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An empirical examination of the relation between attention and motivation in computer-based education: a modeling approach

机译:基于计算机的教育中注意力与动机之间关系的实证检验:一种建模方法

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摘要

Attention is considered a pre-requisite to achieve greater motivation in the classroom. However, empirical evidence of this relationship in educational setting is scarce since the measurement of attention requires specialized equipment such as clinical electroencephalograms (EEG) or fMRI. With the advent of portable, consumer-oriented EEG it is now possible to estimate levels of attention and shed light onto this relationship in the context of a computer-based educational setting. To that end, students (N=40) interacted for an average of 9.48 minutes (SD = .0018) with an assessment exercise in a virtual world. Participants’ attention levels were monitored via a portable EEG and incorporated into an attention model capable of deciding on strategies to correct low levels of attention. The participants’ motivation was assessed using a self-reported motivation questionnaire at pre-test and post-test times. The results indicated that students with higher self-reported motivation and self-reported attention answered significantly more correct answers. However, no direct evidence was found of a relation between EEG readings and self-reported attention or self-reported motivation. This suggests student’s own perceptions of motivation and attention influence performance. Future work consists of defining new models of attention considering self-perceived attention and motivation as baseline as well as improving the model of attention combining EEG reading with an indication of the students’ gaze.
机译:注意被认为是在课堂上获得更大动力的先决条件。但是,由于注意力的测量需要专门的设备,例如临床脑电图(EEG)或fMRI,因此在教育环境中缺乏这种关系的经验证据。随着便携式,面向消费者的EEG的出现,现在可以在基于计算机的教育环境中估计注意力水平并阐明这种关系。为此,学生(N = 40)在虚拟世界中进行了评估练习,平均互动了9.48分钟(SD = .0018)。通过便携式脑电图监测参与者的注意力水平,并将其纳入能够决定纠正低水平注意力策略的注意力模型中。在测试前和测试后的时间使用自我报告的动机调查表评估了参与者的动机。结果表明,自我报告的动机和自我报告的注意力较高的学生回答的正确率明显更高。但是,没有发现直接证据表明脑电图读数与自我报告的注意力或自我报告的动机之间存在关联。这表明学生对动机和注意力的看法会影响他们的表现。未来的工作包括定义新的注意力模型,将自我感知的注意力和动机作为基线,并结合EEG阅读和学生凝视的指示来改进注意力模型。

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