Knowledge Tracing in Sequential Learning of Inflected Vocabulary

Adithya Renduchintala, Philipp Koehn, Jason Eisner

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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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/K17-1025.pdf
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