Feature Engineering for Second Language Acquisition Modeling

Guanliang Chen, Claudia Hauff, Geert-Jan Houben


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 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.
Anthology ID:
W18-0543
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
356–364
Language:
URL:
https://aclanthology.org/W18-0543
DOI:
10.18653/v1/W18-0543
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W18-0543.pdf