Drilon Avdiu
Department of Informatics, Technical University of Munich, Germany
Vanessa Bui
Department of Informatics, Technical University of Munich, Germany
Klára Ptacinová Klimci´ková
Class of Language Education, LMU Munich, Gernany
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Published in: Proceedings of the 8th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2019), September 30, Turku Finland
Linköping Electronic Conference Proceedings 164:1, p. 1-9
NEALT Proceedings Series 39:1, p. 1-9
Published: 2019-09-30
ISBN: 978-91-7929-998-9
ISSN: 1650-3686 (print), 1650-3740 (online)
Predicting the knowledge of language
learners is crucial for personalized interactions in any intelligent tutoring system for language learning. This study
adopts a machine learning approach to the
task of predicting the knowledge of single words for individual learners of English. We experiment with two machine
learning models, neural networks and random forest, and with a set of learner-specific and word-specific features. Both
the models are trained for all the learners
together. However, since learner-specific
features are used, the prediction is personalized for every learner. Both of the models achieve state-of-the-art results for the
task of vocabulary prediction for English
learners.
word knowledge prediction, second language vocabulary assessment, intelligent tutoring system, machine learning
