Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.
In order for a professional learning community to achieve its full potential, all levels districtwide must align with the three big ideas: ensuring a focus on learning, building a collaborative culture, and establishing a results orientation. This book breaks down the complex process of aligning the work of central office staff, building leadership, and teachers to increase student achievement.
How to Extend and Personalize Student Learning in a PLC at Work® (Support and Engage Proficient Learners in a Professional Learning Community) (Personalized Learning)
How to Extend and Personalize Student Learning in a PLC at Work® (Support and Engage Proficient Learners in a Professional Learning Community) (Personalized Learning)
This practical guide is designed to help collaborative teams at all grade levels address the critical question "How will we extend the learning for students who are already proficient?" Mark Weichel, Blane McCann, and Tami Williams identify five elements of personalized learning, along with five instructional strategies for extended, differentiated instruction, that give all students the opportunity to reach their personal best. Rethink how to respond to proficient students in a competency-based curriculum: Realize the importance of addressing the fourth critical question of Professional Learning Communities at WorkTM. Learn the five elements of personalized learning: knowing your learners, allowing student voice and choice, implementing flexibility, using data, and integrating technology. Explore five differentiated instruction strategies for extending the learning for high-ability and high-potential students: curriculum compacting, flexible grouping, product choices, tiered assignments, and multilevel learning stations. Understand how collaborative teams in a professional learning community (PLC) can maximize student engagement, motivating students to learn beyond the essential standards. Utilize individual and collaborative team reflection tools, and read stories based on real-life teachers' experiences implementing the elements of personalized learning in classrooms. Contents: Introduction Chapter 1: Reframing Chapter 2: Personalized Learning Chapter 3: Instructional Strategies That Support Question 4 Students Chapter 4: Knowing Your Learners Chapter 5: Allowing Voice and Choice Chapter 6: Implementing Flexibility Chapter 7: Using Data Chapter 8: Integrating Technology Chapter 9: Bringing It All Together
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