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.
Original language | English |
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Title of host publication | NAACL HLT 2018, Innovative Use of NLP for Building Educational Applications |
Subtitle of host publication | Proceedings of the Thirteenth Workshop |
Editors | Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis |
Place of Publication | Stroudsburg PA USA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 356-364 |
Number of pages | 9 |
ISBN (Electronic) | 9781948087117 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | Innovative Use of NLP for Building Educational Applications 2018 - New Orleans, United States of America Duration: 5 Jun 2015 → 5 Jun 2015 Conference number: 13th https://aclanthology.org/W18-0500/ |
Conference
Conference | Innovative Use of NLP for Building Educational Applications 2018 |
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Country/Territory | United States of America |
City | New Orleans |
Period | 5/06/15 → 5/06/15 |
Internet address |