The paper which is being presented explores the impact of self-directed learning on student’s engagement. This is highly relevant in the context of dropout prevention, as school dropout can be viewed as the final stage of a long-term multi-dimensional process of disengagement from school (Nairz-Wirth et al., 2014). The paper draws upon findings from a longitudinal study (2016-2018) which was conducted at an upper secondary school in Austria. In the course of a quasi-experimental research design, we tried to explore whether self-directed learning has any impact on students’ engagement and dropout-rates. Two different groups of students were compared: One group participated in an innovative learning environment (which includes self-directed learning practices), and one group participated in a traditional learning environment. We combined quantitative and qualitative methods (mixed methods approach) (Johnson et al. 2007). The empirical data consisted of a quantitative survey (127 students) and qualitative group discussions with 21 students. To analyse the quantitative data, a complex econometric model (mixed-effects-model) was applied (Zuur et al. 2009). The qualitative data was analysed by using open and theory-based coding (Saldaña 2016). The results indicate that students who participated in the innovative learning environment – where they experienced self-directed learning – showed a significantly higher engagement and lower dropout-rates than the peer group. This illustrates that self-directed learning can reduce the risk of dropping out. However, there is also evidence in the qualitative data that some students show a negative form of “over-engagement”, which can cause mental and physical health problems. Therefore, we stress that also potentially negative effects of self-directed learning must be taken into account and students should be supported adequately in coping with a high level of autonomous learning, so that they are able to develop healthy learning habits.

Johnson, R. et al. (2007). Toward a Definition of Mixed Methods Research. Journal of Mixed Methods Research, 1, 112–133. Nairz-Wirth, E. et al. (2014). Quo Vadis Bildung? Eine qualitative Längsschnittstudie zum Habitus von Early School Leavers. Vienna: Education Sciences Group, Vienna University of Economics and Business. Retrieved from http://www.wu.ac.at/bildungswissenschaft/aktuelles/abgforschungsprojekte/qv. Saldaña, J. (2016). The Coding Manual for Qualitative Researchers. Los Angeles: SAGE Publications Ltd. Zuur, A. F. et al. (2009). Mixed Effects Models and Extensions in Ecology with R. 1st edition. New York: Springer.