Challenging Learners in Their Individual Zone of Proximal Development Using Pedagogic Developmental Benchmarks of Syntactic Complexity

摘要

This paper introduces an Intelligent Computer Assisted Language Learning system designed to provide reading input for language learners based on the syntactic complexity of their language production. The system analyzes the linguistic complexity of texts produced by the user and of texts in a pedagogic target language corpus to identify texts that are well-suited to foster acquisition. These texts provide developmental benchmarks offering an individually tailored language challenge, making ideas such as Krashen's i+1 or Vygotsky's Zone of Proximal Development concrete and empirically explorable in terms of a broad range of complexity measures in all dimensions of linguistic modeling.