Abstract
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.- Anthology ID:
- W18-0545
- Volume:
- Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 370–373
- Language:
- URL:
- https://aclanthology.org/W18-0545
- DOI:
- 10.18653/v1/W18-0545
- Cite (ACL):
- Jill-Jênn Vie. 2018. Deep Factorization Machines for Knowledge Tracing. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 370–373, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Deep Factorization Machines for Knowledge Tracing (Vie, BEA 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W18-0545.pdf
- Code
- jilljenn/ktm