Performance-Based Content Generation for Language Learning

Performance-Based Content Generation for Language Learning

Kostas Karpouzis, Research Director, Institute of Communication and Computer Systems, AI, Learning Systems Lab, Athens, GreeceTrack 3 HIGHER ED

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Generation of appropriate content for serious/educational games is an extremely important concept since it can make all the difference between adoption and retainment of the game, which increase the possibility to achieve its learning objectives, and attrition.

In the iRead project, we are creating a serious game and supporting applications for entry-level language learning. Content for the game is generated based on language models for each school year, learning models (sequencing of features that need to be mastered) and student performance with respect to each language feature.

Several teaching and learning objectives are enforced through rules which govern content generation, such as fostering motivation and efficacy, promoting accuracy before automaticity, and allowing revision of already mastered language features." The main takeaway of the proposed lecture will be a flexible way to model language, teaching priorities and student mastery, allowing for personalized learning and student analytics. This modeling approach can be extended to different languages, as long as each of them can be modeled as a set of features, taught sequentially; based on that, personalized content selection may be used to select words, sentences or passages of text, suited for each student based on their performance and teacher-selected learning objectives.

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