Knowledge Graph to determine the domain of learning in Higher Education

Apertura Pub Date : 2021-03-26 DOI:10.32870/AP.V13N1.1937
J. Hernández-Almazán, Juan Diego Lumbreras-Vega, Arturo Amaya Amaya, Rubén Machucho-Cadena
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Abstract

The representing of a student’s knowledge in an academic discipline plays an important role in boosting the student’s skills. To support stakeholders in the educational domain, it is necessary to provide them with robust assessment strategies that facilitate the teaching-learning process. Student´s mastery is determined by the degree of knowledge, which demonstrates objectively, on the topics included in the different areas that make up an academic discipline. Although there is a wide variety of techniques to represent knowledge, particularly Knowledge Graph technique is becoming relevant due to the structured approach and benefits it offers. This paper proposes a method that classifies and weights the nodes (topics) of a Knowledge Graph of a disciplinary area, which is analyzed through a case study. The method has two approaches: avoid exhaustive evaluation of the nodes and weight the nodes with adequate precision. Method´s application is illustrated by a case study. As results, a Knowledge Graph is obtained with its classified and weighted nodes through the application of the proposed method, in which 100% of the topics have been impacted through the objective evaluation of 20.8% representing 10 nodes. It is concluded that the proposed method has potential to be used in the representation and management of knowledge, being necessary to improve phases’ iteration to condition number of objective nodes.
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利用知识图谱来确定高等教育中的学习领域
学生在一门学科中的知识表现对提高学生的技能起着重要的作用。为了支持教育领域的利益相关者,有必要为他们提供有力的评估策略,以促进教学过程。学生的掌握程度是由知识的程度决定的,客观地展示了构成一门学科的不同领域所包含的主题。尽管有各种各样的技术来表示知识,特别是知识图技术,由于它的结构化方法和它提供的好处而变得越来越重要。本文提出了一种学科领域知识图谱中节点(主题)的分类和加权方法,并通过实例进行了分析。该方法有两种方法:避免对节点进行穷举评估和以足够的精度对节点进行加权。通过实例分析说明了该方法的应用。应用本文提出的方法得到了一个具有分类和加权节点的知识图谱,通过20.8%代表10个节点的客观评价,100%的主题受到了影响。结果表明,该方法在知识的表示和管理中具有一定的应用潜力,可以改善阶段的迭代,以约束目标节点的数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.70
自引率
0.00%
发文量
20
审稿时长
12 weeks
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