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Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining

  • Zengzhen Shao
  • Yancong Li
  • Xiao Wang
  • Xuechen Zhao
  • Yanhui Guo
Original Research
  • 39 Downloads

Abstract

As an important knowledge visualization tool, concept map has become a research hotspot in educational data mining. Traditional concept map generation algorithms are difficult to generate concept maps quickly because of their strong reliance on experts’ experience. A hybrid TA-ARM algorithm for automatic generation of concept map based on text analysis and association rule mining is proposed. The TA-ARM algorithm fully considers the association rules between concepts, uses the text classification algorithm in text analysis technology instead of manually classify the questions into concepts, and combines the association rule mining method to generate concept maps. The experimental result shows that the TA-ARM algorithm can automatically and rapidly generate the concept map, which not only reduces the impact of outside experts, but can also dynamically adjusts the concept map based on the parameters such as the threshold of confidence between test questions. The concept map generated by the TA-ARM algorithm expresses the association rules between the concepts and the degree of closeness through the associated pairs and relevant degree, and can clearly show the structural associations between concepts. The contrast experiment shows that the quality of the concept map automatically generated by the TA-ARM has a high quality and can visualize the associations between concepts and provide optimization and guidance for knowledge visualization.

Keywords

Concept map Educational data mining Automatic generation Text analysis Text classification Association rules mining 

Notes

Acknowledgements

The authors are grateful to the anonymous reviewers for their constructive comments and invaluable contributions to enhance the presentation of this paper. This work was supported by National Natural Science Foundation of China (no. 71672154), Humanities and Social Sciences Foundation of the Ministry of Education of China (no. 16YJA630038), Joint Research Project between Southwest Jiaotong University and Cornell University (No. 268SWJTU15WTD01), China Postdoctoral Science Foundation (no. 2016M592697), Key Science and Technology Project of Shandong Province of China (no. 2014GGH201022), and Chang Jiang Scholars Program of China.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data Science and Computer ScienceShandong Women’s UniversityJinanChina
  2. 2.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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