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Experimentelle Evidenz zur Wirkung der Teilnahme an E-Learning-Veranstaltungen auf den Klausurerfolg

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  • Decker, Philipp
  • Pierdzioch, Christian
  • Stadtmann, Georg

Abstract

In diesem Beitrag wird analysiert, wie sich die Teilnahme an einer Lehrveranstaltung aus dem Bereich des E-Learning auf das Klausurergebnis auswirkt. Der Leistungsunterschied zwischen Teilnehmern und Nichtteilnehmern lasst sich nicht allein auf die Partizipation an der Veranstaltung zuruckfuhren, sondern gibt als Average Treatment Effect (ATE) die durchschnittliche Performance der Lehrveranstaltung hinsichtlich des studentischen Lernerfolgs an. Zur Kontrolle der Partizipationsneigung wurden die Teilnehmer im Rahmen eines Experiments in zwei Gruppen eingeteilt, denen teilweise unterschiedliche Lehrinhalte vermittelt wurden. Durch den Vergleich der beiden Teilnehmergruppen untereinander konnte der Average Effect of the Treatment on the Treated (ATT) ermittelt werden. Es zeigte sich, dass der Unterschied zwischen den Teilnehmern und den Verweigerern starker ausgeprägt ist, als zwischen den beiden Teilnehmergruppen des Experiments (ATE > ATT).

Suggested Citation

  • Decker, Philipp & Pierdzioch, Christian & Stadtmann, Georg, 2011. "Experimentelle Evidenz zur Wirkung der Teilnahme an E-Learning-Veranstaltungen auf den Klausurerfolg," Discussion Papers 306, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
  • Handle: RePEc:zbw:euvwdp:306
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    References listed on IDEAS

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    More about this item

    Keywords

    Klausurerfolg; Veranstaltungsteilnahme; E-Learning;
    All these keywords.

    JEL classification:

    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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