Dr. Tonya Moon
Professor of Education
Curry School of Education
University of Virginia, United States
Tonya R. Moon, Ph.D., is Professor of Education at the University of Virginia School of Education and Human Development in the Department of Curriculum, Instruction, and Special Education, and Co-Director of the University of Virginia Institutes on Academic Diversity. Her research and teaching interests include the use of student data for supporting students’ academic needs. She works with educators nationally and internationally in the area of assessment to better address the academic diversity of today’s classrooms through differentiated instruction. She and Carol A. Tomlinson are co-authors on the book Assessment in a Differentiated Classroom: A Guide for Students Success ASCD). Moon, Brighton, and Tomlinson are also co-authors on a recently released book, Using Differentiated Classroom Assessment to Enhance Student Learning (Routledge).
Mapping the Route to Success: Using Data to Inform Students’ Academic Pathways.
Students learn differently and may require different paths in order to reach their highest potential. Using the analogy of a journey, this keynote will explore how teachers can plan the best routes for students’ learning, use “GPS” to reroute along the way as needed, and to capture the best “souvenirs” to evidence their learning journeys. Building on the principles of differentiation and data use, this keynote will offer practical strategies and tools for teachers to use in creating and sustaining differentiated classrooms. Participants will leave with a clear rationale for why student data must take center stage in planning for differentiation. Participants will understand data’s role in strategic instructional decision making.
Let the Circle be Unbroken: Linking Assessments and Instructional Decision-making for Differentiation
The theory of action underlying data-driven differentiation begins with establishing clear learning targets, collecting evidence about students’ progress toward those targets, and planning differentiated learning opportunities in response to what the evidence suggests that students need. Otherwise known as the Assessment Cycle, this process requires that teachers intentionally collect information about students’ progress toward intended learning goals, systematically analyze students’ work, and make instructional plans that support and reinforce students’ strengths while also addressing their areas of needs. In this session, participants will learn how to use a decision tree to support data-driven differentiation at each step within the Assessment Cycle. In addition, participants will examine practical management considerations for making this process both effective for students as well as efficient for teachers.
Learning Objectives: Participants will
the assessment cycle and its relationship to instruction
the data-driven instructional decision cycle
that data informed instruction is a pro-active process for continuous student learning
Be Able To:
use a decision tree to identify appropriate differentiated pathways for students.