2. At-Risk Data Mart

3. Student Vitae

4. Detailed score data

5. At-Risk Model Ninth Grade Foundational Dropout Model
The goal of this model is to identify students who are at risk for dropping out of high school
Educators can create, assign and manage programs for at-risk students and track student performance in the programs
The model uses student level data from the SLDS data warehouse 5

6. At Risk Model Models have Measures, Indicators and Indexes
Measures are the data points selected to go into the model
Indicators are groups of measures
Indexes are the resulting score(s) of the model algorithm
Models have the following qualities:
Weights – Measures and Indicators can be weighted to increase their value as part of the overall algorithm
Periodicity – Models may be run more than one time. Models can be snapshots in time, or longitudinal.
Index Evaluations – these are descriptors which help explain the value of the index scores 6

7. Educational Engagement
Model Measures
Academic Performance
State Assessment – Math
State Assessment – Science
State Assessment – Writing
State Assessment – Reading
Educational Engagement
Number of out of school suspensions
Expulsions
Student Background
Repeated one or more grades
Transfers
ACCESS for ELLs Score
Special Education
Free or Reduced Lunch
2 or more years over age for entering 9th grade 7

8. At-Risk Model - Measures
The Model’s Measures are grouped into Indicators. These are scored to create Indexes for each Indicator and for the overall Model. 8

9. Educational Engagement
Two measures are part of this indicator – these are scored based on student data and then calculated to produce an indicator index and evaluation
Measure Score
Measure Name:
Index Evaluation
Indicator Index: 9

10. Model Index Evaluation
Model Results
The 4 indicators are then used to calculate the Model Index Score
The student in this example has an overall model index of 2.01 and an overall risk level “MODERATE RISK”
Indicator Index Score
Indicators
Model Index Evaluation 10

11. Application
The application provides a platform to operationally implement dropout prevention efforts – including:
Programs and Interventions: organization and centralized storage of programming and intervention information
Strategies: create strategies and align them to your programming
Student Vitae: A single source of information for student information
Student Assignment: Assign programming and interventions to students and track their progress and attendance 11 11

12. Reporting The At Risk Data Mart contains two main areas of reporting:
Model Roster - a quick link with Access to all students Model Scores
Reports - contains Data Snapshots and Data Tables for deeper analysis 12