Feature Engineering for Second Language Acquisition Modeling

Feature Engineering for Second Language Acquisition Modeling

Chen, G. (TU Delft Web Information Systems)
Hauff, C. (TU Delft Web Information Systems)
Houben, G.J.P.M. (TU Delft Web Information Systems)

Tetreault, Joel (editor)
Burstein, Jill (editor)
Kochmar, Ekaterina (editor)
Leacock, Claudia (editor)
Yannakoudakis, Helen (editor)

2018

Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students’ knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling (Settles et al., 2018) provides students’ trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students’ learning traces reveal that factors like exercise format and engagement impact their exercise performance to a large extent. Overall, we extracted 23 different features as input to a Gradient Tree Boosting framework, which resulted in an AUC score of between 0.80 and 0.82 on the official test set.

http://resolver.tudelft.nl/uuid:9258d413-129f-45e8-848c-2a9083d3bcc8

Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications

Institutional Repository

conference paper

© 2018 G. Chen, C. Hauff, G.J.P.M. Houben