Unified generator of intelligent tutoring

• US 20060024654A1
• Filed: 07/31/2004
• Published: 02/02/2006
• Est. Priority Date: 07/31/2004
• Status: Abandoned Application

• Current Assignee: Vladimir Antonovich Goodkovsky
• Original Assignee: Vladimir Antonovich Goodkovsky
• Inventors: Goodkovsky, Vladimir Antonovich
• Application Number:
  US10/909,101
• Publication Number:
  US 20060024654A1
• Time in Patent Office: Days
• US Class Current: 434/350
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

Abstract

The invention accelerates successful learning in a wide variety of existing and developing learning environments by generating the most effective dynamic adaptive tutoring tailored to a current learner model. It provides a full coverage of a basic tutoring functionality including passive and active tutoring manners, as well as presenting, testing and diagnosing modes. An innovative component of the invention, a unified generator of intelligent tutoring, deals exclusively with a logical aspect of tutoring leaving all media aspects to be realized by traditional components of tutoring systems. The generator represents a generic logical core (brain) of known specific intelligent tutoring systems comprising a reusable tutoring engine and a reusable tutoring knowledge/data framework including a reusable learner model. All together they transform traditionally sophisticated courseware authoring into a simple fill-in-frameworks routine and automatically generate intelligent tutoring in any specific learning environments including available educational, training, simulation, knowledge management and job support systems.

Claims

1. a method of tutoring a learner including:

a) Providing a tutoring system including

1) providing a media environment for physical supporting at least one learning activity of said learner;

2) providing a unified tutoring logic generator for making a plurality of tutoring decisions;

3) providing a media-logic converter

a. for executing said tutoring decisions in said media environment to support said learning activity of said learner and

b. for providing said logic generator with at least one report about said learning activity in said media environment;

4) associating said logic generator with said media environment by said media-logic converter;

b) tutoring the learner with said tutoring system by controlling over said learning activity of said learner in said media environment with said logic generator through said logic-media converter whereby said method completely separates media and logic of tutoring, enables unified logic-based generating of a specific media-dependent tutoring process, simplifying authoring, improving quality of the tutoring process and accelerating learning success;

2. a method as in claim 1, wherein said providing a logic generator for making a plurality of tutoring decisions including

a) providing a unified knowledge/data model referenced to said learner and said leaning activity including providing a memory for storing knowledge/data;

2) providing a unified reusable knowledge/data framework for representing specific knowledge/data in said memory;

3) providing said unified reusable knowledge/data framework with said specific knowledge/data;

b) providing a unified reusable tutoring engine including

1) providing a decision maker for making a plurality of tutoring decisions based upon said knowledge/data model;

2) providing a processor for adapting said knowledge/data model based upon at least one said learning, report about at least one said learning activity of at least one said learner;

c) associating said knowledge/data model with said tutoring engine;

whereby said method provides unified reusable components for building any specific tutoring system, excludes manual design of the tutoring process by authors, improves quality of said tutoring process and accelerates learning success;

3. a method as in claim 1, wherein said tutoring the learner with said tutoring system including

a) making tutoring decisions from said plurality of tutoring decisions by said decision maker based upon said unified knowledge/data model;

b) executing said tutoring decisions by said media-logic converter providing necessary control over said learning media environment;

c) supporting said learning activity of said learner by said media environment;

d) monitoring said learning activity and providing, said logic generator with at least one said report by said media-logic converter;

e) adapting said unified knowledge/data model by said processor including particularly updating said knowledge/data model based upon said report;

f) making new tutoring decisions from said plurality of tutoring decisions by said decision maker based upon adapted unified knowledge/data model;

whereby said method dynamically adapts said tutoring system, improves quality of said tutoring process and accelerates learning success;

4. a method as in claim 3, wherein said making tutoring decisions from said plurality of tutoring decisions including making a plurality of diagnostic decisions each revealing at least one cause of a fault behavior of said learner in said learning activity,

whereby said method enables focusing of the tutoring process on said cause of said fault behavior and corresponding acceleration of successful learning;

5. a method as in claim 4, wherein said adapting said knowledge/data model by said processor including revising said knowledge/data model based upon a diagnostic decision from said plurality of diagnosing decisions

whereby said method focuses the tutoring process on said cause of said fault behavior of said learner and accelerates successful learning;

6. a method as in claim 3, wherein said making tutoring decisions from said plurality of tutoring decisions by said decision maker including making a plurality of assignments from said plurality of tutoring decisions to said media environment through said media-logic converter to initiate respectively a plurality of extra learning activities of said learner,

whereby said method realizes an active manner of tutoring, eliminates prior manual sequencing of learning activities by authors, improves quality of sequencing and accelerates successful learning;

7. a method as in claim 6, wherein said making a plurality of assignments including

a) making an assignment from said plurality of assignments to supply progress of said learner;

b) making an assignment from said plurality of assignments to test progress of said learner and detect at least one fault behavior of said learner;

c) making an assignment from said plurality of assignments to diagnose at least one cause of said fault behavior of said learner,

whereby said tutoring method dynamically realizes supply, testing and diagnosing modes of active tutoring to accelerate learning progress;

8. a method as in claim 6, wherein said making a plurality of assignments including making a multiple assignment assigning a subset of the best learning activities from said plurality of extra learning activities for final choice of one learning activity by said learner

whereby said tutoring method supports mixed initiative learning/tutoring and accelerates learning progress;

9. a method as in claim 3, wherein said adapting including improving said knowledge/data model including

a) incrementing knowledge/data supported tutoring decisions justified by learning process;

b) decrementing knowledge/data supported tutoring decisions not justified by learning process;

whereby said tutoring method improves itself and accelerates learning progress;

10. a system for tutoring a learner comprising

a) a media environment for physical supporting at least one learning activity of said learner,

b) a unified logic generator for making a plurality of tutoring decisions;

c) a media-logic converter associated with said media environment and said logic generator for executing said tutoring decisions in said media environment and for providing said logic generator with at least one learning report about said learning activity of said learner in said media environment,

wherein said logic generator monitors and controls over said learning activity of said learner in said media environment through said media-logic converter,

whereby said system includes separated media and logic components, provides unified logic-based generating the specific media-dependent tutoring process, simplifies authoring, improves quality of said tutoring process and accelerates learning success;

11. a system for tutoring the learner as in claim 10, wherein said unified logic generator including

a) a unified knowledge/data model referenced to said learner and said learning activity including a memory for storing knowledge/data,

a unified reusable framework for representing specific knowledge/data in said memory;

3) said specific knowledge/data about said learner and said learning activity filled in said unified reusable framework.

b) a unified reusable tutoring engine including

1) a decision maker for making a plurality of tutoring decisions based upon said unified knowledge/data model;

2) a processor for adapting and particularly for updating said unified knowledge/data model based upon at least said learning report about at least said learning activity of at least said learner;

wherein said unified logic generator obtains said learning report about said learning activity of said learner in said media environment, adapts said unified knowledge/data model and makes said plurality of tutoring, decisions to control over said learning activity of said learner,

whereby said system provides unified reusable components for easy building any specific tutoring systems simplifies authoring, improves quality of said tutoring process and accelerates learning success,

12. a system as in claim 11, wherein said unified reusable framework including

a) a learning space framework for representing a logical space of said learning activity;

b) a learner data framework for representing said learner in said logical space;

whereby said unified reusable framework specifies a priori unknown generic structure of said tutoring knowledge/data model;

13. a system as in claim 12, wherein said learning space framework including at least

a) a behavioral space framework for representing essential traceable aspects of said learning activity including at least one said report;

b) a state space framework for representing untraceable aspects of said learning activity essential for making said plurality of tutoring decisions,

c) a state-behavior relation for associating said state space framework with said behavioral space framework;

whereby said learning space framework further specifies the generic structure of said tutoring knowledge/data and enables logical inference of untraceable aspects of the learning activity from traceable behavior;

14. a system as in claim 13, wherein said state space framework including

a) a plurality of learning objectives;

b) a plurality of possible achievement states of each learning objective from said plurality of learning objectives including at least

1) a no-achievement state,

2) a supplied achievement state and

3) a demonstrated achievement state;

wherein said no-achievement state can transit into said supplied achievement state and said supplied achievement state can transit into said demonstrated achievement state;

whereby said state space model further specifies a priori unknown generic structure of said tutoring knowledge/data about said untraceable aspects of said learning activity essential for making said plurality of tutoring decisions;

15. a system as in claim 12, wherein said learner data framework representing at least a plurality of beliefs corresponding to each learning objective from said plurality of learning objectives including at least

a) a no-achievement belief corresponding to said no-achievement state,

b) a supplied achievement belief corresponding to said supplied achievements state,

c) demonstrated achievement belief corresponding to said demonstrated achievement state,

whereby said beliefs flexibly position said learner into said state space framework;

16. a system as in claim 13, wherein said state-behavior relation for each learning objective from said plurality of learning objectives including

a) a local demonstrating belief associating a specific behavior from said behavioral space framework with said demonstrated achievement state of said learning objective;

b) a local supplying belief associating said specific behavior from said behavioral space framework with said supplied achievement state of said learning objective;

c) a local fault belief associating said specific behavior from said behavioral space framework with said no-achievement state of said learning objective;

whereby said state-behavior relation flexibly associates the expected cases of learning behavior with the learning states enabling logical inference of the learning states of said learner from the reported behavior of said learner in said learning media environment;

17. a system as in claim 11, wherein said decision maker including a strategic decision maker making particularly a plurality of diagnostic decisions each revealing at least one cause of a reported fault behavior of said learner in said learning media environment,

whereby said reusable tutoring engine enables focusing of the tutoring process on said cause of said fault behavior and corresponding acceleration of successful learning;

18. a system as in claim 17, wherein said processor including a reviser for revising said knowledge/data model based upon the diagnostic decision from said plurality of diagnosing decisions and focusing said logic generator on said cause of said fault behavior of said learner,

whereby said reviser focuses the whole tutoring system on said cause of said fault behavior of said learner and accelerates successful learning;

19. a system as in claim 11, wherein said decision maker including a tactic decision maker for making particularly a plurality of mode decisions including at least

a) a rule for setting up supply mode of tutoring;

b) a rule for setting up testing mode of tutoring;

c) a rule for setting up diagnosing mode of tutoring;

whereby said uniform tutoring engine dynamically adapts the mode of tutoring in order to accelerate successful learning;

20. a system as in claim 11, wherein said decision maker including an operative decision maker for assigning at least one best learning activity from said plurality of extra learning activities for the learner in each mode from said supply, testing and diagnosing modes,

whereby said logic generator eliminates prior manual sequencing of extra learning activities during authoring process, improve quality of said sequencing and accelerates successful learning in the tutoring stage.