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首页> 外文期刊>Technology, Instruction, Cognition and Learning >A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory
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A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory

机译:确定性AI建模人类教师的基础:结构学习理论的基本假设

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This paper summarizes key stages in development of the Structural Learning Theory (SLT) and explains how and why it is now possible to model human tutors in a highly efficient manner. The paper focuses on evolution of Ihe SLT, a delerministic theory of teaching and learning, on which AuthorIT authoring and Tutorl T delivery systems have been built. It explains how SLT differs fundamentally from other theories used to motivate adaptive tutor development and how AuthorIT and TutorIT technologies differ from others used in developing adaptive learning systems. Implicitly, the paper also makes clear why it has been possible using AuthorIT to develop so many TutorIT tutorials in record time at minimal cost.
机译:本文总结了结构学习理论(SLT)发展的关键阶段,并解释了现在如何以及为什么现在可以以高效的方式对人类导师进行建模。本文着眼于Ihe SLT的发展,这是一种教学的学派理论,在此基础上构建了AuthorIT创作和Tutorl T交付系统。它解释了SLT与用于激励自适应导师发展的其他理论之间的根本差异,以及AuthorIT和TutorIT技术与用于开发自适应学习系统的其他理论之间的差异。隐含地,本文还阐明了为什么使用AuthorIT可以在创纪录的时间内以最低的成本开发出如此多的TutorIT教程的可能性。

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