System and method for a computerized learning system

• US 8,761,658 B2
• Filed: 01/31/2011
• Issued: 06/24/2014
• Est. Priority Date: 01/31/2011
• Status: Active Grant

• Current Assignee: Fast Track Technologies, Inc.
• Original Assignee: Fast Track Technologies, Inc.
• Inventors: Kim, Surrey, Kim, Borrey, Kouritzin, Michael Alexander, Rieger, Garret
• Primary Examiner(s): Utama, Robert J
• Application Number:
  US13/018,331
• Publication Number:
  US 20120196261A1
• Time in Patent Office: 1,240 Days
• Field of Search: None
• US Class Current: 434/350
• CPC Class Codes: G09B 5/062 Combinations of audio and printed presentations, e.g. magnetically striped cards, talking books, magnetic tapes with printed texts thereon
• 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

There is a computerized learning system and method which updates a conditional estimate of a user signal representing a characteristic of a user based on observations including observations of user behavior. The conditional estimate may be updated using a non-linear filter. Learning tools may be generated using the computerized learning system based on distributions of desired characteristics of the learning tools. The learning tools may include educational items and assessment items. The learning tools may be requested by a user or automatically generated based on estimates of the user'"'"'s characteristics. Permissions may be associated with the learning tools which may only allow delegation of permissions to other users of lower levels. The learning system includes a method for annotating learning tools and publishing those annotations. In-line text editors of scientific text allow users to edit and revised previously published documents.

Claims

1. A method of generating learning tools within a computerized learning system, comprising:

storing a plurality of learning tools within a database, each one of the plurality of learning tools being associated with a plurality of characteristics;

obtaining observations through interaction of a user with the computerized learning system;

using a non-linear filter to update a conditional estimate of a signal representing a characteristic of the user based on the observations;

receiving a request in the form of a distribution of desired characteristics; and

using the conditional estimate of the signal, generating a subset of the plurality of learning tools having a plurality of characteristics satisfying the requested distribution of desired characteristics.

2. The method of claim 1 further comprising transmitting the generated subset of the plurality of learning tools to at least the user or a second user.

3. The method of claim 1 in which the distribution of desired characteristics are selected by the user.

4. The method of claim 1 in which generating a subset of the plurality of learning tools having a plurality of characteristics satisfying the distribution of desired characteristics is repeated for a plurality of users and in which the generated subsets of the plurality of learning tools for each of the plurality of users are unique.

5. The method of claim 4 in which each of the plurality of learning tools are generated after the occurrence of an event.

6. The method of claim 5 where the event is triggered by one of the plurality of users selecting an option.

7. The method of claim 6 in which selecting an option comprises clicking a labelled button on a display.

8. The method of claim 5 in which each of the plurality of learning tools are generated simultaneously for each user.

9. The method of claim 4 in which a locked subset of the subset of the plurality of learning tools is included within each of the subsets of the plurality of learning tools being generated for each of the plurality of users.

10. The method of claim 1 in which the subset of the plurality of learning tools are generated through a request by an individual user and the method further comprising distributing the plurality of generated learning tools to a plurality of additional users.

11. The method of claim 1 in which generating the subset of the plurality of learning tools satisfying the distribution of desired characteristics further comprises re-publishing a pre-existing subset of the plurality of learning tools which satisfies the distribution of desired characteristics.

12. The method of claim 1 in which the requested distribution of desired characteristics comprises scheduling characteristics.

13. The method of claim 1 in which the generated subset of the plurality of learning tools comprises courseware.

14. The method of claim 13 in which the courseware comprises a collection of both educational items and assessment items.

15. The method of claim 1 in which a previously generated plurality of learning tools has been generated before the at least one of the learning tool and the report is generated, and in which the distribution of desired characteristics is selected based on the previously generated plurality of learning tools.

16. The method of claim 1 in which the distribution of desired characteristics is selected based on a normalized filter variance distribution.

17. The method of claim 1 in which the distribution of desired characteristics is selected based on a determined probability of causing a desired action based on a filter estimate and model.

18. The method of claim 1 in which the distribution of desired characteristics is selected based on an optimal action strategy model.

19. The method of claim 1 in which the signal also represents a characteristic of a learning tool.