« Home: Key questions for SLC implementation
How do our interdisciplinary teams pursue continuous improvement?
Continuous Program Improvement is integral to SLC team work. Teachers engage in disciplined reflection on their practice to ensure that all students are learning. They regularly examine student work and objective indicators of student learning and solicit input from all members of the SLC community to identify needs for improvement.
Continuous Improvement Tools
- Small Learning Communities Self-Assessment Tool (PDF 4.39M)
Forms
Analyzing data
Data Consensogram from Data-Driven Dialogue: A Facilitator's Guide to Collaborative Inquiry (PDF 40K) Adapted from Lipton, L.&Wellman, B. (2004) MiraVia, LCC, Sherman, CT. (1998) Aurora: Mid-continent Research for Education and Learning (McRel)
This is an activity to survey data readiness of audience; participants answer questions based on a scale and then create a group graph to see overall results.
Data-based decision making: Resources for educators. (2003). Charleston: Appalachia Educational Laboratory (AEL; now Edvantia)& Washington DC: Chief Council of School State Officers (CCSO).
- What are the most useful sources of demographic data? Explanation and examples.
- What are the most useful sources of direct student achievement data? Explanation and examples.
- What are the most useful sources of indirect student achievement data? Explanation and examples.
Assessment continuum of schoolwide improvement outcomes: Implementing the components of systemic schoolwide improvement (PDF 288K) Allen, W., Avery, M., Matsumoto, C., Hamilton, S., Worthley, D., Ciardi, M., & Allen-Malley, M. (2002) Newton: New England Comprehensive Assistance Center at Education Development Center, Inc. (NECAC at EDC)
Systematic process and rubrics for determining schools status with respect to many schoolwide improvement areas including data-based accountability. The tools also provide extensive lists of data that schools can use as evidence to document their status in their rubric.
Data driven decisions (2001) Arlington, VA: American Association of School Administrators (AASA). This issue is devoted to articles that exemplify how schools use data in making decisions.
Comprehensive school reform: Research-based strategies to achieve high standards: A guidebook on school-wide improvement Hale, S. (2000). San Francisco, CA: WestEd. Tool 1: District self-assessment guide, pages 61-65, provides questions for district-level participants regarding ability to support reform (and data use) in schools.
Evaluating Whole-School Reform Efforts: A Guide for District and School Staff Second Edition, August 2000
This guidebook was designed to assist district and school staffs that are working to evaluate their school reform efforts by providing guide posts that district and school staff can consider in choosing an approach to evaluation. The intention of the guidebook is to raise awareness of the complexity of program evaluation in general and increase awareness about the evaluation of whole-school reform efforts.
Data Use: Data Primer
The focus provides guidelines for schools and districts to follow regarding what data should be used and what data matters, helping educators to make the connections between NCLB data and additional types of data that can reinforce the decision-making process.
The Data Primer is organized around four modules. Each module provides a practical question that educators can ask when developing school improvement plans:
- Module 1: Where are we?
- Module 2: Where do we want to go?
- Module 3: How fast are we moving and in what direction?
- Module 4: Are we leaving anyone behind?
Using data in schools: Data-driven decision making (DDDM) toolkit (PDF 204K) (2003). San Francisco, CA: WestEd
This activity sets up a framework of the "stages" of data collection and use in making decisions about school improvement. So often, school staff have negative attitudes toward data especially if the data are not bringing "good news." This activity helps participants understand and think about data constructively.
Types of data card sort (PDF 232K) (unpublished workshop material) (2001). Aurora, CO: Mid-continent Research for Education and Learning (McREL)
This is an activity to educate participants about the four types of data.
Lessons Learned in Using Data to Support School Inquiry and Continuous Improvement: Final Report to the Stuart Foundation (PDF 5.62M) [CSE Technical Report 535]. Herman, J.,&Gribbons, B. (2001). Los Angeles, CA: Center for Research on Evaluation, Standards, and Student Testing (CRESST).
Stakeholder Input
Parents for Public Schools (2001), "Driving decisions with data" (PDF 120K) Jackson, WY: Parents for Public Schools
This newsletter is intended to help parents determine which data are important to school improvement efforts and why, as well as how their demand and use by parents can be helpful as a lever for change.
Toolbox for accountability (2002). Providence, RI: Annenberg Institute for School Reform at Brown University. Information, practical tools, and examples regarding community involvement in school improvement through accountability events.
Aligning Professional Development
Creating a seamless connection Stephanie Hirsh (November 1997), RESULTS National Staff Development Council
Strategies for meeting the NSDC standard: Effective staff development is aligned with the school's and the district's strategic plan and is funded by a line item in the budget.
