Data Collection

Data about our districts, schools, staff, and students are submitted to RIDE and used for a wide variety of purposes. Collecting, validating, and submitting those data is an increasingly complicated task for district technology departments. This page provides district administrators, technology directors and data managers with the necessary resources to manage the data flowing through their departments.
 
Below you will find information about each collection (Collection Specifications), detailed definitions for each data element collected (Data Dictionary), and the resources necessary to ensure the quality of your data (Data Quality).

Specifications for 2014-15

Below are the data collection specifications for the upcoming 2014-15 school year. Each document includes the appropriate submission process, data elements descriptions, validations, and changes from the prior year.

All files are in PDF format.

Current Specifications

Below are the current data collection specifications for the 2013-14 school year. Each document includes the appropriate submission process, data elements descriptions, validations, and changes from the prior year.

All files are in PDF format.

Guidance

Below are the guidance documents on specific data collections for the 2013-14 school year.

All files are in PDF format.


The RIDE Data Dictionary

The RIDE Data Dictionary provides definitions, layouts and attributes for the various data elements that RIDE collects, stores and reports. The principle use of this meta-data repository system is to provide a consistent and reliable means of access to data.

Data Dictionary screenshotThe inclusion of data elements in this dictionary reflects state and federal data collection, analysis, storage and reporting requirements. 

  • Users may use the system to search for data elements and the embedded code-sets by keyword, entity, domain and data event names, and by program areas and data owners.
  • State, district and school administrators may use the built-in tools to build record layout sheets and data submission templates. 
  • Analysts, data administrators and developers can apply the meta-data in system integration, data validations and in creation of enterprise data management and reporting systems.

Go to the Data Dictionary.


Tutorial

A video, Data Dictionary Tutorial: Introduction and Use of Basic Features [MP4] is available to help get you started on the tool.

Data Quality Continuum

graphic showing Data Gathering, Collection & Storage (Date) leads to Data Integration, Access & Retrieval (Information) leads to Data Analysis & Use (Knowledge) leads to Improved Outcomes for Students

Data quality can be seen as a continuum of processes undertaken at all system levels. It is important to understand that improving data quality at the source reduces cost and difficulty and that data quality checks should take place at every stage along the way to provide the greatest accuracy.

National Data Quality Standards (what good data look like)

  • Accuracy (data are recorded correctly)
  • Completeness (all relevant data are recorded)
  • Uniqueness (each entity is recorded only once)
  • Timeliness (data are kept up to date)
  • Consistency (data agrees with itself)


Data Quality Levers

Levers to Improve Data Quality graphic, showing: Systems-Use of technology to minimize use of paper, automate processes, and uncover errors; Processes-Development of systematic procedures, rules, and guidelines to generate clean, reliable and error-free data; People-Involvement of staff technology skills, knowledge of good processes, and judgment about how to prevent or fix data quality problems.

R.I. Data Quality Tools

National Data Quality Resources



RI Data Quality Award Program

To recognize the important work going on across the state toward building and maintaining data quality, the Rhode Island Data Quality Award Program will recognize the commitment and effort RI educators members make to ensure that our educators and the public at large have access to high-quality data and that we use data for wise decision-making to improve teaching and learning.

Goals:

  • Highlight the increasingly complex and difficult work of collecting, maintaining, and using education data
  • Promote the idea that data quality is more important than ever as education embraces data
  • Acknowledge the importance of data responsibilities at all staff levels within the education profession

Criteria

Candidates should demonstrate excellence or leadership in creating a culture of data quality leading to the use of better data for improving student outcomes.

Process

Award

  • Engraved desk clock
  • Framed document/certificate

Moving forward

  • Data Quality Award will be presented three times each school year
  • Nomination windows will be announced throughout the school year