Knowledge tracing using the brain

David Halpern, Shannon Tubridy, Hong Yu Wang, Camille Gasser, Pamela Osborn Popp, Lila Davachi, Todd M. Gureckis

Research output: Contribution to conferencePaperpeer-review

Abstract

Knowledge tracing is a popular and successful approach to modeling student learning. In this paper we investigate whether the addition of neuroimaging observations to a knowledge tracing model enables accurate prediction of memory performance in held-out data. We propose a Hidden Markov Model of memory acquisition related to Bayesian Knowledge Tracing and show how continuous functional magnetic resonance imaging (fMRI) signals can be incorporated as observations related to latent knowledge states. We then show, using data collected from a simple second-language learning experiment, that fMRI data acquired during a learning session can be used to improve predictions about student memory at test. The fitted models can also potentially give new insight into the neural mechanisms that contribute to learning and memory.

Original languageEnglish (US)
StatePublished - Jan 1 2018
Event11th International Conference on Educational Data Mining, EDM 2018 - Buffalo, United States
Duration: Jul 15 2018Jul 18 2018

Conference

Conference11th International Conference on Educational Data Mining, EDM 2018
CountryUnited States
CityBuffalo
Period7/15/187/18/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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