What is academic rigor?
In this series, you’ll begin a conversation about what rigorous content and student conversation look like and you’ll develop common language and images for finding evidence of deep and substantive learning. You’ll learn and practice strategies for creating a professional culture where educators can discuss practice. Through activities, videos, modeling, readings, and dialogue, you’ll begin to explore:
- How do you plan rigorous lessons that will have entry for all, and a high ceiling for those students who need challenge?
- What is evidence of deep, substantive learning?
- What common instructional moves can we make across subject areas that help kids get smart?
- How can we support effective student behaviors across content?
- How can we identify what our students know and don’t know, and address gaps in student content knowledge?
Everyone has a role in academic rigor!
In this program, we explore how different instructional leadership roles support student rigor and the school team learns how to support a shared vision of success.
Design and implement effective instruction; strengthen teacher’s content and pedagogical knowledge. Considers how to be responsive to teacher needs.
Develop an eye for effective practice in order to evaluate teacher performance. Explore how to schedule time/space for teachers and coaches to plan and teach collaboratively.
Design and implement learning experiences that engage and encourage students to become smart and passionate learners.
Building capacity in schools
With AED’s help, implement a three-tiered plan back at school:
- Shared vision: Engage faculty in conversations; come to a shared vision of what academic rigor looks like in practice.
- Building a professional culture: After identifying effective strategies and behaviors, provide meaningful supports and structures so all teachers can continually improve.
- Lab sites: Identify classrooms where desired behaviors can be seen and designate them lab sites. Bring teachers to observe these “sites” and look for evidence of deep learning.