Feature Engineering for Second Language Acquisition Modeling

Conference paper (2018)

Authors

G. Chen Web Information Systems - EEMCS
C. Hauff Web Information Systems - EEMCS
G.J.P.M. Houben Web Information Systems - EEMCS
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2018 G. Chen, C. Hauff, G.J.P.M. Houben
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

Abstract

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.

Files

Chen.slam18.pdf
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