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首页> 外文会议>Humaine Association Conference on Affective Computing and Intelligent Interaction >Which Is More Responsible for Boredom in Intelligent Tutoring Systems: Students (Trait) or Problems (State)?
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Which Is More Responsible for Boredom in Intelligent Tutoring Systems: Students (Trait) or Problems (State)?

机译：哪个更负责智能辅导系统中的无聊：学生（特质）或问题（州）？

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摘要

Boredom is unpleasant, and has been repeatedly shown to be associated with poor performance and long-term disengagement in educational contexts. Boredom is prevalent within a range of online learning environments, has been shown to correlate negatively with learning in those environments, and often precedes disengaged behaviors such as off-task behavior and gaming the system. Therefore, it is important to identify the causes of boredom in these environments. In psychology research, there is ongoing debate about the degree to which individual students are prone to boredom ("trait" explanations) or the degree to which boredom is driven by state-based factors, such as the design of the learning environment. In this study, we apply an unobtrusive computational detector of student boredom to log data from an intelligent tutoring system to determine whether state or trait factors better predict the prevalence of boredom in students using that system. Knowing which type of factor better predicts boredom in a specific system can help us to narrow down further research on why boredom occurs and what steps should be taken to mitigate boredom's negative effects.
机译：无聊是不愉快的，并已被反复证明是与在教育环境中表现不佳，长期脱离关联。无聊是一系列的在线学习环境中普遍存在，已被证明在这些环境中学习负相关，并且往往预示分离行为，如关闭任务行为和游戏系统。因此，以确定在这些环境中无聊的原因是很重要的。在心理学的研究，有关于对个别学生容易感到无聊（“性状”的解释），或到无聊是基于状态的因素，如学习环境的设计驱动的程度程度的辩论。在这项研究中，我们应用学生无聊的一个不显眼的计算探测器记录从智能教学系统数据，以确定状态或特性的因素是否更好地预测无聊的在使用该系统的学生普遍存在。知道哪些因素更好的类型预测无聊在一个特定的系统可以帮助我们缩小为什么无聊时和应采取措施，以减轻无聊的负面影响的进一步研究。

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