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Scenario Evaluation Using Two-mode Clustering Approaches in Higher Education

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Data Analysis, Machine Learning and Applications

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Abstract

Scenario techniques have become popular tools for dealing with possible futures. Driving forces of the development (the so-called key factors) and their possible projections into the future are determined. After a reduction of the possible combinations of projections to a set of consistent and probable candidates for possible futures, traditionally one-mode cluster analysis is used for grouping them. In this paper, two-mode clustering approaches are proposed for this purpose and tested in an application for the future of eLearning in higher education. In this application area, scenario techniques are a very young and promising methodology.

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Author information

Authors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, Postbox 101344, 03013, Cottbus, Germany

    Matthias J. Kaiser & Daniel Baier

Authors
  1. Matthias J. Kaiser
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  2. Daniel Baier
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Institute of Business Economics and Information Systems, University of Hildesheim, Marienburgerplatz 22, 31141, Hildesheim, Germany

    Christine Preisach

  2. Lehrstuhl für Mustererkennung und Bildverarbeitung, Universität Freiburg, Gebäude 052, 79110, Freiburg i. Br, Germany

    Hans Burkhardt

  3. Institute of Computer Science and Institute of Business Economics and Information Systems, Marienburgerplatz 22, 31141, Hildesheim, Germany

    Lars Schmidt-Thieme

  4. Fakultät für Wirtschaftswissenschaften, Lehrstuhl für Betriebswirtschaftslehre, insbes. Marketing, Universitätsstraße 25, 33615, Bielefeld, Germany

    Reinhold Decker

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Kaiser, M.J., Baier, D. (2008). Scenario Evaluation Using Two-mode Clustering Approaches in Higher Education. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_78

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