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Modelling students’ knowledge organisation: Genealogical conceptual networks

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  • Koponen, Ismo T.
  • Nousiainen, Maija

Abstract

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students’ concept networks.

Suggested Citation

  • Koponen, Ismo T. & Nousiainen, Maija, 2018. "Modelling students’ knowledge organisation: Genealogical conceptual networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 405-417.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:405-417
    DOI: 10.1016/j.physa.2017.12.105
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    References listed on IDEAS

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    1. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
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