RNL enrollment and financial aid models

To Whom It May Concern:

Pursuant to the North Carolina Public Records Law, I hereby request the following records:

1) All contracts with Ruffalo, Noel, Levitz (RNL) since Jan. 1, 2018 for enrollment management and financial aid optimization services.

2) All reports provided to the university by RNL since Jan. 1, 2018 regarding the following RNL services:
- Historical Research & Competitor Benchmarking
- Econometric Modeling
- Annual Financial Aid Plan Development
- RNL Smart View Dashboards & Reporting for Insight-Driven Response
- Retention Analysis
- Revenue Optimization & Simulation
- Four-Year Enrollment & Net Revenue Projection Model
- Enrollment Likelihood and FinAid Receptivity Scoring

3) All validation studies, differential impact studies, or comparable studies supplied by RNL or done by the university or a third party on the university's behalf regarding the accuracy of RNL's predictive models.

4) De-identified copies of all enrollment and financial aid model training datasets supplied to/received from RNL for 2021-2022 school year applicants, containing:
- All non-identifiable variables for each applicant that the university supplies to RNL for the purpose of building and training its predictive models
AND
- All output data fields (i.e., receptivity scores, lead categories, etc.) generated by RNL's predictive models for each applicant.

With regards to item 4 of my request, please note that I am asking for a de-identified data set that does not include fields that could be used to identify an individual applicant (e.g., name, address, SSN). These fields can easily be redacted by deleting those columns from the responsive spreadsheet.

I ask that all fees be waived as intend to use the requested records to further public understanding of public agencies and not for any commercial purpose. In the event you choose to impose fees, I request a detailed breakdown of the fees, including the hourly wage of each employee involved and an explanation justifying the employee hours required to fulfill the request.

Should you choose to reject this request or redact portions of responsive documents, I ask that you provide a detailed breakdown of the statutory exemptions and associated case law underlying your decision to withhold each/any portions from public review.

The requested documents will be made available to the general public, and this request is not being made for commercial purposes.

In the event that there are fees, I would be grateful if you would inform me of the total charges in advance of fulfilling my request. I would prefer the request filled electronically, by e-mail attachment if available or CD-ROM if not.

Thank you in advance for your anticipated cooperation in this matter. I look forward to receiving your response to this request within 10 business days.

Sincerely,

Todd Feathers

Hello,

I believe I may have sent the public records request copied below to the wrong email address initially. Could you please confirm receipt of it, or that you've already received this request?

Thank you!

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To Whom It May Concern:

Pursuant to the North Carolina Public Records Law, I hereby request the following records:

1) All contracts with Ruffalo, Noel, Levitz (RNL) since Jan. 1, 2018 for enrollment management and financial aid optimization services.

2) All reports provided to the university by RNL since Jan. 1, 2018 regarding the following RNL services:
- Historical Research & Competitor Benchmarking
- Econometric Modeling
- Annual Financial Aid Plan Development
- RNL Smart View Dashboards & Reporting for Insight-Driven Response
- Retention Analysis
- Revenue Optimization & Simulation
- Four-Year Enrollment & Net Revenue Projection Model
- Enrollment Likelihood and FinAid Receptivity Scoring

3) All validation studies, differential impact studies, or comparable studies supplied by RNL or done by the university or a third party on the university's behalf regarding the accuracy of RNL's predictive models.

4) De-identified copies of all enrollment and financial aid model training datasets supplied to/received from RNL for 2021-2022 school year applicants, containing:
- All non-identifiable variables for each applicant that the university supplies to RNL for the purpose of building and training its predictive models
AND
- All output data fields (i.e., receptivity scores, lead categories, etc.) generated by RNL's predictive models for each applicant.

With regards to item 4 of my request, please note that I am asking for a de-identified data set that does not include fields that could be used to identify an individual applicant (e.g., name, address, SSN). These fields can easily be redacted by deleting those columns from the responsive spreadsheet.

I ask that all fees be waived as intend to use the requested records to further public understanding of public agencies and not for any commercial purpose. In the event you choose to impose fees, I request a detailed breakdown of the fees, including the hourly wage of each employee involved and an explanation justifying the employee hours required to fulfill the request.

Should you choose to reject this request or redact portions of responsive documents, I ask that you provide a detailed breakdown of the statutory exemptions and associated case law underlying your decision to withhold each/any portions from public review.

The requested documents will be made available to the general public, and this request is not being made for commercial purposes.

In the event that there are fees, I would be grateful if you would inform me of the total charges in advance of fulfilling my request. I would prefer the request filled electronically, by e-mail attachment if available or CD-ROM if not.

Thank you in advance for your anticipated cooperation in this matter. I look forward to receiving your response to this request within 10 business days.

Sincerely,

Todd Feathers