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- All Subjects: data-driven
- Creators: Cui, Can
- Creators: Watanabe Kganetso, Lynne
Description
The purpose of this action research was to understand how reflective, job-embedded early childhood science professional learning and development (PLD) impacted Early Head Start (EHS) teacher learning and their perceptions toward science with toddlers. Limited content knowledge and lack of formal preparation impact teachers’ understanding of developmentally appropriate science and their capacity to support children to develop science skills. In Arizona, limited availability of early childhood science coursework and no science-related PLD for toddler teachers showed the need for this project. Four literature themes were reviewed: teacher as researcher, how people learn, reflective PLD, and how young children develop scientific thinking skills.
The participants were nine EHS teachers who worked at the same Head Start program in five different classrooms in Arizona. The innovation included early childhood science workshops, collaboration and reflecting meetings (CPRM), and electronic correspondence. These were job-embedded, meaning they related to the teachers’ day-to-day work with toddlers. Qualitative data were collected through CPRM transcripts, pre/post-project interviews, and researcher journal entries. Data were analyzed using constant comparative method and grounded theory through open, focused, and selective coding.
Results showed that teachers learned about their pedagogy and the capacities of toddlers in their classrooms. Through reflective PLD meetings, teachers developed an understanding of toddlers’ abilities to engage with science. Teachers acquired and implemented teacher research skills and utilized the study of documentation to better understand children’s interests and abilities. They recognized the role of the teacher to provide open-ended materials and time. Moreover, teachers improved their comfort with science and enhanced their observational skills. The teachers then saw their role in supporting science as more active. The researcher concluded that the project helped address the problem of practice. Future research should consider job-embedded PLD as an important approach to supporting data-driven instructional practices and reflection about children’s capabilities and competencies.
Keywords: action research, Arizona Early Childhood Workforce Knowledge and Competencies, Arizona’s Infant and Toddler Developmental Guidelines (ITDG), documentation, early childhood science, Early Head Start (EHS), Head Start Early Learning Outcomes Framework (ELOF), inquiry, job-embedded, pedagogy, professional development (PD), reflective professional development, teacher as researcher, teacher research, toddler science
The participants were nine EHS teachers who worked at the same Head Start program in five different classrooms in Arizona. The innovation included early childhood science workshops, collaboration and reflecting meetings (CPRM), and electronic correspondence. These were job-embedded, meaning they related to the teachers’ day-to-day work with toddlers. Qualitative data were collected through CPRM transcripts, pre/post-project interviews, and researcher journal entries. Data were analyzed using constant comparative method and grounded theory through open, focused, and selective coding.
Results showed that teachers learned about their pedagogy and the capacities of toddlers in their classrooms. Through reflective PLD meetings, teachers developed an understanding of toddlers’ abilities to engage with science. Teachers acquired and implemented teacher research skills and utilized the study of documentation to better understand children’s interests and abilities. They recognized the role of the teacher to provide open-ended materials and time. Moreover, teachers improved their comfort with science and enhanced their observational skills. The teachers then saw their role in supporting science as more active. The researcher concluded that the project helped address the problem of practice. Future research should consider job-embedded PLD as an important approach to supporting data-driven instructional practices and reflection about children’s capabilities and competencies.
Keywords: action research, Arizona Early Childhood Workforce Knowledge and Competencies, Arizona’s Infant and Toddler Developmental Guidelines (ITDG), documentation, early childhood science, Early Head Start (EHS), Head Start Early Learning Outcomes Framework (ELOF), inquiry, job-embedded, pedagogy, professional development (PD), reflective professional development, teacher as researcher, teacher research, toddler science
ContributorsBucher, Eric Zachary (Author) / Marsh, Josephine (Thesis advisor) / Martin, Laura (Committee member) / Watanabe Kganetso, Lynne (Committee member) / Arizona State University (Publisher)
Created2019
Description
Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems.
ContributorsCui, Can (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery D. (Thesis advisor) / Li, Jing (Committee member) / Fowler, John (Committee member) / Hu, Mengqi (Committee member) / Arizona State University (Publisher)
Created2016