Student Predictive Analytics Services docs
It is a clone of this request.
| Tracking # |
No. 364 364 |
| Submitted | Sept. 5, 2023 |
MuckRock users can file, duplicate, track, and share public records requests like this one. Learn more.
Communications
From: Todd Feathers
To Whom It May Concern:
Pursuant to the Pennsylvania Right to Know Act, I hereby request records related to student predictive analytics services used by the university.
For the purpose of this request, the term "student predictive analytics services" means software or computer code that employ machine learning models to generate predictions about individual students or groups of students for one or more of the following purposes:
a) To generate predictions about which prospective student names/contact information to buy from vendors such as the College Board, ACT, Cappex, etc. for advertising and recruiting.
b) To generate predictions about prospective students' likelihood to apply to, be admitted to, and/or enroll at the university.
c) To generate predictions about prospective students' financial aid needs and/or likely response to different financial aid packages.
d) To generate predictions about enrolled students' likelihood of passing a class, re-enrolling for another term (retention), and/or graduation (these tools are often called early warning systems).
My request applies to student predictive analytics services used by the university during the 2022-23 academic year, including those developed in-house by the university or purchased by the university from a vendor.
I request:
1) All contracts and statements of work between the university and outside parties for student predictive analytics services that the university used during the 2022-23 academic year.
2) Records defining the input variables and their weights/relative power for each student predictive analytics service the university used during the 2022-23 academic year (In other words, records defining the data points fed into each predictive model and its respective impact on the model's output).
3) Records documenting the accuracy (AUC, true/false positive, specificity, lift, etc.) of each student predictive analytics service the university used during the 2022-23 academic year.
4) Any validation studies, disparate impact studies, or other records documenting the student predictive analytics services the university used during the 2022-23 academic year performed for different student groups (e.g. Black students vs. White students, male students vs. female students).
I ask that all fees be waived as I am a journalist and intend to use the requested records to publish articles in the public interest about the operations of a government agency. If you choose to impose fees, I ask that you provide an explanation of the fees, including the hourly wage of the lowest-paid employee capable of fulfilling the request.
If you choose to reject this request or redact portions of responsive documents, I ask that you cite the statutory exemptions and associated case law underlying your decision to withhold each portion 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 5 business days, as the statute requires.
Sincerely,
Todd Feathers
From: Muckrock Staff
To Whom It May Concern:
I wanted to follow up on the following Pennsylvania Right to Know Act request, copied below, and originally submitted on Sept. 5, 2023. Please let me know when I can expect to receive a response.
Thanks for your help, and let me know if further clarification is needed.
From: Slippery Rock University
September 12, 2023
Filed via MuckRock.com
E-mail (Preferred): requests@muckrock.com<mailto:requests@muckrock.com>
MuckRock News
DEPT MR 151593
263 Huntington Ave
Boston, MA 02115
Re: Right to Know Law Request [No. 364]
To Whom It May Concern:
On Wednesday, September 6, 2023, Slippery Rock University of Pennsylvania received your written request for records pursuant the Pennsylvania Right-to-Know Law ("RTKL"), 65 P.S. §§ 67.101 et. seq. Slippery Rock University will require a 30-day extension as permitted by law, 65 P.S. § 902. Therefore, a response is due on or before Thursday, October 12, 2023.
Request:
To Whom It May Concern:
Pursuant to the Pennsylvania Right to Know Act, I hereby request records related to student predictive analytics services used by the university.
For the purpose of this request, the term "student predictive analytics services" means software or computer code that employ machine learning models to generate predictions about individual students or groups of students for one or more of the following purposes:
a) To generate predictions about which prospective student names/contact information to buy from vendors such as the College Board, ACT, Cappex, etc. for advertising and recruiting.
b) To generate predictions about prospective students' likelihood to apply to, be admitted to, and/or enroll at the university.
c) To generate predictions about prospective students' financial aid needs and/or likely response to different financial aid packages.
d) To generate predictions about enrolled students' likelihood of passing a class, re-enrolling for another term (retention), and/or graduation (these tools are often called early warning systems).
My request applies to student predictive analytics services used by the university during the 2022-23 academic year, including those developed in-house by the university or purchased by the university from a vendor.
I request:
1) All contracts and statements of work between the university and outside parties for student predictive analytics services that the university used during the 2022-23 academic year.
2) Records defining the input variables and their weights/relative power for each student predictive analytics service the university used during the 2022-23 academic year (In other words, records defining the data points fed into each predictive model and its respective impact on the model's output).
3) Records documenting the accuracy (AUC, true/false positive, specificity, lift, etc.) of each student predictive analytics service the university used during the 2022-23 academic year.
4) Any validation studies, disparate impact studies, or other records documenting the student predictive analytics services the university used during the 2022-23 academic year performed for different student groups (e.g. Black students vs. White students, male students vs. female students).
I ask that all fees be waived as I am a journalist and intend to use the requested records to publish articles in the public interest about the operations of a government agency. If you choose to impose fees, I ask that you provide an explanation of the fees, including the hourly wage of the lowest-paid employee capable of fulfilling the request.
If you choose to reject this request or redact portions of responsive documents, I ask that you cite the statutory exemptions and associated case law underlying your decision to withhold each portion 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 5 business days, as the statute requires.
Sincerely,
Todd Feathers
View request history, upload responsive documents, and report problems here:
https://www.muckrock.com/
If prompted for a passcode, please enter:
••••••••
Response: This request will be completed. Slippery Rock University will require a 30-day extension as permitted by law, 65 P.S. § 902. Therefore, a response is due on or before Thursday, October 12, 2023. Please do not hesitate to contact me if you have any questions.
Tina Moser
Agency Right-to-Know Officer
Slippery Rock University
104 Maltby Center, Suite 300
Slippery Rock, PA 16057
C: Laura Neal, Esquire
Pennsylvania State System of Higher Education
From: Muckrock Staff
To Whom It May Concern:
I'm following up on the following Pennsylvania Right to Know Act request, copied below, and originally submitted on Sept. 5, 2023. You had previously indicated that it would be completed on Oct. 12, 2023. I wanted to check on the status of my request, and to see if there was a new estimated completion date. You had assigned it reference number #364.
Thanks for your help, and let me know if further clarification is needed.
From: Slippery Rock University
October 12, 2023
Filed via MuckRock.com
E-mail (Preferred): requests@muckrock.com<mailto:requests@muckrock.com>
MuckRock News
DEPT MR 151593
263 Huntington Ave
Boston, MA 02115
Re: Right to Know Law Request [No. 364]
To Whom It May Concern:
On Wednesday, September 6, 2023, Slippery Rock University of Pennsylvania received your written request for records pursuant the Pennsylvania Right-to-Know Law ("RTKL"), 65 P.S. §§ 67.101 et. seq. By notice dated September 12, 2023, Slippery Rock University informed you of its intent to take a 30-day extension as permitted by law, 65 P.S. § 902.
This letter serves as the University's final response. Your request is granted in part and denied in part, as outlined below:
1) All contracts and statements of work between the university and outside parties for student predictive analytics services that the university used during the 2022-23 academic year.
* Granted. Attached, please find three (3) documents entitled, Service Purchase Contract 1, Service Purchase Contract 2 and Service Purchase Contract (Civitas).
Please note redactions have been made, as permitted by the RTKL, with respect to signatures and information deemed by the vendor to be Personal Identification Information (PII). 65 P. S. § 67.708(b)(6)(i)(A).
2) Records defining the input variables and their weights/relative power for each student predictive analytics service the university used during the 2022-23 academic year (In other words, records defining the data points fed into each predictive model and its respective impact on the model's output).
* Granted in Part. Attached, please find a Spreadsheet entitled, Input Information Provided to RNL, which provides information about the categories of input variables provided to RNL by the University.
* Denied in Part. The University does not have specific information pertaining to the weights/relative power of each input variable. This information is proprietary to RNL and is not shared with the University. Additionally, as regards input variables provided to Civitas, such information is exempt from disclosure under 65 P.S. § 67.708(b)(11) because in relation to that vendor's software, the requested records constitute confidential proprietary information.
3) Records documenting the accuracy (AUC, true/false positive, specificity, lift, etc.) of each student predictive analytics service the university used during the 2022-23 academic year.
* The requested records do not exist. 65 P.S. § 67.705.
4) Any validation studies, disparate impact studies, or other records documenting the student predictive analytics services the university used during the 2022-23 academic year performed for different student groups (e.g. Black students vs. White students, male students vs. female students).
* Denied pursuant to 65 P. S. §§ 67.708(b)(11) (confidential proprietary information) and 65 P. S. §§ 67.708(b)(10)(i)(A) (internal, predecisional deliberations). In Shannon v. Pa. Dep't of Educ., the Office of Open Records (OOR) found that, while communications from an agency's contractor were not internal, a report generated by the consultant for the agency's exclusive use that was not shared with anyone outside the agency qualified as internal. OOR Dkt. AP 2021-1351, 2021 PA O.O.R.D. 1797. The OOR noted that this is particularly the case when the records constitute "research, memos or other documents used in the predecisional deliberations." Id. (quoting 65 P.S. § 67.708(b)(10)(i)(A)); see also Wolfson v. Allegheny County, OOR Dkt. AP 2021-2372, 2021 PA O.O.R.D. 2529 (finding that a master spreadsheet of proposals that was internally compiled but discussed with an outside accounting firm to ensure eligibility qualified as internal). Specifically, in Shannon v. Pa. Dep't of Educ., the OOR ruled that a report authored by an outside third-party Department of Education contractor, which was used by the Department of Education for the purpose of internally deliberating policy and courses of conduct, was internal to the Department and therefore exempt from disclosure. OOR Dkt. AP 2021-1351, 2021 PA O.O.R.D. 1797.
As portions of your request were denied, you have the ability to appeal this response to:
Executive Director
Office of Open Records
333 Market St. 16th Floor
Harrisburg, Pennsylvania 17101-2234
If you choose to file an appeal you must do so within 15 business days of the mailing date of this response and send to the OOR: (1) this response; (2) your request; (3) the reasons why you think the record is public (a statement of the grounds you assert for the requested record being a public record); and (4) why you think the agency is wrong in its reasons for saying that the record is not public (a statement that addresses any ground stated by the agency for the denial, which you are challenging). The Office of Open Records has an appeal form available on its website at: https://www.openrecords.pa.gov/RTKL/Forms.cfm.
Please be advised this correspondence will serve to close this record with our office, as permitted by law.
Tina Moser
Agency Right-to-Know Officer
Slippery Rock University
104 Maltby Center, Suite 300
Slippery Rock, PA 16057
-
Input Information Provided to RNL - Headings
From: Slippery Rock University
Good morning,
RE: Re: Right to Know Law Request [No. 364]
Please be advised I have attached the CIVITAS contract as it was missed in the original response to you sent yesterday (10.12.23). My apologies for the oversight.
1) All contracts and statements of work between the university and outside parties for student predictive analytics services that the university used during the 2022-23 academic year.
* Granted. Attached, please find three (3) documents entitled, Service Purchase Contract 1, Service Purchase Contract 2 and Service Purchase
Contract (Civitas).
Please note redactions have been made, as permitted by the RTKL, with respect to signatures and information deemed by the vendor to be Personal Identification Information (PII). 65 P. S. § 67.708(b)(6)(i)(A).
Please be advised this correspondence will serve to close this record with our office, as permitted by law.
Tina Moser
Agency Right-to-Know Officer
Slippery Rock University
104 Maltby Center, Suite 300
Slippery Rock, PA 16057
-
Input Information Provided to RNL - Headings
Files
pages