Neural imaging to track mental states while using an intelligent tutoring system
Neural imaging to track mental states while using an intelligent tutoring system
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DOI：
10.1073/pnas.1000942107
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发表时间：
2010-04-13
影响因子：
11.1
通讯作者：
Fincham, Jon M.
中科院分区：
文献类型：
--
作者：
Anderson, John R.;Betts, Shawn;Fincham, Jon M.
脑活动的血液动力学测量可以用来解释学生在与智能辅导系统交互时的精神状态。功能性磁共振成像（fMRI）的数据收集，而学生与一个辅导系统，教代数同构。一个认知模型从问题复杂性的测量中预测了解决时间的分布。另外，线性判别分析使用功能磁共振成像数据来预测学生是否参与解决问题。隐马尔可夫算法合并了这两个信息源，以预测学生在解决问题时的心理状态。该算法在来自1天的交互的数据上进行训练，并在随后的一天中使用数据进行测试。在预测学生在2秒内的状态方面，该算法在训练数据上达到了87%的准确率，在测试数据上达到了83%的准确率。结果说明了整合自下而上的信息从成像数据与自上而下的信息从认知模型的重要性。
Hemodynamic measures of brain activity can be used to interpret a student's mental state when they are interacting with an intelligent tutoring system. Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from 1 day of interaction and tested with data from a later day. In terms of predicting what state a student was in during a 2-s period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. The results illustrate the importance of integrating the bottom-up information from imaging data with the top-down information from a cognitive model.