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The Effectiveness of a Pedagogical Agent's Immediate Feedback on Learners' Metacognitive Judgments during Learning with MetaTutor

机译:在使用MetaTutor学习期间,教学代理对学习者的元认知判断的即时反馈的有效性。

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Using pedagogical agents (PAs) in hypermedia learning environments have been found to be an effective way to scaffold students and provide tailored feedback to enhance learning outcomes. In this study, we investigated the effectiveness of immediate feedback provided by PA embedded in MetaTutor [1] (a multi-agent, adaptive hypermedia learning system) on learners' metacognitive calibration and bias of Feelings of Knowing (FOK) and Judgments of Learning (JOL), and accuracy of Content Evaluations (CE) made during a 2-hour learning session with the system. Seventy (N = 70) undergraduate students were randomly assigned to one of two instructional conditions: Immediate Feedback (IF) or the Control Group, where they were asked to learn about the circulatory system with the environment. Overall, pretest-posttest learning outcome data revealed that participants in the IF condition significantly outperformed those in the Control condition. Additionally, participants who received immediate feedback from the PA were more accurate and calibrated in their metacognitive judgments than those in the Control condition. An overall bias was found toward overconfidence in JOLs and FOKs for participants in both conditions. These findings have significant relevance for the understanding of metacognitive monitoring and regulation during complex learning with multi-agent systems and for designing metacognitive-responsive PAs capable of co-adapting to learners' cognitive and metacognitive regualtory processes.
机译:已经发现,在超媒体学习环境中使用教学代理(PAs)是一种有效的方法来为学生提供支持,并提供量身定制的反馈以增强学习成果。在这项研究中,我们调查了MetaTutor [1](一种多智能体,自适应超媒体学习系统)中嵌入的PA提供的即时反馈对学习者的元认知校准和知觉(FOK)和学习判断( JOL),以及在与系统进行2小时的学习期间进行的内容评估(CE)的准确性。七十名(N = 70)的本科生被随机分配到以下两个教学条件之一:即时反馈(IF)或对照组,要求他们在其中学习有关环境的循环系统。总体而言,测试前-测试后学习结果数据表明,IF条件下的参与者明显优于对照条件下的参与者。此外,接受过PA即时反馈的参与者在元认知判断中的准确度和校准性高于对照组。发现在这两种情况下,参与者对JOL和FOK的过度自信都存在总体偏见。这些发现对于在使用多智能体系统进行复杂学习期间理解元认知监控和调节,以及设计能够共同适应学习者的认知和元认知调节过程的元认知响应式PA具有重大意义。

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