Educational Pathways of students who enrolled in a subject-specific teacher training in Flanders: An Optimal Matching Approach

In recent years, concerns have risen regarding the Flemish teacher labour market : there is a fear of a decreasing quality of inflowing students in teacher education programmes. The focus in this paper is on the profiles and educational pathways of students who enrolled in a subject-specific teacher training programme in university colleges and universities in Flanders (i.e. a one year specialized teacher education program after having obtained a regular master's degree). The main aim of this report is to gain insight in the entire educational pathways of the subject-specific teacher training programmes.Optimal Matching Analysis (OMA) will be used to gain insight into the educational pathways of students who entered a subject-specific teacher training. OMA is a technique that only found its entrance relatively recently in the social sciences but has known an increasing number of applications in this domain (Kovalenko and Mortelmans 2011). This method makes it possible to consider trajectories from a more holistic point of view, rather than focusing on the occurrence of a single event. OMA allows to consider state sequences, which can be defined as an ordered list of states on a time axis (Gabadinho, Ritschard et al. 2011). In order to conduct this type of analysis, one has to define a cost matrix that assigns costs to every possible substitutions, insertions and deletions required to transform one sequence into another. Next, various types of algorithms can be applied to minimize the cost paths or distances between the different sequences. Once this is done, classical tools such as cluster analysis can be applied in order to create a career taxonomy of these trajectories (Abbott and Forrest 1986; Gabadinho, Ritschard et al. 2011). This typology can then be related with covariates using traditional (multinomial) logistic regression techniques (Gabadinho, Ritschard et al. 2011).Optimal Matching Analysis (OMA) allowed to identify the most representative and most frequent trajectories of entering teacher education after having obtained a master's degree.The application of additional cluster analyses led to a clear distinction of the students in two groups, more specifically a group of students who completed a professional bachelor and a group who completed an academic bachelor before enrolling in the subject-specific teacher training programme. Cluster analyses wherein more than two clusters were allowed led to different subdivision of the academic group. An attempt to use multinomial regression analyses to explain different cluster memberships made clear that different cluster membership cannot be explained by differences in characteristics such as gender, grade retention, nationality and education form in secondary education. It is however likely that students with different education pathways differ in interests, motivation and backgrounds. Further research is necessary to define the reasons certain students opt for other educational pathways than others.