The Value of Running on a Longitudinal Data Structure

By

August 30th, 2017

Did you miss our presentation with Fort Wayne Community Schools (FWCS) at this year’s NCES Summer Data Conference 2017 in Washington, D.C.? Well, you’re in luck! Below you will find a detailed agenda from the presentation and the PowerPoint slides. If you have any questions, feel free to reach out to us so that we can connect with you!

 

 

Download our case study on how FWCS Informs Its Strategic and Daily Decision Making with Data.

 

 

 

 

The Value of Running on a Longitudinal Data Structure

 Byrd, Gernhardt, Nolan-Abrahamian, White

August 1, 2017

 

Agenda

  • Motivation
  • Implementation
  • Outcome
  • Application
  • Considerations
  • Aspirations

Motivation

  • Disparate transactional systems, CSVs and/or Excel

– Lack of data direction

– Time cost in data compilation from multiple sources

  • Inconsistent data structure and quality

– Frequently recreating reports

– Unreliable key matching

– Lack of consistent field values

  • Instability in data sources and reporting over time

– Transactional system change destroys longitudinal reporting capability

– Report recreation in the transactional systems

Implementation

  • 1998 – RFP for data warehouse
  • 1999 – Implement Complete Data Warehouse (CDW) from eScholar
  • 2000 – Data Warehouse goes live
  • 2000+ – Expanded data and reporting capabilities

Outcomes

  • Common data structure
  • Common reporting platform
  • Facilitated data cleanup through standardized ETL (Extract, Transform, Load) plans
  • Longitudinal data despite 4 different Student Information Systems

Application (School Reporting)

  • Centralized location for demographic, attendance, discipline, and assessment data
  • Allows for comparisons across years/transactional systems

 

Application (Evaluative Reporting)

  • Teacher Evaluation System
    • Longitudinal data system serves as host for initial student-teacher linkage
    • Historical assessment data leads to growth band creation for formative literacy assessments
    • District created growth measures are used for teachers in non-tested subject areas

 

Application (Predictive Analysis)

  • Allows administrators ready export of student-level data

Considerations

  • Potential for overabundance of reports and reporting platforms (transactional and data warehouse)

– Report identification, organization and management

– Customizable Data Security

  • Process and implementation needs to be supported from top-down
  • Data Extraction and Data Analysis expertise

Aspirations

  • Data modeling
  • Business Intelligence (SSAS)
  • Enhance data analysis
  • Unified dashboard (report management)
  • Power User access to clean, pre-joined data sets
  • Row-level security

 

Click here to download the slide deck. 

Be the first to write a comment.

Your feedback

Our Solutions

eScholar

myTrack

Instructional improvement system helping educators personalize education and students achieve their goals.

Learn More
eScholar

Uniq-ID

Identity matching solution to support the integrity and accuracy necessary to personalized education, even for highly mobile students.

Learn More
eScholar

Complete Data Warehouse

Complete solution that includes standards based data collection, powerful data cleansing and comprehensive data integration.

Learn More
eScholar

EDFacts

Supported and comprehensive solution that takes the tedium and complexity out of federal EDFacts reporting.

Learn More
eScholar

eScholar U

Professional development and training for educators, administrators, support staff and IT teams delivered both live and on-line.

Learn More
eScholar

DirectMatch

With just a SNAP record and student enrollment data, eScholar DirectMatch quickly and accurately identifies children who are eligible for free and reduced school meals.

Learn More