Teacher’s Conceptions of Caring and Protective Teaching Elements in Rwamwanja Refugee Settlement Schools

The purpose of this study is to find out what conceptions Rwamwanja refugee settlement teachers have about caring teaching methods. The study was conducted by analysing the data gathered from semi-structured interviews. Twelve teachers were interviewed in four different refugee settlement schools. The main theory of this study is based on ethics of care research by Nel Noddings. In addition, the framework was developed by combining the theories of resilience and psychosocial support which are often employed in research concerning emergency contexts. This study uses qualitative content analysis to describe the conceptions of caring teachers have and protective teaching elements they employ. The results of this study show that many of the key elements of caring and protective teaching were present in teacher’s answers. For example, in their answers, the majority of the teachers pointed out the significance of using soft discipline. However, many teaching elements considered ideal in emergency contexts were missing. These missing methods include routines and flexibility which are considered essential for vulnerable children. The teachers’ levels of conceptual thinking varied remarkably depending on their language skills. The communication was limited to very basic and concrete language in some of the interviews due to lack of mutual understanding. This also raised a question about the level of understanding between refugee pupils and teachers since there is no strong common language between them. The results of this research call for further studies about the effect of caring teaching elements in growth of resilience in refugee children. Keywords: The ethics of care, resilience, psychosocial support, education in emergencies, refugees, education, protection.

Structure learning of context-specific graphical models

Abstract The ultimate problem considered in this thesis is modeling a high-dimensional joint distribution over a set of discrete variables. For this purpose, we consider classes of context-specific graphical models and the main emphasis is on learning the structure of such models from data. Traditional graphical models compactly represent a joint distribution through a factorization justi ed by statements of conditional independence which are encoded by a graph structure. Context-speci c independence is a natural generalization of conditional independence that only holds in a certain context, speci ed by the conditioning variables. We introduce context-speci c generalizations of both Bayesian networks and Markov networks by including statements of context-specific independence which can be encoded as a part of the model structures. For the purpose of learning context-speci c model structures from data, we derive score functions, based on results from Bayesian statistics, by which the plausibility of a structure is assessed. To identify high-scoring structures, we construct stochastic and deterministic search algorithms designed to exploit the structural decomposition of our score functions. Numerical experiments on synthetic and real-world data show that the increased exibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.