Recommending Competitive Learning Objects

• US 20130266922A1
• Filed: 06/12/2013
• Published: 10/10/2013
• Est. Priority Date: 01/15/2010
• Status: Active Application

• Current Assignee: University of Phoenix Incorporated
• Original Assignee: Apollo Group Incorporated
• Inventors: Needham, Catherine, Menon, Satish, Wheeler, Willie, Muthukumarasamy, Jayakumar, Saha, Partha, Katz, Nitzan, Honea, Adam, Kelsey, Marla, Carmargo, Jorge
• Application Number:
  US13/916,348
• Publication Number:
  US 20130266922A1
• Time in Patent Office: Days
• US Class Current: 434/350
• 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
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F

Abstract

A method and apparatus for competitive learning objects in a learning environment is provided. Based on the success that a student has with a particular learning item, a learning recommendation is generated for another student.

Claims

1. A method, comprising:

receiving, from a user of a plurality of users, a learning recommendation request for a learning object that is designed to teach a particular skill;

in response to receiving the learning recommendation request, requesting input from two or more learning system modules;

receiving module-selected learning recommendations from each of the two or more learning system modules; and

generating, for the user, a learning recommendation based, at least in part, on one or more of the module-selected learning recommendations;

wherein the learning recommendation identifies a particular learning object that is associated with a content item that is designed to teach the particular skill; and

wherein the method is performed by one or more computing devices.

2. The method of claim 1, further comprising automatically increasing the influence of a first learning system module of the two or more learning system modules relative to a second learning system module of the two or more learning system modules based, at least in part, on merit determined for previous learning recommendations of the first learning system module.

3. The method of claim 2, wherein the merit determined for the previous learning recommendations of the first learning system module is based, at least in part, on one or more of:

a speed at which a user progresses;

or

a speed with which the first learning system module produces module-selected learning recommendations.

4. The method of claim 1, further comprising:

receiving information designating a first learning system module of the two or more learning system modules to be a primary module used for the particular skill; and

assigning greater importance to a response from the first learning system module than to responses from other learning system modules in generating the learning recommendation.

5. The method of claim 4, further comprising:

prior to receiving the information designating the first learning system module to be a primary module used for the particular skill, automatically increasing the influence of a second learning system module of the two or more learning system modules relative to the first learning system module based, at least in part, on merit determined for previous learning recommendations of the second learning system module for learning objects configured to teach the particular skill; and

after receiving the information designating the first learning system module to be a primary module used for the particular skill, assigning greater importance to a response from the first learning system module than to a response from the second learning system module in generating the learning recommendation.

6. The method of claim 4, further comprising:

after assigning greater importance to the response from the first learning system module in generating the learning recommendation, automatically increasing the influence of a second learning system module of the two or more learning system modules relative to the first learning system module based, at least in part, on merit determined for learning recommendations, of the second learning system module, that the second learning system module provides subsequent to generating, for the user, the learning recommendation.

7. The method of claim 1, further comprising:

receiving information designating a particular learning system module of the two or more learning system modules to be a primary module used for a particular subject;

determining that the learning recommendation request pertains to the particular subject; and

in response to determining that the learning recommendation request pertains to the particular subject, assigning greater importance to a module-selected learning recommendation from the particular learning system module than to module-selected learning recommendations from other learning system modules, of the two or more learning system modules, in generating the learning recommendation.

8. The method of claim 1, further comprising:

receiving information designating a particular learning system module of the two or more learning system modules to be a primary module used for a particular task;

determining that the learning recommendation request pertains to the particular task; and

in response to determining that the learning recommendation request pertains to the particular task, assigning greater importance to a module-selected learning recommendation from the particular learning system module than to module-selected learning recommendations from other learning system modules, of the two or more learning system modules, in generating the learning recommendation.

9. The method of claim 1, wherein a first learning system module, of the two or more learning system modules, implements a first learning model and a second learning system module of the two or more learning system modules implements a second learning model.

10. The method of claim 1, further comprising:

maintaining for the plurality of users, profiles, wherein the profile maintained for each of the plurality of users describes one or more attributes associated with the corresponding user of the plurality of users;

wherein a particular learning system module of the two or more learning system modules bases its module-selected learning recommendation, at least in part, on profile information for the user.

11. The method of claim 1, wherein input from the two or more learning system modules is requested via an application programming interface.

12. The method of claim 1, wherein the two or more learning system modules are configured to produce learning recommendations for learning objects that are designed to teach the particular skill.

13. A non-transitory computer-readable storage medium storing instructions which, when executed by one or more processors, cause performance of:

receiving, from a user of a plurality of users, a learning recommendation request for a learning object that is designed to teach a particular skill;

in response to receiving the learning recommendation request, requesting input from two or more learning system modules;

receiving module-selected learning recommendations from each of the two or more learning system modules; and

generating, for the user, a learning recommendation based, at least in part, on one or more of the module-selected learning recommendations;

wherein the learning recommendation identifies a particular learning object that is associated with a content item that is designed to teach the particular skill.