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An Efficient Data Mining Approach to Concept Map Generation for Adaptive Learning

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Advances in Data Mining: Applications and Theoretical Aspects (ICDM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9165))

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Abstract

Data mining has recently drawn an increasing interest as an effective approach to generation of a concept map in an adaptive learning platform that provides students with personalized learning guidance. Although it has seen significant progresses, the data mining-based concept map generation needs to be further improved both in complexity and accuracy for wide acceptance in actual education services. This paper aims to improve the accuracy of concept map by considering both wrong-to-wrong and correct-to-correct relationships of questions, and by adopting more accurate formulas in calculation of relevance degrees between concepts. Through simulations using a set of concepts, questions, and student test records sampled from a practical courseware, we show that the proposed approach can generate a more accurate and robust concept map at an acceptable additional complexity.

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Notes

  1. 1.

    The test data was generated by several students and questions in Liner Algebra & Geometry, and has been modified to make it suitable for the purpose of the paper. The difficulty level of concepts increases from C 1 to C 4 .

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Author information

Authors and Affiliations

  1. SmileK12 Inc., Freehold, NJ, USA

    Xiaopeng Huang

  2. Stevens Institute of Technology, Hoboken, NJ, USA

    Kyeong Yang & Victor B. Lawrence

Authors
  1. Xiaopeng Huang
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  2. Kyeong Yang
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  3. Victor B. Lawrence
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Corresponding author

Correspondence to Xiaopeng Huang .

Editor information

Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

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Huang, X., Yang, K., Lawrence, V.B. (2015). An Efficient Data Mining Approach to Concept Map Generation for Adaptive Learning. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-20910-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20909-8

  • Online ISBN: 978-3-319-20910-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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