Passionate and proactive: the role of the secondary principal in leading curriculum change.

It is widely recognised that the leadership of school principals is a crucial factor in school-based curriculum change. With the recent introduction of a new national curriculum in New Zealand, schools will need to develop strategies to incorporate this new curriculum into their programmes. This paper outlines evidence from international literature about how the leadership of principals is linked to change. It also examines evidence from case studies of early adopter schools. A major finding is that there appear to be common factors at work across effective secondary school principals, in particular an enthusiasm for proactive leadership of changes in school culture involving fundamental shifts in thinking and behaviour

How career variety promotes the adaptability of managers: a theoretical model

This paper presents a theoretical model showing how managerial adaptability develops from career variety over the span of the person’s career. By building on the literature of career theory, adult learning and development, and career adjustment, we oVer a new conceptualization of managerial adaptability by identifying its behavioral, cognitive, and socio-emotional dimensions, discuss how these competencies can develop from the variety of managers’ cumulative career experiences, and propose several individual and career-related factors that moderates the relationship between managerial career variety and adaptability

Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set