Construction of a Knowledge Map-based System for Personalized Second Language Learning (2019)
Abstract: A great number of Computer-Assisted Language Learning (CALL) systems have been developed, focusing on one
particular language skill and a limited range of user expertise. In this paper, we propose the use of a knowledge graph that contains
both lexicon and grammatical data that serve as the basis of learner’s model. By being populated with event log data from external
sources, the graph could serve as a user model for client systems in a lifelong learning perspective. The knowledge map is exposed
as a service that can be interfaced with existing CALL and learning analytics tools. Beyond the direct display of the knowledge
map to the learner that most existing systems provide, we investigate text-based use cases, such as: recommending grammatical
concepts with examples tailored to the user's current knowledge, and quiz generation that feeds the system's feedback loop.