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Interactive Event: The Rimac Tutor - A Simulation of the Highly Interactive Nature of Human Tutorial Dialogue

机译:互动事件:RIMAC导师 - 一种模拟人类教程对话的高度互动性

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Rimac is a natural-language intelligent tutoring system that engages students in dialogues that address physics concepts and principles, after they have solved quantitative physics problems. Much research has been devoted to identifying features of tutorial dialogue that can explain its effectiveness (e.g., [1]), so that these features can be simulated in natural-language tutoring systems. One hypothesis is that the highly interactive nature of tutoring itself promotes learning. Several studies indicate that our understanding of interactivty needs refinement because it cannot be defined simply by the amount of interaction nor the granularity of the interaction but must also take into consideration how well the interaction is carried out (e.g., [2]). This need for refinement suggests that we should more closely examine the linguistic mechanisms evident in tutorial dialogue. Towards this end, we first identified which of a subset of co-constructed discourse relations correlate with learning and operationalized our findings with a set of nine decision rules which we implemented in Rimac [3]. To test for causality, we are conducting pilot tests that compare learning outcomes for two versions of Rimac: an experimental version that deliberately executes the nine decision rules within a Knowledge Construction Dialogue (KCD) framework, and a control KCD system that does not intentionally execute these rules.
机译:Rimac是一种自然语言智能辅导系统,可以在解决定量物理问题之后与学生参加学生解决物理概念和原则。识别有很多研究识别可以解释其有效性的教程对话的特征(例如,[1]),从而可以在自然语言辅导系统中模拟这些特征。一个假设是辅导本身的高度互动性质促进了学习。几项研究表明,我们对互动性需求的理解,因为它不能仅仅通过相互作用的量来定义,而且还必须考虑到进行相互作用的程度(例如,[2])。这种改进的必要性表明,我们应该更加密切地研究教程对话中明显的语言机制。为此,我们首先确定了共同构建的话语关系中的哪些子集与学习相关联,并使用我们在RIMAC实施的一系列九个决策规则进行了我们的调查结果[3]。为了测试因果关系,我们正在进行试验试验,该试验测试为两个版本的RIMAC进行学习结果:故意在知识建设对话(KCD)框架内的九个决定规则以及无意执行的控制KCD系统中的实验版本这些规则。

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