Value-added (VA) modelling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g. Braun, 2005); in other words, VA strives to model the added value of teaching. The conceptual idea is to identify the amount of “value” that has been “added” by teachers, schools, or other pedagogical interlocutors, to the evolution of students’ performance. VA is typically used for teacher and/or school accountability (e.g. Sanders, 2000). Although VA models have gained popularity in recent years—a substantial increase of publications is to be observed over the last decade—, there is no consensus on how to calculate VA, nor is there a consensus whether and which covariates should be included in the statistical models (e.g. Newton, Darling-Hammond, Haertel, & Thomas, 2010).
Especially in diverse settings, leveling out the influence of background factors is of interest. The Luxembourgish school setting is a useful example, as it is very heterogeneous and multilingual: 63% of the children in primary school and 52% of the students in secondary school do not speak Luxembourgish at home (Ministry of National Education, Children and Youth, 2017). Additionally, the instruction language in the Luxembourgish curriculum switches from Luxembourgish in preschool to German in primary school to French for some topics in secondary school. This represents a challenge for teachers, schools and pupils. One aim of such a diverse school setting should be to help students improve regardless of their background. Therefore, it is interesting to calculate and compare VA models in the Luxembourgish school setting.
This contribution has two purposes: In the first part, results from an exhaustive literature review will be presented. The objective of this literature review is to analyse the current status of the use of VA models in practice and research and to find similarities and differences. Concretely, the research questions are: (1) Which statistical models have been used? (2) Which variables have been included and which statistical adjustments (e.g. for measurement error or missing data) have been made? (3) Which statistical parameters (e.g. explained variance) have been reported?
In the second part of our contribution, we will present results from a quantitative analysis of VA models. It consists of calculations and comparisons between different VA models, using largescale longitudinal data from the Luxembourg School Monitoring Programme “Épreuves Standardisées” (ÉpStan). In these calculations, we will consider the results of the literature review and empirically investigate the different model types we found. The objective is on the one hand to compare the quality of different statistical models and on the other hand to apply them in a multilingual and diverse setting.