Custom electronic learning system and method

• US 7,165,054 B2
• Filed: 09/16/2002
• Issued: 01/16/2007
• Est. Priority Date: 09/14/2001
• Status: Expired due to Fees

• Current Assignee: Thinkclear, Inc. (The Boeing Co.)
• Original Assignee: Thinkclear, Inc. (The Boeing Co.)
• Inventors: Geoghegan, David
• Primary Examiner(s): Knight; Anthony
• Assistant Examiner(s): Holmes; Michael B.
• Application Number:
  US10/244,266
• Publication Number:
  US 20030055800A1
• Time in Patent Office: 1,583 Days
• Field of Search: 706/45, 706/16, 434/362
• US Class Current: 706/16
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F

Abstract

A system and method for generating a custom learning object. The system and method generate the custom learning object based on a knowledge object and a set of user characteristics for a user. In one embodiment, the knowledge object is converted to a set of knowledge atoms. Each knowledge atom is then mapped to a container defining an output format. One or more containers are combined to define the custom learning object.

Claims

1. A custom electronic learning system, comprising:

a characteristic system for defining a set of user characteristics for a user;

a conversion system for converting a knowledge object into a set of knowledge atoms, wherein each knowledge atom includes;

learning data based on a portion of information in the knowledge object;

a type attribute describing the learning data; and

a level attribute representing a measure of detail of the learning data; and

a compiler system for generating a learning object based on the set of user characteristics and the set of knowledge atoms.

2. The system of claim 1, wherein the user characteristics include at least one of:

a learning style, a level of detail, and system attributes.

3. The system of claim 1, wherein the set of knowledge atoms is stored as a tree structure.

4. The system of claim 3, wherein the compiler system includes an engine for walking the tree structure.

5. The system of claim 1, wherein the compiler system filters the set of knowledge atoms used to generate the learning object based on the level attribute of each knowledge atom.

20. The system of claim 1, wherein the learning object comprises an electronic learning object for presentation to the user.

6. A custom electronic learning system, comprising:

a characteristic system for defining a set of user characteristics for a user;

a conversion system for converting a knowledge object into a set of knowledge atoms; and

a compiler system for generating a learning object based on the set of user characteristics and the set of knowledge atoms, wherein the compiler system includes;

a set of containers, wherein each container defines an output format;

a container manager for managing containers, wherein the learning object is based on at least one container; and

a set of handlers, wherein each handler maps a knowledge atom into at least one of the set of containers.

7. The system of claim 6, wherein the compiler system further includes a set of presentation sheets for defining a mapping of a type of knowledge atom to a container, wherein each handler uses at least one presentation sheet to perform the mapping.

8. A method of generating a custom electronic learning object, comprising:

receiving a knowledge object that includes information for presentation to a user;

defining a set of user characteristics for the user;

creating a set of knowledge atoms based on the knowledge object, wherein each knowledge atom includes;

learning data based on a portion of the information in the knowledge object; and

a type attribute describing the learning data; and

generating the learning object based on the set of knowledge atoms and the set of user characteristics.

14. The method of claim 8, further comprising storing the set of knowledge atoms in a tree structure.

15. The method of claim 14, wherein the generating step includes walking the tree structure to handle each knowledge atom.

9. A method of generating a custom electronic learning object, comprising:

receiving a knowledge object;

defining a set of user characteristics for a user, wherein the defining step includes;

assigning default values for at least one of the user characteristics; and

selecting a value for at least one of the user characteristics;

creating a set of knowledge atoms based on the knowledge object, wherein each knowledge atom includes;

learning data based on a portion of information in the knowledge object; and

a type attribute describing the learning data; and

generating the learning object based on the set of knowledge atoms and the set of user characteristics.

10. The method of claim 9, wherein the defining step further includes testing the user to determine at least one of the user characteristics.

11. A method of generating a custom electronic learning object, comprising:

receiving a knowledge object;

defining a set of user characteristics for a user;

creating a set of knowledge atoms based on the knowledge object, wherein each knowledge atom includes;

learning data based on a portion of information in the knowledge object; and

a type attribute describing the learning data; and

a level attribute representing a measure of detail of the learning data; and

generating the learning object based on the set of knowledge atoms and the set of user characteristics, wherein the generating step filters the set of knowledge atoms based on the level attribute.

12. A method of generating a custom electronic learning object, comprising:

receiving a knowledge object;

defining a set of user characteristics for a user;

creating a set of knowledge atoms based on the knowledge object, wherein each knowledge atom includes;

learning data based on a portion of information in the knowledge object; and

a type attribute describing the learning data; and

generating the learning object based on the set of knowledge atoms and the set of user characteristics, wherein the generating step includes;

providing a set of containers, wherein each container defines an output format; and

providing a set of handlers, wherein each handler maps a knowledge atom having a particular type attribute into at least one of the set of containers.

13. The method of claim 12, wherein, for each knowledge atom, the generating step further includes:

selecting one of the set of containers based on the type attribute;

selecting one of the set of handlers based on the type attribute;

mapping the knowledge atom into the selected container using the selected handler; and

adding the mapped container into the learning object.

16. A system for generating a custom electronic learning object, comprising:

a set of user characteristics for a user including a learning style;

a set of knowledge atoms, wherein each knowledge atom includes learning data; and

a compiler system for generating the learning object, the compiler system including a set of containers, wherein each container defines an output format; and

wherein the learning object comprises each knowledge atom mapped into at least one container based on at least the learning style.

17. The system of claim 16, further comprising a characteristic system for defining the set of user characteristics.

18. The system of claim 16, further comprising a conversion system for converting a knowledge object into the set of knowledge atoms.

19. The system of claim 16, wherein the compiler system further includes:

a set of handlers for mapping the set of knowledge atoms into the set of containers; and

a set of presentation sheets, wherein each presentation sheet defines a mapping of a type of knowledge atom to a handler.