Knowledge Tracing Using the Brain
Knowledge Tracing Using the Brain
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DOI：
10.17605/osf.io/p7yzn
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发表时间：
2018-04
影响因子：
7.4
通讯作者：
D. Halpern;Shannon Tubridy;Hong Yu Wang;Camille Gasser;P. O. Popp;L. Davachi;T. Gureckis
中科院分区：
文献类型：
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作者：
D. Halpern;Shannon Tubridy;Hong Yu Wang;Camille Gasser;P. O. Popp;L. Davachi;T. Gureckis
知识追踪是一种流行且成功的学生学习建模方法。在本文中，我们调查是否增加了神经影像学的观察知识追踪模型，使记忆性能的准确预测，保持了数据。我们提出了一个隐马尔可夫模型的记忆采集相关的贝叶斯知识追踪，并显示如何连续的功能性磁共振成像（fMRI）信号可以被纳入有关的潜在知识状态的意见。然后，我们表明，使用从一个简单的第二语言学习实验中收集的数据，在学习过程中获得的功能磁共振成像数据可以用来提高学生记忆测试的预测。拟合模型还可以潜在地为有助于学习和记忆的神经机制提供新的见解。
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.