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Automatic annotation method on learners' opinions in case method discussion

机译：案例方法讨论中对学习者观点的自动注释方法

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Purpose - The purpose of this paper is to annotate an attribute of a problem, a solution or no annotation on learners' opinions automatically for supporting the learners' discussion without a facilitator. The case method aims at discussing problems and solutions in a target case. However, the learners miss discussing some of problems and solutions. Design/methodology/approach - Because opinions about problems and solutions on the same case are similar to each other, the proposed method uses opinions that are correctly annotated in past discussions for annotating an appropriate attribute on each opinion in discussions of the same case. The annotation on each opinion is identified by Support Vector Machine learned with opinions and annotations in the past discussion. Findings - Compared to a simple method that uses decision tree classification, this proposed method improves the recall rate and the precision rate of annotating the attribute by over 10 per cent. The proposed method is effective for automatic annotation. Originality/value - Because the recall rate and the precision rate of annotating an attribute of a problem are over 80 per cent, it is possible to make learners aware of problems that they should discuss. On the other hand, the recall rate and the precision rate of annotating an attribute of a solution are still low. The authors discuss the research issue to improve the rates for automatic annotation.
机译：目的-本文的目的是自动注释问题的属性，解决方案或不注释学习者的意见，以支持学习者的讨论，而无需提供帮助。案例方法旨在讨论目标案例中的问题和解决方案。但是，学习者错过了讨论一些问题和解决方案的机会。设计/方法/方法-由于对同一案例的问题和解决方案的意见彼此相似，因此所提出的方法使用在先前讨论中正确注释的意见，以便在对同一案例的讨论中为每种意见添加适当的属性。支持向量机（Support Vector Machine）通过在过去的讨论中学习到的意见和注释来识别每个意见的注释。结果-与使用决策树分类的简单方法相比，该方法可将召回率和标注属性的准确率提高10％以上。所提出的方法对于自动标注是有效的。原创性/价值-因为回想率和注释问题属性的准确率超过80％，因此可以使学习者意识到他们应该讨论的问题。另一方面，标注解的属性的查全率和准确率仍然较低。作者讨论了研究问题，以提高自动标注的速度。

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