Intelligent tutoring method and system

• US 5,597,312 A
• Filed: 05/04/1994
• Issued: 01/28/1997
• Est. Priority Date: 05/04/1994
• Status: Expired due to Term

• Current Assignee: Qwest Communications International Incorporated (Lumen Technologies, Inc.)
• Original Assignee: US West Technologies, Inc.
• Inventors: Bloom, Charles P., Norton, Edwin H., Linton, Franklyn N. Jr., Haines, Mark H., Bell, Brigham R.
• Primary Examiner(s): CHENG, JOE H
• Application Number:
  US08/237,648
• Time in Patent Office: 1,000 Days
• Field of Search: 434/118, 434/156, 434/169, 434/307 R, 434/308, 434/322, 434/323, 434/327, 434/362, 434/365, 434/219, 434/335, 395/100, 395/927, 395/154, 395/375, 395/650, 364/419.1, 364/419.19
• US Class Current: 434/362
• CPC Class Codes: G09B 7/04 characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation

Abstract

A computer based intelligent method and system for tutoring a student in an interactive application. The method and system include a computer system for selecting a mode for an adjustable teaching parameter, generating a student model, and monitoring a student interactive task based upon the teaching parameter and the student model. The method and system also include a computer system for generating an updated student model based upon a student response to the student interactive task generated, and monitoring a student interactive task based upon the teaching parameter and the updated student model.

Claims

1. A computer based intelligent method for tutoring a student in an interactive application, the method comprising:

providing a teaching parameter having a plurality of adjustable factors;

selecting a contribution percentage for each of the plurality of adjustable factors of the teaching parameter;

generating a student model;

monitoring a student interactive task based upon the teaching parameter and the student model;

generating an updated student model based upon a student response to the student interactive task generated; and

monitoring a student interactive task based upon the teaching parameter and the updated student model.

2. The method of claim 1 wherein the teaching parameter having a plurality of adjustable factors comprises a topic parameter having a sequential factor, a last studied relationship factor, and a student proficiency factor.

3. The method of claim 1 wherein the teaching parameter having a plurality of adjustable factors comprises a student/contact conversation parameter having a selected topic factor, a complexity factor, a student interaction factor, a student proficiency factor, a teacher preference factor, and a student preference factor.

4. The method of claim 1 wherein the teaching parameter having a plurality of adjustable factors comprises a student parameter having an average correct factor, a consecutive correct factor, and an observe factor.

5. The method of claim 1 wherein the teaching parameter having a plurality of adjustable factors comprises a question probability parameter having an in-topic component and an out-of-topic component, each in-topic and out-of-topic component having a question when known factor, a question when unknown factor, and a decrease questioning when known factor.

6. The method of claim 1 wherein generating an updated student model comprises:

providing a student model data structure representing a plurality of student interactive tasks to be learned;

mapping the student response to the student model data structure; and

processing the student model data structure to determine a confidence estimate for each of the plurality of student interactive tasks to be learned.

7. The method of claim 1 wherein monitoring a student interactive task based upon the teaching parameter and the updated student model comprises:

generating a student/contact situation; and

recording the student action responsive to the student/contact situation.

8. The method of claim 7 wherein generating a student/contact situation comprises:

selecting one of a plurality of student/contact conversations, each conversation having a contact dialog component and a contact service component; and

synthesizing a student audible version of the contact dialog component.

9. The method of claim 8 wherein the adjustable teaching parameter comprises a student practice parameter having a contact dialog component and a contact service component, each contact dialog and contact service component having an observe mode, a focused practice mode, and a full practice mode.

10. A computer based intelligent system for tutoring a student in an interactive application, the system comprising:

a teaching parameter having a plurality of adjustable factors;

means for selecting a contribution percentage for each of the plurality of adjustable factors of the teaching parameter;

means for generating a student model;

means for monitoring a student interactive task based upon the teaching parameter and the student model;

means for generating an updated student model based upon a student response to the student interactive task generated; and

means for monitoring a student interactive task based upon the teaching parameter and the updated student model.

11. The system of claim 10 wherein the teaching parameter having a plurality of adjustable factors comprises a topic parameter having a sequential factor, a last studied relationship factor, and a student proficiency factor.

12. The system of claim 10 wherein the teaching parameter having a plurality of adjustable factors comprises a student/contact conversation parameter having a selected topic factor, a complexity factor, a student iteration factor, a student proficiency factor, a teacher preference factor, and a student preference factor.

13. The system of claim 10 wherein the teaching parameter having a plurality of adjustable factors comprises a student parameter having an average correct factor, a consecutive correct factor, and an observe factor.

14. The system of claim 10 wherein the teaching parameter having a plurality of adjustable factors comprises a question probability parameter having an in-topic component and an out-of-topic component, each in-topic and out-of-topic component having a question when known factor, a question when unknown factor, and a decrease questioning when known factor.

15. The system of claim 10 wherein the means for generating an updated student model comprises:

a student model data structure representing a plurality of student interactive tasks to be learned;

means for mapping the student response to the student model data structure; and

means for processing the student model data structure to determine a confidence estimate for each of the plurality of student interactive tasks to be learned.

16. The system of claim 10 wherein the means for monitoring a student interactive task based upon the teaching parameter and the updated student model comprises:

means for generating a student/contact situation; and

means for recording the student action responsive to the student/contact situation.

17. The system of claim 16 wherein the means for generating a student/contact situation comprises:

means for selecting one of a plurality of student/contact conversations, each conversation having a contact dialog component and a contact service component; and

means for synthesizing a student audible version of the contact dialog component.

18. The system of claim 17 wherein the adjustable teaching parameter comprises a student practice parameter having a contact dialog component and a contact service component, each contact dialog and contact service component having an observe mode, a focused practice mode, and a full practice mode.