At-Risk Data Mart

Student Vitae

Detailed score data

At-Risk Model

Ninth Grade Foundational Dropout ModelThe 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

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

Academic Performance

State Assessment – MathState Assessment – ScienceState Assessment – WritingState Assessment – Reading

Educational Engagement

Number of out of school suspensionsExpulsions

Student Background

Repeated one or more gradesTransfers ACCESS for ELLs ScoreSpecial EducationFree or Reduced Lunch2 or more years over age for entering 9th grade

Model Measures

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.

Two measures are part of this indicator – these are scored based on student data and then calculated to produce an indicator index and evaluation

Educational Engagement

Measure Name:

Indicator Index:

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”

Model Results

Indicators

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

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