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Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks

机译:自动图形表示法指导阅读:基于模型的文本可视化如何促进阅读理解任务中的学习

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Our study integrates automated natural language-oriented assessment and analysis methodologies into feasible reading comprehension tasks. With the newly developed T-MITOCAR toolset, prose text can be automatically converted into an association net which has similarities to a concept map. The “text to graph” feature of the software is based on several parsing heuristics and can be used both to assess the learner’s understanding by generating graphical information from his or her text and to generate conceptual graphs from text which can be used as learning materials. In this study we investigate the effects of association nets made available to learners prior to reading. The results reveal that the automatically created graphs are highly similar to classical expert graphs. However, neither the association nets nor the expert graphs had a significant effect on learning, although the latter have been reported to have an effect in previous studies.
机译:我们的研究将面向自然语言的自动评估和分析方法集成到了可行的阅读理解任务中。使用新开发的T-MITOCAR工具集,可以将散文自动转换为与概念图相似的关联网络。该软件的“文本到图形”功能基于几种解析启发法,既可以用于通过从其文本生成图形信息来评估学习者的理解,又可以用于从可用作文本的学习材料中生成概念图。在这项研究中,我们调查了在阅读之前提供给学习者的联想网络的影响。结果表明,自动创建的图与经典专家图高度相似。然而,尽管在以前的研究中已经报道后者有影响,但是协会网络和专家图都没有对学习产生重大影响。

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