1. Official Term Data (OTD):
New Structures for Colleague, for Research Purposes
MMCC Data Discussion
November 14, 2016

2. Collaborating Offices:
Institutional Research
Information Technology
Registrar

3. Design phase:
Consider first: “Which questions would we like OTD to answer”?
1. Consulted colleagues at other institutions: “Which
types of data do you include in your OTD?”
a. Student data (one row per student per term).
b. Student/Course data (students’ experience xcv with each course that they take).

4. Design phase:
Consider first: “Which questions would we like OTD to answer”?
Types of reports that MMCC faculty and staff run most frequently (Ginger).
External reporting requirements.

5. Colleague changes continuously, as MMCC records transactions.
Examples:
Student applies for admission.
Student registers for or drops a course.
Student receives a grade.
Someone pays a bill.

6. Colleague changes continuously.
Creates inconsistency in research and reporting.
Particular statistics vary depending on
when the data were drawn from Colleague.
Other sources of variation: which mnemonic is used, and how employees construct Informer reports.

7. OTD creates a consistent historical record for each term.
1. Freeze OTD at a standard time each term.
2. Implement standard missing data entry, and zzzprogram validation procedures.
3. OTD then does not change.

8. OTD creates a consistent historical record for each term.
Allows “apples to apples” comparisons across terms.
Most colleges and universities use OTD.
Predictive analytics (e.g., Civitas, IBM Modeler).

9. Clean Up of Existing Files
Began with Fall 2014
Entered missing demographic and residency data for approximately 1800 student files
Corrected program of study information for students with 2 or more active programs
Confirming information with students at registration counter

10. OTD creates a consistent historical record for each term.
Anthony’s historical perspective:  
dearth of confidence in MMCC’s data reported from Colleague
fluidity leads to discrepancies.   
Colleague is a transactional database (continually changing)  Result = inconsistency.
Someone ran a report two months apart and got different results, and this is the consequence . . .

13. Clean-ups of research-essential fields in Colleague, including:
Term Status (first-time, continuing, transfer, guest, or dual).
Disabilities.
Veterans (in progress).
International Students.
First generation and parents’ education levels.

14. The largest, most complex, and most essential clean-up was to Academic Programs..
90% of students (going back to Fall 2012) now have only one active program per OTD term.
Degree of accuracy of primary Academic Programs for past terms is about 93% to 95%.
Allows accurate reporting by Deans’ Areas, and by ACS Divisions. (VFA cohorts.)

15. Clean-up process for Academic Programs
for past terms.
Examined existing start dates for programs
Maintained programs with most current start dates, avoiding labor intensive case-by-case examinations
Based on that approach, we now can demonstrate at least a 93% to 95% accuracy rate for remaining active program of study

16. New points of emphasis regarding academic programs
for Registration and Advising staff.
When students change programs, start date for the new program must be specified, as well as end date for the previous program

17. Predictive analytics. Forecast future probabilities,
by analyzing current and historical data.
Includes data mining, statistical modeling, and machine learning.

18. Integrating OTD into reporting processes
Fall 2016 enrollment reported to IPEDS.
Redirecting some mnemonics to draw ifrom OTD, in prioritized order.
Available in Informer.

19. Research Brief on OTD distributed college-wide, in August 2016
Research Brief on OTD distributed college-wide, in August (See IR website.)

20. AQIP Action Project Official Term Data (OTD) will be included
in the next HLC Systems Portfolio
as an AQIP Action Project.

21. Comments and/or Questions?