Knowledge Tracing Using the Brain
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.
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Bibtex entry:
@inproceedings{halpern2018knowledge,
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.},
address = {Buffalo, NY},
author = {Halpern, D. and Tubridy, S. and Wang, H.Y. and Gasser, C. and Osborn Popp, P.J. and Davachi, L. and Gureckis, T.M.},
booktitle = {Proceedings of Educational Data Mining (EDM) 2018},
publisher = {PsyArXiv},
title = {Knowledge Tracing Using the Brain},
year = {2018}}QR Code:
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