PREDICTING STUDENT RETENTION USING SMARTCARD TRANSACTIONS

• US 20180144352A1
• Filed: 03/08/2017
• Published: 05/24/2018
• Est. Priority Date: 03/08/2016
• Status: Abandoned Application

• Current Assignee: Arizona Board of Regents (University of Arizona)
• Original Assignee: Arizona Board of Regents (University of Arizona)
• Inventors: RAM, SUDHA, WANG, Yun, CURRIM, Sabah Ahmed, CURRIM, Faiz Ahmed
• Application Number:
  US15/453,668
• Publication Number:
  US 20180144352A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 17/12 Simultaneous equations , e.g. systems of linear equations
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/20 Education

Abstract

Systems and methods for analyzing student retention rates are disclosed. The systems and methods disclosed construct networks of students based on data associated with financial transactions conducted by those students. The systems and methods analyze the networks of students to calculate network features associated with the networks and utilize those network features to forecast student retention. The network features analyzed include node appearance metrics, degree metrics, and edge metrics. The systems and methods may also utilize campus integration metrics calculated from data associated with financial transactions conducted by students to forecast student retention.

Claims

1. A non-transitory computer-readable medium that stores a program for analyzing student retention rates, that when executed, causes a processor to:

receive input of a plurality of financial transaction variables associated with a plurality of students;

aggregate the plurality of financial transaction variables into a network of students, wherein a connection between students in the network represents a latent relationship;

calculate a plurality of network features based on the connections between students, wherein said network features indicate the students'"'"' integration; and

forecast retention for each of the plurality of students.

2. The non-transitory computer-readable medium of claim 1, wherein the network features are comprised of node appearance metrics, degree metrics, and edge metrics.

3. The non-transitory computer-readable medium of claim 2, wherein the node appearance metrics are further comprised of number of appearance periods and longest consecutively appearance periods.

4. The non-transitory computer-readable medium of claim 2, wherein the degree metrics are further comprised of average degree, standard deviation of degrees, and ratio of average degree between a first half of networks and a second half of networks.

5. The non-transitory computer-readable medium of claim 2, wherein the edge metrics are further comprised of a proportion of strong out-going edges, a proportion of strong in-coming edges, and a proportion of loyal edges.

6. The non-transitory computer-readable medium of claim 1, wherein the data related to a financial card transaction is comprised of a student identifier, a service type, a location indicator, and a timestamp.

7. The non-transitory computer-readable medium of claim 1, wherein the processor is further programmed to calculate a plurality of campus integration metrics using the plurality of financial transaction variables, wherein said campus integration metrics are used to forecast retention rates for each of the plurality of students.

8. A computer-implemented method for analyzing student retention rates comprising the steps of:

receiving input of a plurality of financial transaction variables associated with a plurality of students;

aggregating the plurality of financial transaction variables into a network of students, wherein a connection between students in the network represents a latent relationship;

calculating a plurality of network features based on the connections between students, wherein said network features indicate the students'"'"' integration; and

forecasting retention for each of the plurality of students.

9. The computer-implemented method of claim 8, wherein the network features are comprised of node appearance metrics, degree metrics, and edge metrics.

10. The computer-implemented method of claim 9, wherein the node appearance metrics are further comprised of number of appearance periods and longest consecutively appearance periods.

11. The computer-implemented method of claim 9, wherein the degree metrics are further comprised of average degree, standard deviation of degrees, and ratio of average degree between a first half of networks and a second half of networks.

12. The computer-implemented method of claim 9, wherein the edge metrics are further comprised of a proportion of strong out-going edges, a proportion of strong in-coming edges, and a proportion of loyal edges.

13. The computer-implemented method of claim 8, wherein the data related to a financial card transaction is comprised of a student identifier, a service type, a location indicator, and a timestamp.

14. The computer-implemented method of claim 8, further comprising the step of calculating a plurality of campus integration metrics using the plurality of financial transaction variables, wherein said campus integration metrics are used to forecast retention for each of the plurality of students.

15. A non-transitory computer-readable medium that stores a program that causes a processor to:

receive input of a plurality of financial transaction variables associated with a plurality of students;

calculate a spatial sequence model using the input, said calculation taking the form of;

16. The non-transitory computer-readable medium of claim 15, further comprising calculating a temporal sequence model, said calculation taking the form of:

17. The non-transitory computer-readable medium of claim 15, further comprising calculating a social influence model, said calculation taking the form of:

18. The non-transitory computer-readable medium of claim 17, wherein the data related to a financial card transaction is comprised of a student identifier, a service type, a location indicator, and a timestamp.

19. The non-transitory computer-readable medium of claim 17, wherein said model is used to forecast student retention at a facility of higher learning.

20. The non-transitory computer-readable medium of claim 17, wherein said model is used to create a model of implicit social networks between the plurality of students.