Associate a learner and learning content

Patent metadata

Field	Value
Publication number	US-2017193620-A1
Application number	US-201415315241-A
Country	US
Kind code	A1
Filing date	May 30, 2014
Priority date	May 30, 2014
Publication date	Jul 6, 2017
Grant date	—

Abstract

Examples disclosed herein relate to associating a learner and learning content. A processor determines a learning type cluster based on clustering of learning content attributes and learner attributes based on historical pairings of content and learners and information about outcomes of the pairings. The processor may associate a piece of learning content and a learner based on the learning type clusters and output information about the association.

First claim

1 . A computing system, comprising:
a storage to store historical learning information, wherein the historical learning information includes learner attribute information and learning content attribute information and previous result information associated with combinations of learners and learning content; and a processor to:
determine learning type clusters based on associations between learn attribute information and learning content attribute information based on the historical learning information;
associating a learner with the learning content based on a comparison of the degree to which a piece of learning content is associated with a learning type cluster and the degree to which a learner is associated with the learning type cluster; and
outputting information about the associated between the learning content and learner.

2 . The computing system of claim 1 , wherein the processor determines the content type of the piece of content and determines the learning content attribute information based on the type of content.

3 . The computing system of claim 1 , wherein the processor further selects a position to order the associated learner content among other learning content associated with the learner.

4 . The computing system of claim 1 , wherein associating a learner with learning content comprises comparing a learner learning profile associated with the learner to a learning content learning profile associated with the learning content,
wherein the learner learning profile includes attributes associated with the learner and the degree to which the individual learner attributes are associated with the learning type, and wherein the learning content learning profile includes attributes associated with the learning content and the degree to which the individual learning content attributes are associated with the learning type.

5 . The computing system of claim 1 , wherein the processor is further to associate the learning content with a second piece of learning content based on the degree to which the learning content is associated with the learning type and the degree to which the second piece of learning content is associated with the learning type.

6 . A method, comprising:
determining a learning type cluster of learners and learning content based on past combinations of learners and learning content and the associated outcomes, wherein the learning type clusters include attributes based on the attributes of the learners and attributes of the learning content within the learning type cluster; associating a weight with learning content indicating the degree o which the learning content is associated with a learning type cluster; associating a weight with a learner indicating the degree to which the learner is associated with the learning type cluster; associating the learner and learning content based on the learning content weight and the learner weight associated with the learning type; and output information about the associated learner and content.

7 . The method of claim 7 , wherein determining learning type clusters comprises determining clusters based on similar outcomes.

8 . The method of claim 7 , wherein determining learning type clusters comprise disregarding a learner and teaming type combination when determining a learning type cluster where the outcome associated with the combination is less than a threshold.

9 . The method of claim 7 , wherein associating the learner and learning content comprises a a comparison based on learner attributes of the learner and the association of the learner attributes with the learning type and learning content attributes of the learning content and the association of the learning content attributes with the learning content.

10 . The method of claim 7 , further comprising, updating the learning type clusters based on feedback related to new learner and learning content combinations.

11 . The method of claim 7 , further comprising associating a semantic label with a learning type cluster.

12 . The method of claim 7 , further comprising associating a second piece of learning content with the learning content based on the learning type.

13 . A machine-readable non-transitory storage medium comprising instructions executable by a processor to:
determine learning type clusters based on clustering of learning content, attributes and learner attributes based on historical pairings of content and learners and information about outcomes of the pairings: score a relationship between the learning content for a learner based on a multidimensional comparison of a learner to a piece of learning content according to learning type associations with the learner and learning type associations with the piece of learning content; and output information about the score.

14 . The machine-readable non-transitory storage medium of claim 12 , wherein the multidimensional comparison comprises a comparison based on learner attributes of the learner and the association of the learner attributes with the learning type and learning content attributes of the learning content and the association of the learning content attributes with the learning content.

15 . The machine-readable non-transitory storage medium of claim 12 , where instructions to output information about the score comprise instructions to output at least one of a selection of learners associated with learning content and output recommended learning content to a learner.

Assignees

• Hewlett Packard Development Co Lp

Inventors

• Chatow Ehud
• Koutrika Georgia

Classifications

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title

• G06Q50/20 Primary

  Education · CPC title

• G09B5/06

  with both visual and audible presentation of the material to be studied · CPC title

• G06Q10/067

  Enterprise or organisation modelling · CPC title

• G09B7/00

  Electrically-operated teaching apparatus or devices working with questions and answers (mechanically operated G09B3/00; computing arrangements G06F) · CPC title