid or leverage
likely misinterpretations of a sentence. Future research will evaluate implementations of
these principles in comparison with teacher judgments and learner performance.
As mentioned above, measures of cognitive load can be significantly improved by the
availability of student models specifying known words and grammar principles. Although
several ICALL tutoring systems have implemented student models, no authentic text ICALL
systems make use of them. Authentic text ICALL systems are faced with processing
diverse and unpredictable vocabulary, with few natural opportunities to check learner
knowledge of words. Future research is needed to identify effective methods to initially
estimate and then maintain student models in an authentic text ICALL environment.
To the extent that VanPat
ten’s principles of input processing are valid predictors of student
uptake of form-
meaning connections, the operationalizations of VanPatten’s principles
suggested in this paper can be used in computer applications to automatically evaluate
sentences for likelihood of learner uptake.
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