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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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 164:7, p. 65-78
NEALT Proceedings Series 39:7, p. 65-78
Published: 2019-09-30
ISBN: 978-91-7929-998-9
ISSN: 1650-3686 (print), 1650-3740 (online)
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.
learning analytics, adaptive language learning system, individual differences, vocabulary growth
