DataBased Decision Making Driving Force for instruction Analyzing

Data-Based Decision Making D-riving Force for instruction A-nalyzing for better outcomes T-eacher training and ownership A-chievement for all stakeholders Presented by: Norris Evans Robbie Garnes Shenoah Howard Kristina Truell

Presentation Objectives O To understand the benefits of data driven instruction O To learn the key steps of successful data use O To implement data strategies to be used daily

Changing Habits If you keep doing what you’re doing, you’ll keep getting what you’ve got. - Albert Einstein

Data-Based Decision Making O Data-Based Decision Making is a school improvement approach that uses quantitative data to help describe or define problems, direct activities, target interventions, and allocate resources. (asca. membershipsoftware. org)

Entertaining Video about Data Driven Instruction http: //www. youtube. com/watch? v=Mf 5 6 q. EGm. Is. I

What are the benefits of understanding and using data? O Develops a system that is unified, integrated, and, meaningful O Helps us to more accurately and efficiently choose interventions and determine if they are working O Builds a culture of inquiry and continuous improvement

Benefits of the Data Graph A Data Graph: O can be used to show a clear visual picture of student performance. O can be used to show areas of growth, areas of weakness, and can also serve as a guide for departments when developing lesson plans to meet students’ needs.

Differential Population

Expectations for Data Use O Use daily to improve instruction O Use daily to improve student performance O Use daily to improve ownership of the teaching process O Use daily to improve ownership of the learning process

Conditions that Facilitate the Growth Process O Collaborative Culture – Wide range and diversity of perspectives with colleagues who value data O Collaborative Structures – Data teams with scheduled meeting times and officially sanctioned opportunities O Access to useful data – pulling data from multiple sources in a user-friendly way O Widespread data literacy – To make sense of the data and to develop measurable goals

Sources & Resources O Staff Development O Data Room O Data planning Wednesday O Data teams based on grade level

Data Teams O Teams will be based on grade level. O Teams will also be given time to develop strategies that address low performing students in a cross- curricular format with incentives to increase student achievement.

Implementation O August- Analyze O O Previous year’s scores September –Course Pre-test October- Data driven instruction November-Data driven instruction cont. December-Data driven instruction cont. O January – Mid Term O February – Data driven SOL prep O March – Data driven SOL prep cont. O April – Data driven SOL prep cont. O May – Course Post-Test

Previous Year’s Scores O Core Teachers will receive scores relevant to their content area. O Elective/HPE Teachers will receive reading scores. (Reading in the content area will be addressed in these classes. )

Intervention OTeachers should show evidence of Intervention by giving students pretests and post-tests.

Pre- Test O Each teacher will give a pre-test before learning begins; this will allow teachers the ability to identify areas to focus on during instruction.

Post Test O The teacher will give a post-test to determine if students have increased their knowledge and/or mastered particular concepts that were taught throughout the school year.

Example of Data gathering tool Pre and Post Test

Data Observation DATA USE OBSERVATION: Purpose: To evaluate the use of data to improve instruction The teacher: ☐ ☐ ☐ • • 1 2 3 4 Analyzes data and revises lesson plans. Uses data to differentiate instruction. Uses data to re-focus student attention. Collaborates with grade level. Shows others how to use data. Evidence will be data collection tools ( informal and formal assessments) Teachers will be rated on a scale of 1 – 4 of which 4 is the highest rating.

Conclusion Data are important tools that all stakeholders should fully understand, evaluate, and use daily to drive instruction to increase student achievement.

References Harringtion, K. & Gray, K. Creating successful creating successful elementary schools data teams (2012). Retrieved from asca. membershipsoftware. org/ files/datadecisionteams. pdf