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A Novel Design of the Pre-Processing Stage of Data Mining for Educational Purposes

Author

Listed:
  • Ahrens Andreas

    (Hochschule Wismar, Wismar, Germany)

  • Grünwald Norbert

    (Hochschule Wismar, Wismar, Germany)

  • Zaščerinska Jeļena

    (Centre for Education and Innovation Research,Riga, Latvia)

  • Melnikova Julija

    (Klaipeda University,Klaipeda, Lithuania)

Abstract

As data sets, in education too, change in size and structure, an appropriate design of the preprocessing stage of data mining for the implementation of data mining for educational purposes is becoming a hot research topic. The aim of the present research is to carry out interdisciplinary analysis of scientific literature on pre-processing in data mining and to design a pre-processing stage of data mining for educational purposes underpinning elaboration of a new research question. The present research employs both theoretical and empirical methods. Theoretical methods include analysis of scientific literature and theoretical modelling. The theoretical findings allow identifying sub-stages of the pre-processing stage for the implementation of data mining for educational purposes. The empirical study was carried out in 2018. The study was a case study The empirical results emphasize the main areas of analysis of teachers’ behaviour in an international project. The empirical study validates the model of the pre-processing stage of data mining for educational purposes. The practical application of the model allows drawing a conclusion that the model is valid. The novel contribution of this paper is the design of the sub-stages of the preprocessing stage for the implementation of data mining techniques for educational purposes.

Suggested Citation

  • Ahrens Andreas & Grünwald Norbert & Zaščerinska Jeļena & Melnikova Julija, 2019. "A Novel Design of the Pre-Processing Stage of Data Mining for Educational Purposes," Balkan Region Conference on Engineering and Business Education, Sciendo, vol. 3(1), pages 353-361, October.
    • Handle: RePEc:vrs:brcebe:v:3:y:2019:i:1:p:353-361:n:42
    DOI: 10.2478/cplbu-2020-0042
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