Abstract
We present a feature-rich knowledge tracing method that captures a student’s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student’s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.- Anthology ID:
- K17-1025
- Volume:
- Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Roger Levy, Lucia Specia
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 238–247
- Language:
- URL:
- https://aclanthology.org/K17-1025
- DOI:
- 10.18653/v1/K17-1025
- Cite (ACL):
- Adithya Renduchintala, Philipp Koehn, and Jason Eisner. 2017. Knowledge Tracing in Sequential Learning of Inflected Vocabulary. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 238–247, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Knowledge Tracing in Sequential Learning of Inflected Vocabulary (Renduchintala et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/K17-1025.pdf