System and method of using academic analytics of institutional data to improve student success

• US 8,412,736 B1
• Filed: 10/25/2010
• Issued: 04/02/2013
• Est. Priority Date: 10/23/2009
• Status: Active Grant

• Current Assignee: Purdue Research Foundation (Purdue University)
• Original Assignee: Purdue Research Foundation (Purdue University)
• Inventors: Campbell, John P., Arnold, Kimberly E.
• Primary Examiner(s): BURKE, JEFF A
• Application Number:
  US12/911,310
• Time in Patent Office: 890 Days
• Field of Search: 707/776, 707/942, 707/603, 706 20- 21
• US Class Current: 707/776
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06Q 10/00 Administration; Management

Abstract

Systems and methods that extend the use of data mining to identify students academically at risk of performing poorly or withdrawing from school altogether. In doing so, academically at-risk students are identified early and guided to resources to improve their academic performance.

Claims

1. A system for associating a student-specific categorization of risk of poor academic performance to a plurality of students in at least one academic course and informing a person of the risk categorization, the system comprising at least one computer having a processor and memory containing processor-executable instructions for:

collecting and storing data related to each student in computer memory, said data including a plurality of types of data including data selected from a first group including demographic data, personal data, extracurricular activity data, financial data, housing data, employment data, admission data, and registration data, and data selected from a second group including academic data, course management system data, course data, help resources data, technical log data, attendance data, tardiness data, unexcused absences data, workshop attendance data and training data;

permitting a user of the system to select a plurality of data types including data from the first group and from the second group, and to assign custom weights to the selected plurality of data types;

analyzing said data in a manner that takes into account the custom weight assigned to each type of data and results in a categorization of risk that a student will perform poorly academically in the at least one academic course;

associating a risk categorization with each student; and

informing a person of said risk categorization associated with the student through the computer.

2. The system of claim 1, wherein the step of informing a person of said risk categorization associated with the student through the computer includes selecting from computer memory and sending a pre-scripted email, voice message, or text message to the person.

3. The system of claim 1, wherein the at least one computer having a processor and memory containing processor-executable instructions further includes processor-executable instructions for presenting a graphical user interface allowing a person to define and adjust criteria for each of a plurality of categorizations of risk that the student will perform poorly academically.

4. The system of claim 1, wherein the at least one computer having a processor and memory containing processor-executable instructions further includes processor-executable instructions for presenting a graphical user interface allowing a person to specify whether or not the system may send a text message or voice mail to a phone associated with the person.

5. The system of claim 1, wherein the at least one computer having a processor and memory containing processor-executable instructions further includes processor-executable instructions for presenting a graphical user interface allowing a person to see how the categorization of risk associated with the student has changed over at least two time periods.

6. The system of claim 2, wherein the pre-scripted email, voice message, or text message is selected based on the risk categorization associated with the student and an amount of remaining opportunities to improve the academic performance of the student.

7. The system of claim 3, wherein the graphical user interface further allows the person to establish and adjust a weight to be assigned to each type of data in the criteria for each of the plurality of categorizations of risk.

8. The system of claim 3, wherein the graphical user interface further allows the person to define time increments and different criteria for each of the plurality of categorizations of risk for each time increment.

9. The system of claim 3, wherein the graphical user interface further allows the person to define the number of categorizations of risk.

10. The system of claim 3, wherein the graphical user interface presents in real time the categorization of risk to be associated with the student in response to any change in the criteria or data.

11. The system of claim 1, further comprising executable instructions for providing real-time feedback to the user of the system of the effect of the selected data types and custom weights on the risk categorization of a student as the data types are selected and custom weights are assigned by the user.

12. A method implemented by at least one computer having a processor and memory containing processor executable instructions, for associating a categorization of risk of poor academic performance to a plurality of students in at least one academic course and electronically informing a person of the risk categorization, comprising:

collecting and storing data related to each student in computer memory, said data including a plurality of types of data including data selected from a first group including demographic data, personal data, extracurricular activity data, financial data, housing data, employment data, admission data, and registration data, and data selected from a second group including academic data, course management system data, course data, help resources data, technical log data, attendance data, tardiness data, unexcused absences data, workshop attendance data and training data;

permitting a user of the system to select a plurality of data types including data from the first group and from the second group, and to assign custom weights to the selected plurality of data types;

analyzing said data in a manner that takes into account the custom weight assigned to each type of data and results in a categorization of risk that a student will perform poorly academically in the at least one academic course;

associating a risk categorization with the student; and

informing a person of said risk associating the risk categorization with the student through the computer.

13. The method of claim 12, further including presenting a graphical user interface allowing a person to define and adjust criteria for each of a plurality of categorizations of risk that the student will perform poorly academically.

14. The method of claim 12, further including presenting a graphical user interface allowing a person to specify whether or not the at least one computer may send a text message or voice mail to a phone associated with the person.

15. The method of claim 12, further including presenting a graphical user interface allowing a person to see how the categorization of risk associated with the student has changed over at least two time periods.

16. The method of claim 12, further including selecting the pre-scripted email, voice message, or text message is based on the risk categorization associated with the student and an amount of remaining opportunities to improve the academic performance of the student.

17. The method of claim 13, wherein the graphical user interface further allows the person to establish and adjust a weight to be assigned to each type of data in the criteria for each of the plurality of categorizations of risk.

18. The method of claim 13, wherein the graphical user interface further allows the person to define time increments and different criteria for each of the plurality of categorizations of risk for each time increment.

19. The method of claim 13, wherein the graphical user interface further allows the person to define the number of categorizations of risk.

20. The method of claim 13, wherein the graphical user interface presents in real time the categorization of risk to be associated with the student in response to any change in the criteria or data.

21. The method of claim 12, wherein the steps of collecting and storing data, and of analyzing said data occur continuously in real time as new data is generated.

22. The method of claim 12, further comprising providing real-time feedback to the user of the system of the effect of the selected data types and custom weights on the risk categorization of a student as the data types are selected and custom weights are assigned by the user.