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Extracting learning concepts from educational texts in intelligent tutoring systems automatically

机译：在智能补习系统中自动从教育文本中提取学习概念

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This paper argues that the educational support systems can give a meaning to an educational content semantically, and an answer has been sought to the question of "what should be taught to students" in the field of intelligent tutoring systems. With reference this aim, a system, which automatically detects the concepts to be learned by students, has been designed. The developed system uses the statistical language models together with conceptual map modeling as a student model to extract the minimal set of learning concepts within an educational content.rnIn the study, ten corpora have been generated as a learning domain, which consist of two different subjects in mathematics. For each subject, five distinct chapters have been quoted from the books written by various authors. After extracting the candidate concepts from the given content, the system checks to clarify whether these candidates are in a dictionary within postprocessing. The dictionary consists of approximately 9500 technical terms related to the learning domain. The system performance has also been analyzed using Recall, Precision and F-measure scores. The results indicate that the postprocessing step increases precision with a small loss of recall.
机译：本文认为，教育支持系统可以在语义上赋予教育内容某种意义，并且已经在智能补习系统领域中寻求“应向学生教什么”的问题的答案。出于这个目的，已经设计了一种系统，该系统可以自动检测学生要学习的概念。开发的系统使用统计语言模型和概念图模型作为学生模型，以提取教育内容内的最小学习概念集。在研究中，已生成十个语料库作为学习域，其中包括两个不同的主题在数学上。对于每个主题，从不同作者撰写的书中引用了五个不同的章节。从给定的内容中提取候选概念之后，系统会检查以弄清楚这些候选对象是否在后处理中的字典中。该词典包含大约9500个与学习领域相关的技术术语。还使用召回率，精度和F量度得分对系统性能进行了分析。结果表明，后处理步骤提高了精度，而召回损失很小。

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