Collaborative Data Informed Decision Making: How to Use Data to Improve Student Learning

Overview

In this interactive 35 minute video presentation, we will explore how collaborative teams can engage in data informed decision making (DIDM) in order to advance student learning. In recent years, it has become expected of educators that they use data to inform their instructional decisions and help close learning gaps. Additionally, DIDM is a primary component of collaborative groups like professional learning communities (PLCs). After first reflecting on why data analysis is important, DIDM will be unpacked. Attention will also be given to the process a team goes through to engage in DIDM and the role of the individual teacher. The concept of equitable DIDM will also be briefly reviewed. By the end of this power-up, you will be able to identify the characteristics of DIDM and the professional moves you can make to incorporate DIDM into your professional toolbox and your PLC or collaborative team’s practice.

Essential Questions:

1. What is data informed decision making (DIDM)?

2. How does a team engage in DIDIM?

3. What is a teacher’s role with DIDIM?

Learning Outcomes

• InTASC Standards:

  • 6(a) The teacher balances the use of formative and summative assessment as appropriate to support, verify, and document learning.

  • 6(c) The teacher works independently and collaboratively to examine test and other performance data to understand each learner’s progress and to guide planning.

Success Criteria

• Submit a reflection on the prompts for the application activity.

Power-Up Instructions

• Review the learning outcomes and consider how you might be able to contribute to collaborative DIDIM.

• Make a copy of the slidedeck for review and notetaking.

• Watch this presentation on Collaborative DIDM (35 minutes).

• Complete the Application Activity.

Application Activity

Application Activity:

To apply your learning, please complete the following activities:

1. Work with a team of teachers or teacher candidates and create a common assessment. It can be formative or summative, but I would advise you create something that you will be able to use and then analyze while completing this power up. Make sure this assessment aligns with specific standards.

2. Collect and organize the data for this assessment. Identify students by the following categories: mastery, approaching mastery, and developing learner. Try to align the individual items within the assessment by standards. If the assessment is qualitative in nature and you have a rubric, try to do the same thing and align components by standard.

3. Bring your results to a team meeting and discuss your findings and next steps. If you are able to meet again after those next steps have been implemented, include that in your reflection.

4. Reflect and respond to the following prompts in video or writing:

   1. What assessment did you and your team design? How did the process go? Did you follow similar guidelines as discussed in this power-up?

   2. Describe the conversations around the data, students, and the next steps. What went well and what didn’t? What did you learn about your students and how will you plan for future instruction?

5. Submit your reflection to the prompts in your submission for this power-up.

References

• [EL Education]. (2018, Jan. 19). Grade Level Data Meeting with Third Grade Teachers [Video]. YouTube. https://www.youtube.com/watch?v=BLCbNX84ll4

• [CLAS Network]. (2015, Nov. 30). PLCs at Work: Analyzing and Using Summative Assessment Data [Video]. YouTube. https://www.youtube.com/watch?v=7V28XWekcNI

• [Data Quality Campaign]. (2014, Jan. 15). How Data Help Teachers [Video]. YouTube. https://www.youtube.com/watch?v=cgrfiPvwDBw

• Guskey, T. R. (2003, February). How Classroom Assessments Improve Learning. How Classroom Assessments Improve Learning - Educational Leadership. http://www.ascd.org/publications/educational-leadership/feb03/vol60/num05/How-Classroom-Assessments-Improve-Learning.aspx.

• Ford, A. (2020, August 4). Using Data to Advance Racial Equity. Edutopia. https://www.edutopia.org/article/using-data-advance-racial-equity.

• Knips, A. (2019, June 13). 6 Steps to Equitable Data Analysis. Edutopia. https://www.edutopia.org/article/6-steps-equitable-data-analysis.

• Lillydahl, D. (2019, June 26). All Things PLC. Blog | All Things PLC | Powered by Solution Tree. https://www.allthingsplc.info/blog/view/392/helping-data-analysis-take-root.

• Martin, J. and Torres, A. (N.D.). User’s Guide and Toolkit for the Surveys of Student Engagement: The High School Survey of Student Engagement (HSSSE) and the Middle Grades Survey of Student Engagement (MGSSE). National Association of Independent Schools. https://www.nais.org/Articles/Documents/Member/2016%20HSSSE-report-full-FINAL.pdf

• Schuhl, S. (2016, April 27). All Things PLC. Blog | All Things PLC | Powered by Solution Tree. https://www.allthingsplc.info/blog/view/325/doing-it-or-doing-it-well-using-data-for-learning.

• Sterling, S. (2015, October 15). Best Practices for Using Data in the Classroom. Edmentum Blog. https://blog.edmentum.com/best-practices-using-data-classroom.