View source for 20240029185. APPARATUS FOR IMPROVING APPLICANT SELECTION BASED ON PERFORMANCE INDICES simplified abstract (East Carolina University)

=APPARATUS FOR IMPROVING APPLICANT SELECTION BASED ON PERFORMANCE INDICES=


==Organization Name==

[[:Category:East Carolina University|East Carolina University]]


[[Category:East Carolina University]]

==Inventor(s)==

[[:Category:Robert Todd Watkins, Jr. of Chapel Hill NC (US)|Robert Todd Watkins, Jr. of Chapel Hill NC (US)]][[Category:Robert Todd Watkins, Jr. of Chapel Hill NC (US)]]

==APPARATUS FOR IMPROVING APPLICANT SELECTION BASED ON PERFORMANCE INDICES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240029185 titled 'APPARATUS FOR IMPROVING APPLICANT SELECTION BASED ON PERFORMANCE INDICES

==Simplified Explanation==

The patent application describes a system, methods, and computer program products for determining the application status of an applicant for an educational program. Here is a simplified explanation of the abstract:

* The system receives cohort performance data, which includes outcomes achieved by participants in the educational program.
* The system also receives applicant performance data, which includes data entries for the applicant.
* It calculates adjusted cohort performance data based on the cohort performance data and characteristics of the first data entries.
* A predictor model is provided based on the adjusted cohort performance data and the outcomes.
* Predictive parameters of the first data characteristics are sequentially changed to create second data characteristics.
* An adjusted predictor model is created based on the second data characteristics and the outcomes.
* Adjusted applicant performance data is calculated based on the applicant performance data and the second data characteristics.
* A probability of success for the applicant in the educational program is calculated based on the adjusted applicant performance data and the adjusted predictor model.

Potential applications of this technology:

* Educational institutions can use this system to determine the likelihood of an applicant's success in their educational program.
* It can be used to make informed decisions regarding admissions and scholarships.
* The system can help identify applicants who may need additional support or resources to succeed in the program.

Problems solved by this technology:

* The system solves the problem of determining an applicant's potential for success in an educational program based on relevant data.
* It eliminates the need for manual evaluation of each applicant's qualifications and performance.

Benefits of this technology:

* The system provides a more objective and data-driven approach to evaluating applicants.
* It saves time and resources by automating the evaluation process.
* Educational institutions can make more informed decisions about admissions and allocate resources more effectively.


==Original Abstract Submitted==

systems, methods, and computer program products for determining an application status of an applicant for an educational program may include receiving cohort performance data comprising first data entries for participants that have respectively achieved outcomes for the educational program and applicant performance data comprising second data entries for the applicant, calculating adjusted cohort performance data based on the cohort performance data and first data characteristics, providing a predictor model based on the adjusted cohort performance data and the outcomes, sequentially changing predictive parameters of the first data characteristics to create second data characteristics and creating an adjusted predictor model based on the second data characteristics and the outcomes, calculating adjusted applicant performance data based on the applicant performance data and the second data characteristics, and calculating a probability of success for the applicant in the educational program based on the adjusted applicant performance data and the adjusted predictor model.



[[Category:G06Q50/20]]
[[Category:G06F17/18]]
[[Category:G06Q10/0639]]
[[Category:G06F16/901]]
[[Category:G06F16/904]]
[[Category:G06F16/906]]