Are we making progress on our school readiness goals?
Best practice in the field of early childhood education recommends that teachers use their daily interactions and observations to inform lesson planning on an ongoing and regular basis. While the information teachers use to plan may certainly be accurate and current, it is not always the most relevant, as teachers naturally categorize their observations by developmental area rather than by program-specific school readiness goals and objectives. Now that observations of a child’s development are gathered digitally (such as in the child assessment tool, COR Advantage) programs have an opportunity to analyze assessment data as it correlates to their school readiness goals and objectives.
ANALYSIS
First, Centro crosswalked individual items on the COR Advantage child assessment with the program’s school readiness goals and objectives. They looked specifically at how each data point in the assessment correlated with children’s progress towards their school readiness goals.
Then, child assessment data were exported from COR Advantage in Excel format, uploaded into Power BI, and disaggregated and grouped according to Centro’s school readiness goals and objectives. Centro maintains a dashboard that allows them to filter data by school year, program, site, classroom, individual child, and disability status.
The matrix (bottom half of the image) shows the average score for each of domain of Centro’s school readiness goals and objectives. Full drill-down capability also allows them to look at the scores of specific items within each domain if they choose. The data are represented as average scores for each of Centro’s four assessment periods. Also included are the number of observations that serve as the basis for each average score.
Above the matrix are two line graphs. The one on the left shows average scores across the four assessment periods. The line graph on the right shows the number of observations recorded for each assessment period.
Teachers analyze this dashboard to (1) make changes to the classroom environment, (2) improve teacher-child interactions, (3) plan intentional teaching activities, and (4) inform the selection of activities to send to the home.
RESULT
The analysis allows teachers to make intentional, data-driven decisions while planning their lessons and activities in order to meet the program’s commitment to its published school readiness goals and objectives.
ADDITIONAL DETAILS
AUDIENCE: Direct Staff, Management, Leadership
LEVEL OF ANALYSIS: Child, Classroom, Center, Grantee
DIFFICULTY: Advanced
CONTENT AREA: Child development
DATA SOURCES: COR Advantage, Excel, SharePoint, PowerBI
CONSIDERATIONS AND CAVEATS: Must have: Processes that ensure data integrity, extraction and manipulation. Next Steps: Training direct users (classroom staff) in use of data for data-driven decisions. Ongoing: Monitoring protocols for data-driven analysis and reporting of school readiness outcomes.
ALTERNATE AUDIENCE GUIDANCE: Summary progress reports to Board of Trustees and Policy Council. Inclusion in Annual Report.
TECHNICAL APPENDIX
Education Specialist extracts CSV flat file from COR Assessment,
The unmanipulated data is uploaded into a designated SharePoint folder,
Microsoft Flow (workflow tool) is triggered to extract the SharePoint file and transfer data to Centro’s Microsoft’s Azure (cloud-based) Data Warehouse.
Stored procedures (coder) build(s) tables that create data relationships for PowerBI analysis and visualization.
Importantly, step 4, coder is assisted by the content-area user, in this case by the Education Specialist or Coordinator, who identifies the source data sets, reports or data repositories for use in the construction and analysis --as well as in the final “feel and look”-- of the PowerBI visualization. This is Centro’s organic design development process to assure the validity of the outcome indicators that support and demonstrate Centro’s performance beyond compliance and continuous improvement for the respective content area of service.
CONTACT
Content Area Education Specialist - Euleta Christiansen e.christiansen@cdlf.org
Technical Appendix - Michael Fairchild m.fairchild@cdlf.org
Program Director - Gonzalo Palza g.palza@cldf.org