Knowledge Tracing in Sequential Learning of Inflected Vocabulary

Adithya Renduchintala, Philipp Koehn, Jason Eisner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Place of PublicationVancouver, Canada
PublisherAssociation for Computational Linguistics
Pages238-247
Number of pages10
ISBN (Print)978-1-945626-54-8
DOIs
Publication statusPublished - 1 Aug 2017
Event 21st Conference on Computational Natural Language Learning - Vancouver, Canada
Duration: 3 Aug 20174 Aug 2017
http://www.conll.org/2017

Conference

Conference 21st Conference on Computational Natural Language Learning
Abbreviated titleCoNLL 2017
CountryCanada
CityVancouver
Period3/08/174/08/17
Internet address

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