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Bootstrapping in a language learning environment

This paper addresses a fundamental dilemma in the design of intelligent language learning environments: the more freedom a system offers to learners in the use of the target language, the more unwieldy the data is which the learners produce and the less able the system is to support inferences about learners from that data. It is shown how in a platform where learners and teachers interact, the teachers' feedback which is archived in the system and indexed to the learners' target language production can constitute affordances that support a process of bootstrapping from raw language output to potential insights into the learners' interlanguage and gaps in their grasp of the target language. The approach is illustrated with three types of learner errors uncovered in the corpus of learner English through this bootstrapping heuristic.