The aim of this meta-analysis was to analyze the experimental research into the effects of job-embedded professional development (JEPD) for teachers and student outcomes. Our meta-analysis of experimental studies of the effects JEPD, included 20 studies (with 79 experimental comparisons) at teacher level and 19 studies at student level (with 34 experimental comparisons). Analyses of the studies, representing 2,062 teachers and 21,425 students, revealed a significant, medium-to-large effect size at teacher level (ES= 0.699, SE= 0.092) and a significant medium effect at student level (ES = 0.523, SE= 0.137). Effects for teachers were smaller in studies with a large sample size. Effects for students were positively related to the length of the intervention. The positive outcomes at teacher and student level support the implementation and expansion of JEPD programsacross schools.
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Computational thinking (CT) has become a necessity in many professional domains. As such, scholars argue that the acquisition of CT and application should be embedded in existing school subjects. Within the CT literature, a tax-onomy distinguishes CT practices in STEM education into four categories: data related, systems thinking, modeling & simulation and computational problem solving (CPSP). Practical applications of these different categories are still limited. This paper presents three examples in which edu-cators of science teachers integrate CT within STEM con-tent knowledge using the above mentioned taxonomy. The first example applies to CPSP and data practices, the sec-ond to CPSP exclusively, the final to systems thinking and modeling & simulation. The examples provide practical insight that makes the use of CT in STEM education more tangible for practitioners.
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
