Mitch Fowler School Data Consultant Calhoun ISD Identifying and Acting on At Risk Data

Outcomes Today you will: 1.identify at risk data points for dropout prevention and intervention support. 2.determine what thresholds exists for at risk data points. 3.observe how other districts are using this data.

Title I and 31-A Risk Factors

31-A Legislation Section 31a of the State School Aid Act Provides supplementary instructional and pupil support services for pupils who meet the at-risk criteria specified in the legislation. Criteria include – low achievement on MEAP tests in mathematics, reading or science; – OR failure to meet core academic curricular objectives in English language arts or mathematics (applies to grade K-3 pupils only); – OR the presence of two or more identified at-risk factors.

31-A Workbooks

Adding a Local Twist – Two Examples

Activity - What Data Should You Use?

Category Weights? Atypical Behavior MME

Collecting and Organizing At Risk Data 31 A Risk Factors Assessment Victim of Childhood Abuse and Neglect Teen Parent Family History

Collecting and Organizing At Risk Data At-Risk Data Report MEAP Data 31-A Risk Factors Assessment Data BehaviorAttendanceLocal Data

Collecting and Organizing At Risk Data At-Risk Data Report CISD At- Risk Data Tool At-Risk Demographics

Early Warning Signs for At Risk Students - Dropouts

The Research Says… Sixth graders with poor attendance (less than 80 percent), a failing mark for classroom behavior, a failing grade in math or a failing grade in English had only a 10 percent chance of graduating within four years of entering high school and only a 20 percent chance of graduating a year late (Balfanz & Herzog, 2005).

Building Early Warning Systems According to Craig Jerald, an early warning system should: – identify risk factors by individual student – aggregate risk factors by school and type of school – identify rates of decline in academic achievement and engagement – identify school-level outcomes (on track by grade, off- track recovery rates, and graduation rates), and system wide analysis of student characteristics, risk factors, outcomes, and impact of interventions. Identifying Potential Dropouts: Key Lessons for Building an Early Warning Data System: A Dual Agenda of High Standards and High Graduation Rates by Craig Jerald.

The CISD’s Early Warning System - DataDirector Blending of: – Research – Local Preferences – Existing DataDirector Assessments – Newly Created DataDirector Assessments – Custom Reporting in DataDirector – Augmented Reporting Outside of DataDirector – Newly Generated Demographics

Collecting Obscure Data Early Warning Signs Demographic Assessment Academic Marks Mobility Repeated Grades

Collecting Obscure Data

Aggregating the Data Early Warning Signs Custom Report State / Local Data Academic Marks AttendanceBehavior Repeated Grades Mobility

Aggregating the Data

Identifying / Ranking At-Risk Students

Sharing Findings With Stakeholders

Acting on the Data - Rigor Align performance standards to college and career readiness Focus on transitions from high school to college and careers as well as on transitions into high school Schools that offer fewer math courses below Algebra I reduced the odds of dropping out by 28 percent, and those that offer calculus reduced the odds by 55 percent (Lee & Burkham, 2000).

Acting on the Data – Effective Teachers Effective teachers need to be working closely with those most at-risk for dropping out…

Acting on the Data – Extended Learning Time Check & Connect : Provides trained monitors to small groups of students. Closely follow tardiness, absenteeism, behavioral referrals, and academic performance and meet with individual students each week. Intensive interventions such as Check and Connect can cut dropouts by as much as half, but they are even more effective when implemented with school wide reforms (Jerald, 2007).