一个远程学习框架的自动讲师答复:清晰的隐性知识用于反馈的要求

M. Richards, J. Schiffel
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引用次数: 1

摘要

远程学习有很多方面,从技术实现到评估方法。在过去的十年里,促进虚拟教室和协作的工具越来越多。然而,在线课程的反馈和评估只是部分自动化。本文主要遵循知识管理理论和人工智能技术,开发了一个框架来捕获和管理对学生回复的自动回复。教师的隐性知识在增加课堂参与、学习社区和反馈评价方面起着直接的作用。概念图是从教师的书面回答中提取隐性知识,并帮助将其外部化以供将来重用。通过一个问答任务来说明心理模型与概念图之间的关系,以及通过关键词匹配选择答案的机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A distance learning framework for automatic instructor replies: articulable tacit knowledge used for feedback upon request
Distance learning has many facets, ranging from technology implementations to assessment methods. The last decade has seen an increased number of tools to facilitate virtual classrooms and collaboration. However, feedback and evaluation are only partially automated in online courses. This paper largely follows knowledge management theories and artificial intelligent techniques, developing a framework to capture and manage automated responses to student replies. The instructor's tacit knowledge plays a direct role in augmenting class participation, learning communities, and feedback evaluation. Conceptual graphs are proposed to extract tacit knowledge from instructors written responses and to assist in externalizing it for future re-use. A question answering task is presented to illustrate the relationship between mental models and conceptual graphs and the mechanism to select responses through keyword match.
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