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Higher education (HE) is engaged in a variety of educational innovations, as well as professional development initiatives (PDIs) to support teachers in attaining the required expertise. To improve teacher professional learning and development (PLD) and innovation processes, it is important to understand whether, how and why different PLD practices work for different innovations, contexts and populations. However, research is characterized by descriptive, single case studies and lacks a common framework to relate research findings. To address this shortcoming, this study collected and compared a wide variety of cases to develop a typology of practices. The results showed that educational innovations and teacher PLD were typically configured in three ways: (1) the focus is on implementing a new form of education and teacher learning is used as a means to this end, (2) the focus is on teachers’ professional learning and the educational innovations are spin-offs, and (3) the focus is on stimulating innovations and teacher learning is a side-effect. These types of configurations differed regarding the educational innovation, required teacher expertise, professional development initiatives, teacher learning, and outcome measures. The typology serves as a framework that may help to reflect on practices, bridge disciplines, and formulate hypotheses for future research.
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The understanding of charging behavior has been recognized as a crucial element in optimizing roll out of charging infrastructure. While current literature provides charging choices and categorizations of charging behavior, these seem oversimplified and limitedly based on charging data. In this research we provide a typology of charging behavior and electric vehicle user types based on 4.9 million charging transactions from January 2017 until March 2019 and 27,000 users on 7079 Charging Points the public level 2 charging infrastructure of 4 largest cities and metropolitan areas of the Netherlands. We overcome predefined stereotypical expectations of user behavior by using a bottom-up data driven two-step clustering approach that first clusters charging sessions and thereafter portfolios of charging sessions per user. From the first clustering (Gaussian Mixture) 13 distinct charging session types were found; 7 types of daytime charging sessions (4 short, 3 medium duration) and 6 types of overnight charging sessions. The second clustering (Partition Around Medoids) clustering result in 9 user types based on their distinct portfolio of charging session types. We found (i) 3 daytime office hours charging user types (ii) 3 overnight user types and (iii) 3 non-typical user types (mixed day and overnight chargers, visitors and car sharing). Three user types show significant peaks at larger battery sizes which affects the time between sessions. Results show that none of the user types display solely stereotypical behavior as the range of behaviors is more varied and more subtle. Analysis of population composition over time revealed that large battery users increase over time in the population. From this we expect that shifts charging portfolios will be observed in future, while the types of charging remain stable.
How can European migration, between countries and within countries between regions, contribute to the development of vulnerable regions in Europe? This is the central question of project Premium_EU (Policy REcommendations to Maximise the beneficial Impact of Unexplored Mobilities in and beyond the European Union), which is financed by Horizon Europe.The key goal of Premium_EU is the development of a Regional Policy Dashboard for national and regional policy makers to help them in the formulation of new policies aimed at the potential of migration to enhance the development of vulnerable regions. The Dashboard combines all available knowledge of three domains in three modules: the Mobility Module, the Regional Development Effects Module, and the Policy Module.The Mobility Module includes both past trends and projections and scenarios, in addition to new mobility estimates based on data from social media usage, such as LinkedIn and Facebook. The module also includes qualitative information from case studies on specific types of mobility groups, such as Polish seasonal workers, or Turkish migrants to EU countries. These trends, projections and case studies will be summarized in a regional typology on the basis of the mobility profile of the region.In the Regional Development Effects Module all available data on regional development is summarized in a regional development typology, where regional development is interpreted much broader than economic development. Using causal models the role of regional mobility in regional development will be established.In the Policy Module all possible forms of regional policies will be collected and linked to the mobility- and regional development characteristics of the region.The Dashboard integrates these modules so that a policy maker, on the basis of the unique mobility and regional development profile of his or her region is able to make an evidence based choice out of a relevant set of policy options. Users of the Dashboard will also be able to add their experiences to the Dashboard, so that other users can benefit from their knowledge.
