Ein Prozessmodell des Studieneinstiegs: Differentielle Aspekte studiumsbezogener Kognitionen und deren Effekte auf Studienerfolg und Studienabbruch

Education has long been recognized as an essential element in life course. Especially the decision to participate in tertiary education has an important influence on the subsequent career path (OECD, 2016). Taking this into account, successful completion (and the avoidance of dropout) appears desirable. Unfortunately, the percentage of students who drop out remains at a relatively high level of about 30% in recent years not only in general across OECD (Organisation for Economic Cooperation and Development) countries (OECD, 2016, 2018), but also in specific in Germany (Heublein et al., 2017; OECD, 2014). This is considered to be unacceptably high (Heublein et al., 2017). Although there are institutional attempts to intervene, these are usually not based on scientific models of dropout. This might be due to the fact that existing models have either a very narrow theoretical focus or are not yet empirically validated, especially not for the German student population (D. Klein & Stocke, 2016).The discrepancy between scientific insights gained in the field of higher education research on the one hand and counseling practices in higher education in Germany on the other hand calls for action. I therefore reviewed selected models of student dropout and compared them empirically in their fit todata collected from freshmen in Germany. Subsequently, I developed a new, integrative model (EOSModel) of student dropout which has been subject to a comprehensive set of validation studies. Below, I would like to outline the stepwise approach I used in my PhD project:1. I examined and compared four established models (Spady, 1971; Tinto, 1975; Neuville et al., 2007; Lent & Brown, 2013) theoretically (chapter 2) and empirically to determine their utility for the German freshmen population: I conducted three longitudinal studies (each over nine months) to track three cohorts of freshmen. Using data from the first cohort, I compared model fit of the established models.2. I then developed a new process model – the EOS-Model – with an integrative broad theoretical perspective, based on existing models and including recent meta-analytic findings (Robbins et al., 2004; Richardson, Abraham & Bond, 2012) on single predictors of academic achievement and dropout (chapter 6).3. Independent data from the second and third cohort served to validate it (chapters 7 and 8).4. Additionally, a (partial) validation study (chapter 10) was performed using cross-sectional DZHW data (Middendorff et al., 2017).The new model not only revealed a good model fit and proved its utility but also outperformed the other tested models. Furthermore, I could show its measurement invariance across the three cohorts. This allows to conclude that it could serve as a framework for future research on dropout (intentions) in tertiary education. In addition, it could serve as a framework for higher education counseling as it points out the most important aspects which are highly predictive for dropout intentions. Hence, tailoring interventions to the identified key elements may help concentrating limited counseling resources to the most promising aspects in order to maximize the impact of the interventions.Last but not least, the new model can serve as a guideline for evaluation studies for (institutional) interventions. To give an example for this application of the model, I conducted a first pilot study in cooperation with the Student Coaching at the Justus-Liebig-Universitat Giesen.Besides the development of the new model, an additional focus within my PhD project was the operationalization of the constructs under consideration. I would like to emphasize in particular the development of a new scale for the assessment of self-efficacy specifically tailored to the context of study entrance in the German higher education system (chapter 3).