Conceptualization of IMS that Estimates Learners' Mental States from Learners' Physiological Information Using Deep Neural Network Algorithm
Conceptualization of IMS that Estimates Learners' Mental States from Learners' Physiological Information Using Deep Neural Network Algorithm
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使用深度神经网络算法根据学习者的生理信息估计学习者心理状态的 IMS 的概念化
DOI：
10.1007/978-3-030-22244-4_9
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Tatsuro Uno
中科院分区：
文献类型：
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作者：
T. Matsui;Yoshimasa Tawatsuji;Siyuan Fang;Tatsuro Uno
为了提高教学效率，了解学习者在学习过程中的心理状态非常重要。在这项研究中，我们试图利用机器学习来提取学习者的心理状态和学习者的生理信息之间的关系，并辅以教师的言语行为。系统仿真结果表明，该系统能够高精度地估计学习者的心理状态。在系统建设的基础上，我们进一步讨论了IMS的概念以及IMS发展未来必要的工作。
To improve the efficiency of teaching and learning, it is substantially important to know learners’ mental states during their learning processes. In this study, we tried to extract the relationships between the learner’s mental states and the learner’s physiological information complemented by the teacher’s speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner’s mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.
利用深度学习从学习者的生理信息实时估计学习者的心理状态
DOI：
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发表时间：
2018
期刊：
影响因子：
--
作者：
Yoshimasa TAWATSUJI;Tatsuro UNO;Siyuan Fang;Tatsunori MATSUI
通讯作者：
Tatsunori MATSUI