Building a Learning Space for Language Acquisition
Résumé
Learning spaces offer a rigorous mathematical foundation for algorithms that can be employed in intelligent tutoring systems to organize the body of knowledge necessary to become proficient in a domain. These mathematical structures are particular types of partial ordered sets, and thus they can be built using formal concept analysis (FCA), which provides powerful formal tools, such as concept lattices, to order data of this nature. Learning spaces are helpful to bring to light implications between notions and uncover students’ different learning paths. These structures eventually help teachers create personalized learning plans and intelligently assess students. Learning spaces have been used to ease teaching and learning in a variety of subjects. However, applications in language acquisition remain challenging.
This manuscript presents an adaptation of this framework to language acquisition, specifically Mandarin, by leveraging the shared properties between learning space theory and FCA. The functioning of the tool realized is based on a local and dynamic exploration of the space, without the need to compute the whole structure beforehand. This way, a scalable system architecture, which is not limited by the size of the input and the power of the machine on which it runs, is achieved.
We report experiments conducted using several datasets and feedback provided by teachers who used the first prototype of the system. The results we obtain are encouraging and suggest that the approach can be adapted to other languages.
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