CCC Data update

• DW Advisory Meeting Notes, FY 20/21

Mark Cohen

Data Warehouse Report Server

• 95 colleges live with Data Warehouse report server

• Outreach continues to bring remaining colleges onboard

CCC Data 2.0.0

• Provides direct (ODBC/JDBC) access to the Data Warehouse

• Deployed to Pilot 10/2/20

• 6 Pilot Participants

  • Team/Enabling Services working w Phase 1 Pilot Colleges (2) to setup VPN  site to site connection so that the pilot testing can commence

    • 1 college configured with VPN

    • 1 college expected to be configured this week.

  • Engagement starting w Phase 2 Pilot Colleges (4)

• To be deployed to production in December

CCC Data 2.1.0

• New data sources in DW segmented by MIS code & made available to CCC’s through DW Report Server (DWRS) and direct  DW access, including:

  • Canvas (as opted-in to by individual colleges)

  • MyPath

  • COCI

  • C-ID

• New data sources in DL & made available to CCCCO:

  • Nova

  • Launchboard

• DWRS CVC-OEI enrollment report 

Development work has begun

• Pilot Q2

• Production Q3

Update on status of vision for success and student equity measurements metrics

Manos Stefanakos

Student Equity and Achievement/Vision for Success Metrics

• Addresses data change management issues and the connections to the metrics and that aspect of the data change management

Open

Student Equity and Achievement

1. Background: Student Equity and Achievement (SEA)

2. SEA Reporting Requirements

   1. Executive Summary (Includes goals identified, activities to achieve those goals)

   2. Budget Information

3. SEA Sample Report from NOVA

   1. Set of demographic groups

   2. Four categories of metrics they can select from (Access, Readiness, Retention,

   3. Can select what is the goal, what is the gap seeking to close

   4. Report is in table format, comes out of NOVA where reporting is done

   5. Accompanied by planning information that is free form text fields, to support report table

4. What are the SEA Metrics

   1. Colleges have to select at least 5 different groups of traditionally underrepresented students and select a metric for each (from Student Success Metrics list) in the following categories: Access, Readiness, Retention,
      Completion

5. What are the SEA Metics - Examples

   1. Percentage of students who enroll after applying (Access, SEA)

   2. In parenthesis you can see SEA metrics

   3. Analogous component that comes from the other two sets of metrics being evaluated

6. Data Lake/Data Warehouse Inclusion

   1. The NOVA annual reporting includes both metrics as well as planning and activity information that is provided in the form of text

   2. The metrics are generally student counts/ratios, often disaggregated by subgroups of students, e.g., by race, gender, LGBTQ, veteran, and economically disadvantaged status

   3. The numbers behind these already exist in MIS data, as student counts and demographic flags on individual student records

   4. The text fields could be brought in to provide additional insight

7. Suggestions for SEA Metrics

   1. While, ideally, one would be able to recreate the NOVA report using MIS data, because of timing, and other reasons, it is suggested that the NOVA SEA report be brought in, as it might be impossible to recreate the numbers from existing MIS data.

8. Questions/Concerns

• Manos Stefanakos: If it is based on completely on MIS data may not need to sort of re-import it as it comes out of Nova

  • Suggestion: similar view as to the reporting table be available maybe as part of the reporting or in a table saved either in Data Lake or Data Warehouse

• This is something that will be added to the Data Source prioritization list

• These data sources should be added to the list of potential data sources though nota high priority of this time. Next time we go over the privatization exercise we could, throw these into the mix and see where it ranks. But for now we should assume that it's not on the top.”

Vision for Success Metrics

1. Vision for Success Background

2. What are the Vision for Success Metrics?

3. Vision for Success Commitments

4. VFS Reporting

5. Recommendations VFS Metrics

   1. Harmonization/improved definitions so that people know if the metric they are looking at represents what they think it does

   2. Changes to Launchboard to show the appropriate target as part of reports/charts that track VfS metrics (have not seen latest builds that are coming out soon

6. Questions/Concerns

Change Management; to include both an update on change management efforts at the CO and discussion on how we can best support changes to metrics and data elements. 

David Kendall

Address Change Management as process/artifact

1. Need for Change Management

2. Best Practices

3. How we’re going to handle change management

   1. We want to known state of anything that is under change management

   2. We want a clear documented impact analysis, impact to Stakeholder community

   3. We want solid representation from known group of reviewers

   4. Needs to be presented to the appropriate decision making body for deciding on what to do with that change

   5. Ensures that we have the appropriate voice the appropriate identified and approved authority for making decisions around it

   6. Currently creating this concept and presenting it to leadership for their consideration.

Questions/Concerns

Discussion on concept of enabling districts to share data

Intro: Mark Cohen

• Discussion around a design perspective and specific data set.

• Question of whether a college district could use certain data sets and enable sharing with select colleges or districts

• Will table topic for right now. Will discuss further at a later time

Virginia Moran: In the past SCA was pre populated with MIS data and then we would just respond. So, but in the future. You're saying enable us to change what was reported in state MIS?”

*Related to discussion topic #2

1. Elaine Kuo: We just input the data we receive the data file. We then and then we enter it into Nova. There was no additional manipulation, there will be some drop downs that would occur, but I don't remember us doing any manipulation, there was no manipulation and then depending on what boxes you collect check that would also create other drop downs was my recollection, but there was no additional sort of like analysis of the data or inputting of the data.

2. Craig Hayward: That I can think of that may go to this, which is, I think it was possible to add a group that was not included in the derived in the data that was sent to the colleges so that may be the piece that you know can't be replicated or picked up kind of automatically from MIS.

Dulce Delgadillo: Are we saying that this data set would only have desegregation for metrics that are associated with student equity? Versus all of the other initiatives so SSM and we look at adult ed because some of those are also presented in a desegregated method. So I'm just a little curious on you know if the overlap. And when we say looking at SEA metrics are we saying that we are going to produce that the database would be able to produce a report that would list all of the SEA metrics with the desegregation that is intended that the state has told us to look at, because that also shifts right?

*Related to discussion topic #2

Manos Stefanakos: You'll see a little bit in the next section, we're talking about the success metrics, it absolutely captures the point you're making that similar metrics are looked at in a variety of different ways for different reasons. So, most of the students success metrics are represented in the list of the SEA metrics, you're just looking at them in a slightly different cut.

Z Reisz: “I think I just have kind of a broad one. So when we're talking about change management. So if you were to take, for example, the student success metrics, we're kind of talking about how we document and make modifications to any of those elements?”

Related to discussion topic #3

David Kendall: The decisions have to be made about the level of granularity, for sure, but that absolutely would seem logical to me.

Valerie Lundy-Wagner: Isn't this a legal issue?

*Related to discussion topic #4

Bridget (she/her/hers) Herrin: Do you mean under FERPA? If it's being used for legitimate academic purposes it should be covered.

Elaine Kuo: How much coordination is there between the data warehouse work and the launchboard efforts? I recognize that is part of our continuing conversation. Should there be representation from the launchboard side on this task force (especially when these data elements are discussed)?

*Related to discussion topic #4

Valerie Lundy-Wagner: I think that sharing data should be done only when needed/necessary. The "legitimate" academic purposes has not been articulated to me just yet, but I understand it is possible.

Jake Kevari:

Discussion with possibility of sharing data from the data warehouse shared with other colleges or other districts

Would this be across aggregates or at local level?

Discussed at this meeting, a decision was made to table this until a later date.

Nov 9, 1010

What do analytics indicate about CO MIS usage with regard to the data mart?

Alex Jackl will work with Todd Hoig to get answer to this question, will share response w Mark and Advisory group.

Per Alex, CO has been running some page web page analytics against people accessing data mart and so we might have some data from that.

Alex will share responses with Mark so that he can share with Advisory group.

Alex Jackl

Should vision for success and student equity measurements metrics to be included

Manos Stefanakos will look into it and report back to the group on whether it's something that will be published again, or what the formula is in terms of how it is produced

Manos Stefanakos

Mark Cohen

Attaches in these meeting notes.

Manos Stefanakos

Mark Cohen