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Research on high-performing schools has been increasing for years. However, using this data for effective school improvement requires a merge of preconditions, known as schools' capacity for change. For schools that lack this capacity, it is unlikely to self-generate the implementation and transfer strategies to benefit from this data.
In our study, we address this disparity by analyzing improvement difficulties in schools with variable profiles. First, we cluster schools based on multi-methods quantitative data for determining their effectiveness and improvement capacity. We found that the two clusters with average outcomes embrace the majority and differ by teachers satisfaction. Building on that, we conducted qualitative interviews in schools of each cluster, aiming to understand the particular improvement efforts and contextual interdependence.