A Phoneme-Based Student Model for Adaptive Spelling Training

Authors:
Gian-Marco Baschera;Markus Gross
Affiliations:
Department of Computer Science, ETH Zürich, Switzerland;Department of Computer Science, ETH Zürich, Switzerland
Venue:
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Year:
2009

Citing 1
Cited 1

Technical Section: A multimedia framework for effective language training

Computers and Graphics

Poisson-based inference for perturbation models in adaptive spelling training

International Journal of Artificial Intelligence in Education

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Abstract

We present a novel phoneme-based student model for spelling training. Our model is data driven, adapts to the user and provides information for, e.g., optimal word selection. We describe spelling errors using a set of features accounting for phonemic, capitalization, typo, and other error categories. We compute the influence of individual features on the error expectation values based on previous input data using Poisson regression. This enables us to predict error expectation values and to classify errors probabilistically. Our model is generic and can be utilized within any intelligent language learning environment.