Learning system self-optimization

Patent metadata

Field	Value
Publication number	US-9412281-B2
Application number	US-201314089432-A
Country	US
Kind code	B2
Filing date	Nov 25, 2013
Priority date	Nov 25, 2013
Publication date	Aug 9, 2016
Grant date	Aug 9, 2016

Abstract

A method/system for learning system self-optimization is disclosed. The learning system can include a plurality of learning objects that are connected to each other by a plurality of learning vectors. The learning vectors can identify a prerequisite relationship between the connected learning objects and include data indicating a likelihood of success of a student in traversing the learning vector and/or an expected speed for traversing the learning vector. Data generated from a student's traversal of one of the learning vectors can be used to strengthen or weaken the traversed learning vector based on the student experience.

First claim

What is claimed is:

1. A method of maintaining a learning vector comprising:
automatically generating a learning object network, wherein generating the learning object network comprises:
receiving a plurality of content objects from a plurality of data sources via a network at a processor, wherein each of the learning objects comprises an aggregation of learning content that is associated with an assessment;
automatically identifying on the processor a first learning object, wherein the first learning object comprises a first aggregation of learning content, wherein the aggregation of learning content is associated with an assessment;
automatically identifying on the processor a second learning object, wherein the second learning object comprises a second aggregation of learning content, wherein the aggregation of learning content is associated with an assessment;
automatically identifying on the processor a prerequisite relationship between the first learning object and the second learning object, wherein the first learning object is a prerequisite to the second learning object;
automatically generating on the processor a learning vector connecting the first learning object and the second learning object, the learning vector comprising a direction and a strength, wherein the direction of the learning vector indicates that the first learning object is a prerequisite of the second learning object, and the strength of the learning vector indicates a likelihood of a student successfully traversing the learning vector, wherein the strength of the learning vector varies with respect to a student context, wherein the student context describes an attribute of the student; and
repeating the generating of learning vectors until the plurality of content objects are connected;

receiving an input from a first student device via the network;
automatically identifying a student user of the first student device based on the received input and information retrieved from a student database;
automatically retrieving information relating to a plurality of learning object networks;
automatically identifying with the processor one of the plurality of learning object networks relevant to the student user of the first student device;
automatically receiving at the processor an input indicative of the traversal of the learning vector, wherein the input is received from the first student device via a network, wherein the input identifies a student property and the effectiveness of the learning vector;
automatically determining on the processor a value according to a Boolean function, wherein the value comprises a first value when the input indicative of the traversal of the learning vector indicates a desired outcome and wherein the value comprises a second value when the input indicative of the traversal of the learning vector indicates an undesired outcome, and
automatically determining that the student context of the student corresponds to a context of a first at least one student that previously traversed the learning vector when the first value is determined;
automatically strengthening the learning vector when the first value is determined and when the student context of the student corresponds to the context of the first at least one student that previously traversed the learning vector;
receiving an identifier from a second student device via a network;
automatically identifying a second student context associated with the user of the second student device;
automatically varying the magnitudes of the learning vectors connecting the plurality of content objects based on the second student context;
automatically providing the first learning object to the second student device via a network, wherein the second student device is selected for receipt of the first learning object based on the learning vector and student information retrieved by the processor from the student database when the learning vector is strengthened;
receiving an indicator of completion of the provided first learning object; and
automatically generating and sending a communication to the second student device, wherein the communication comprises an enhancement object automatically triggered for providing to the student via a threshold, wherein the enhancement object is outside of the learning path of the provided first learning object, and wherein the communication activates a user interface of the second student device to provide the enhancement object to the user via a screen of the second student device.

2. The method of claim 1 , further comprising retrieving a success threshold, wherein the success threshold is a value, the attainment of which indicates success or failure in the traversal of the learning vector.

3. The method of claim 2 , wherein the success threshold comprises a plurality of thresholds.

4. The method of claim 3 , wherein some of the thresholds delineate between successful traversal of the learning vector and unsuccessful traversal of the learning vector with respect to different success metrics.

5. The method of claim 3 , wherein the learning object comprises a plurality of content objects, each content object containing a portion of the learning content of the learning object.

6. The method of claim 5 , wherein each of the content objects of the learning object is associated with at least one of the some of the plurality of success metrics.

7. The method of claim 4 , wherein at least one of the some of the plurality of success metrics is indicative of mastery of the lexile content of the learning object.

8. The method of claim 4 , wherein at least one of the some of the plurality of success metrics is an indicator of mastery of the quantile content of the learning object.

9. The method of claim 4 , further comprising generating the assessment associated with the second learning object, wherein the assessment comprises a plurality of questions directed to the success metrics.

10. The method of claim 2 , wherein determining a value according to a Boolean function further comprises comparing the input indicative of the traversal of the learning vector to the success threshold; and
wherein the value is assigned when the comparison of the input indicative of the traversal of the learning vector to the success threshold indicates a successful traversal of the learning vector.

11. The method of claim 1 , wherein the first at least one student that previously traversed the learning vector successfully traversed the learning vector.

12. The method of claim 1 , further comprising:
determining if the student context corresponds to the student context of a second at least one student that previously traversed the learning vector if the second value is determined; and
weakening the learning vector if the second value is determined and if the student context of the student corresponds to the context of the second at least one student that previously traversed the learning vector.

13. The method of claim 12 , wherein the second at least one student that previously traversed the learning vector unsuccessfully traversed the learning vector.

14. The method of claim 1 , further comprising updating the student context to strengthen the identification of a student learning style indicated in the student context; and matching the learning style indicated in the context of the first at least one student that previously traversed the learning vector if the first value is determined and if the student context of the student corresponds to the context of the first at least one student that previously traversed the learning vector.

15. A method of

Assignees

• Pearson Education Inc

Inventors

• Spagnola Perry M

Classifications

• G09B7/04 Primary

  characterised by modifying the teaching program in response to a wrong answer, e.g. repeating the question or supplying a further explanation · CPC title

• G09B7/02

  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 · CPC title