At-Risk Data Mart

Student Vitae

Detailed score data

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

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

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

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

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

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

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

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