Decoding individual differences in STEM learning from functional MRI data
Decoding individual differences in STEM learning from functional MRI data
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DOI：
10.1038/s41467-019-10053-y
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发表时间：
2019-05-02
影响因子：
16.6
通讯作者：
Kraemer, David J. M.
中科院分区：
文献类型：
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作者：
Cetron, Joshua S.;Connolly, Andrew C.;Kraemer, David J. M.
传统的概念知识测试会产生分数来评估学习者对概念的理解程度。在这里，我们调查了在概念知识任务中收集的大脑活动模式是否可以用来计算神经‘分数’，以补充个人概念理解的传统分数。使用一种新的数据驱动的多变量神经成像方法-信息网络分析-我们成功地从整个大脑的活动模式中得出了神经评分，该模式预测了物理和工程领域中多个概念知识任务的个体差异。这些任务包括功能磁共振成像范式，以及另外两个先前经过验证的概念清单。信息网络评分优于使用数据驱动的神经成像方法计算的替代神经评分，包括多变量表征相似性分析。这项技术可用于量化广泛领域的概念知识，包括基于课堂的教育研究、机器学习和认知科学的其他领域。
Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual's conceptual understanding. Using a novel data-driven multivariate neuroimaging approach-informational network analysis-we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.