KnowEdu: A System to Construct Knowledge Graph for Education

被引:226
作者
Chen, Penghe [1 ]
Lu, Yu [2 ]
Zheng, Vincent W. [3 ]
Chen, Xiyang [1 ]
Yang, Boda [1 ]
机构
[1] Beijing Normal Univ, Adv Innovat Ctr Future Educ, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Fac Educ, Sch Educ Technol, Beijing 100875, Peoples R China
[3] Adv Digital Sci Ctr, Singapore 138602, Singapore
基金
中国国家自然科学基金;
关键词
Educational knowledge graph; instructional concept; educational relation; pedagogical data; learning assessment; educational data mining;
D O I
10.1109/ACCESS.2018.2839607
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
Motivated by the vast applications of knowledge graph and the increasing demand in education domain, we propose a system, called KnowEdu, to automatically construct knowledge graph for education. By leveraging on heterogeneous data (e.g., pedagogical data and learning assessment data) from the education domain, this system first extracts the concepts of subjects or courses and then identifies the educational relations between the concepts. More specifically, it adopts the neural sequence labeling algorithm on pedagogical data to extract instructional concepts and employs probabilistic association rule mining on learning assessment data to identify the relations with educational significance. We detail all the abovementioned efforts through an exemplary case of constructing a demonstrative knowledge graph for mathematics, where the instructional concepts and their prerequisite relations are derived from curriculum standards and concept-based performance data of students. Evaluation results show that the F1 score for concept extraction exceeds 0.70, and for relation identification, the area under the curve and mean average precision achieve 0.95 and 0.87, respectively.
引用
收藏
页码:31553 / 31563
页数:11
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