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DataMining for Small Student Data Set: Knowledge Management System for Higher Education Teachers

Listed author(s):
  • Srecko Natek

    (International School for Social and Business Studies, Slovenia)

  • Moti Zwilling

    (College of Law and Business, Israel)

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    Higher education teachers are often curious whether students will be successful or not. Before or during a course they try to estimate the percentage of successful students. But is it possible to predict the success rate of students enrolled in their course? Are there any specific student characteristics, known to a teacher, which can be associated with the student success rate? Is there any relevant student data available to teachers on the basis of which they could predict the student success rate? The answers to the above research questions can generally be obtained with data mining tools. Unfortunately, data mining algorithms work best with large data sets, while student data, available to higher education teachers, is extremely limited and clearly falls into the category of small data sets. Thus, the study focuses on data mining for small student data sets and aims to answer the above research questions using data and comparative analysis using data mining tools normally available to higher education teachers. The conclusions of this study are very promising and will encourage teachers to incorporate data mining tools as an important part of their higher education knowledge management systems.

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    This chapter was published in: Srecko Natek & Moti Zwilling , , pages 1379-1389, 2013.
    This item is provided by ToKnowPress in its series Active Citizenship by Knowledge Management & Innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013 with number 1379-1389.
    Handle: RePEc:tkp:mklp13:1379-1389
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