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Understanding Vocabulary Growth Through An Adaptive Language Learning System

Elma Kerz
RTWH Aachen University, Germany

Andreas Burgdorf
University of Wuppertal, Germany

Daniel Wiechmann
University of Amsterdam, The Netherlands

Stefan Meeger
RTWH Aachen University, Germany

Yu Qiao
RTWH Aachen University, Germany

Christian Kohlschein
RTWH Aachen University, Germany

Tobias Meisen
University of Wuppertal, Germany

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Ingår i: Proceedings of the 8th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2019), September 30, Turku Finland

Linköping Electronic Conference Proceedings 164:7, s. 65-78

NEALT Proceedings Series 39:7, s. 65-78

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Publicerad: 2019-09-30

ISBN: 978-91-7929-998-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Learning analytics and educational data mining have gained an increased interest as an important way of understanding the way humans learn. The paper introduces an adaptive language learning system designed to track and accelerate the development of academic vocabulary skills thereby generating dense longitudinal data of individual vocabulary growth trajectories. We report on an exploratory study based on the dense longitudinal data obtained from our system. The goal is the study was twofold: (1) to examine the pace and shape of vocabulary growth trajectories and (2) to understand the role various individual differences factors play in explaining variation in such growth trajectories.

Nyckelord

learning analytics, adaptive language learning system, individual differences, vocabulary growth

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