Knowledge Tracing in Sequential Learning of Inflected Vocabulary

Adithya Renduchintala, Philipp Koehn, Jason Eisner


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
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
238–247
Language:
URL:
https://www.aclweb.org/anthology/K17-1025
DOI:
10.18653/v1/K17-1025
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PDF:
http://aclanthology.lst.uni-saarland.de/K17-1025.pdf