Input Enhancement and Input Flood in Data-Driven Learning
摘要
Effective language instruction aims to convert input into intake through input flooding and input enhancement. Input flooding involves exposing learners to numerous instances of a target language feature, while input enhancement uses strategies to make language features more salient and noticeable. This entry discusses techniques of input flood and input enhancement that are often used in DDL. These techniques—such as using concordance lines, textual highlights, frequency information, comparison across languages and varieties, collocations, visualisation, auditory highlights, focusing on pragmatic functions, and corrective feedback—are showcased with examples from research and practical collections of DDL activities.