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Political Science in Europe

Effects of Digital Simulation Games on Students' Political Motivations and Attitudes Regarding the European Union

Political Participation
European Parliament
Sven Ivens
Georg-August-Universität Göttingen
Sven Ivens
Georg-August-Universität Göttingen
Monika Oberle
Georg-August-Universität Göttingen

Political simulation games are well established as an approach of civic education. Their potential for political learning has been much discussed (Usherwood 2014; Petrik & Rappenglück 2017). Recently, empirical studies have demonstrated that simulation games have positive effects on students` political motivation, attitudes and knowledge (Oberle & Leunig 2017; Oberle et al. 2018; Bursens & Jones 2015, 2018).
However, there is much less literature on digital simulation games as a tool for teaching politics. Generally, it has been argued that digital simulation games have the potential to create a constructive learning environment (Charsky 2010) and could support a long-lasting learning effect (Wouters 2013). Other scholars doubt that there are positive effects of using digital simulation games for education (Spies 1976, Ohler & Nieding 2000). There is a lack of empirical studies on the effects of digital simulation games as a tool for political science teaching (Motyka 2017; Oberle et al. 2017).
This paper presents a pilot study about the effects of short digital EU simulation games on secondary school students` political motivations (political interest, self-efficacy, political participation) and attitudes (towards the EU, pluralism, European identity, empathy). For the study, approximately N=250 students from 10 secondary school classes in Lower Saxony are taking part in a digital European Parliament simulation game (each 90 minutes, designed by planpolitik). Data are collected by means of a pen-and-paper questionnaire at three points of measurement (pre, post, follow-up test). Political motivations and attitudes are captured by item batteries with a four-point Likert scale, measurement and structural equation models are calculated latently in Mplus 7.4. Background variables are sex, age, migration background and computer resp. gaming affinity. Most constructs used in the study have already been validated in other studies, e.g. ICILS 2013, ICCS 2001, Oberle & Leunig 2016 and Maes et al. 1995.
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