Abstract
SLAM 2018 focuses on predicting a student’s mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features plays a major role in language acquisition modeling. Our model beats Duolingo’s baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.- Anthology ID:
- W18-0524
- Volume:
- Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 212–216
- Language:
- URL:
- https://aclanthology.org/W18-0524
- DOI:
- 10.18653/v1/W18-0524
- Cite (ACL):
- Nihal V. Nayak and Arjun R. Rao. 2018. Context Based Approach for Second Language Acquisition. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 212–216, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Context Based Approach for Second Language Acquisition (Nayak & Rao, BEA 2018)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/W18-0524.pdf
- Code
- additional community code
- Data
- Duolingo SLAM Shared Task