Introducing
Prezi AI.
Your new presentation assistant.
Refine, enhance, and tailor your content, source relevant images, and edit visuals quicker than ever before.
Trending searches
Loading…
Data correction process flow
Transcript
PEIMS Data Submission Overview
SIS
$
Data Entry
- there is no data validation at the entry level regardless of registration method (online, paper)
- dirty data is the result
- SIS does not have self checking processes
- data problems remain until submission time
- third party software required to check dirty data
- little focus is placed on data quality by SIS
Problem
Texas Student Data System
TSDS
- 4 submissions per year
- Student data / staff data / financial data
- deadlines must be met
- only exceptions are major catastrophic events
- XML file format
- 2 days to create XML file in SIS
- 45 minutes to upload file - errors could occur
- 2 hours to batch file on weekends at low peak time
- 2 hours to promote file if file passes batching stage
- 2 hours to process file if file passes promote stage
- Error reports are difficult dissiminate
Process for error correction
Error
correction
- upload xml file to third party software
- campus clerks recieve email of errors from third party software
- clerks call PEIMS office for assistance
- errors include both staff and student data
- Enormous amount of time is spent cleaning data
- Staff and resource utilization is high
- Cleaning of data is a year round repetetive task
- Every campus has a PEIMS data clerk
- Some departments also have PEIMS data clerks
- Most clerks are unskilled employees
Fall Submission
- Enormous amount of time is spent cleaning data
- Staff and resource utilization is high
- Cleaning of data is a year round repetetive task
- Every campus has a PEIMS data clerk
- Some departments also have PEIMS data clerks
- Most clerks are unskilled employees
Fall Submission
Summer Submission
- 2017 summer submission top four errors
- PK coding
- program type code
- funding source code
- Early reading indicator (kg, 01, 02)
- CATE coding
- course setups
- dual credit
- college credit hours
- Course Completion
- scheduling issues - MS/HS
- credit by exam courses
Dirty data implications
- LEA is rated based on 4 areas - Student Acheivement, Student Progress, Closing Performance Gaps and Postsecondary Readiness
- Data quality is essential for an accurate district/campus ratings
- Postsecondary readiness
- If we under report our graduates
- If we under report our college courses
- If we under report our dual credit courses
Accountability
Campuses rely on accurate data for maximum funding
ADA, bilingual, special Education, etc, errors affects funding
Fiscal
Implication
Data clean up
Solution
Choose a template
Presentations from around the world
Learn more about creating dynamic, engaging presentations with Prezi