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Coalescing individual and collaborative learning to model user linguistic competences

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Abstract

A linguistic, pedagogic and technological framework for an ICALL system called COPPER is presented here, where individual and collaborative learning are combined within a constructivist approach to facilitate second language learning. Based upon the Common European Framework of Reference for Languages, the ability to use language is viewed as one of several cognitive competences that are mobilised and modified when individuals communicate. To combine the different types of learning underlying the European Framework, a student model has been developed for COPPER that represents linguistic competences in a detailed way, combining high granularity expert-centric Bayesian networks with multidimensional stereotypes, and is updated following student activities semi-automatically. Instances of this model are used by an adaptive group formation algorithm that dynamically generates communicative groups based upon the linguistic capabilities of available students, and a collection of collaborative activity templates. As well as the student model, which is a representation of individual linguistic knowledge, preferences, etc., there is a group model, which is a representation of how a set of students works together. The results of a student’s activity within a group are evaluated by a student monitor, with more advanced linguistic competences, thereby sidestepping the difficulties present when using NLP techniques to automatically analyse non-restricted linguistic production. The monitor role empowers students and further consolidates what has been previously learnt. Students therefore initially work individually in this framework on certain linguistic concepts, and subsequently participate in authentic collaborative communicative activities, where their linguistic competences can develop approximately as they would in ‘real foreign language immersion experiences’.

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Read, T., Barros, B., Bárcena, E. et al. Coalescing individual and collaborative learning to model user linguistic competences. User Model User-Adap Inter 16, 349–376 (2006). https://doi.org/10.1007/s11257-006-9014-5

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