Dynamic learning supplementation with intelligent delivery of appropriate content

Patent metadata

Field	Value
Publication number	US-2016358488-A1
Application number	US-201514729993-A
Country	US
Kind code	A1
Filing date	Jun 3, 2015
Priority date	Jun 3, 2015
Publication date	Dec 8, 2016
Grant date	—

Abstract

Systems, methods, and computer program products to perform an operation comprising identifying, in a corpus comprising a plurality of items of content, a subset of the plurality of items of content having a concept matching a concept in a learning environment, wherein each item of content comprises a set of attributes, computing an assistance score for each item of content in the subset based on the set of attributes of the respective item of content in the subset and a set of attributes of a user in the learning environment, and upon determining that a first item of content, of the subset of items of content, has as an assistance score greater than the assistance scores of the other items in the subset, returning the first item of content to the user as a learning supplement for the concept in the learning environment.

First claim

1 .- 7 . (canceled)

8 . A system, comprising:
one or more processors; and a memory containing a program, which when executed by the processors, performs an operation comprising:
identifying, in a corpus comprising a plurality of items of content, a subset of the plurality of items of content having a concept matching a concept in a learning environment, wherein each item of content comprises a respective set of attributes;
computing an assistance score for each item of content in the subset based on the respective set of attributes of the item of content in the subset and a set of attributes of a user in the learning environment; and
upon determining that a first item of content, of the subset of items of content, has as an assistance score greater than the assistance scores of the other items in the subset, returning the first item of content to the user as a learning supplement for the concept in the learning environment.

9 . The system of claim 8 , wherein the set of attributes of the items of content comprise one or more of: (i) a reading level of each item of content, (ii) a format of each item of content, (iii) an instruction type of each item of content, and (iv) feedback reflecting a level of instruction effectiveness of each item of content.

10 . The system of claim 8 , wherein the set of attributes of the user comprise one or more of: (i) a reading level of the user, (ii) a learning classification of the user, (iii) a level of understanding of the user relative to the concept in the learning environment, (iv) a preferred learning format of the user, and (v) a preferred instruction type of the user.

11 . The system of claim 8 , wherein the assistance score of each item is computed based on a machine learning model receiving the set of attributes of the user and the set of attributes of the content as input, wherein the first item of content is returned at a first time, the operation further comprising:
returning, at a second time, subsequent to the first time, at least one of: (i) the first item of content (ii) and a second item of content from the subset.

12 . The system of claim 8 , the operation further comprising:
determining the concept in the learning environment based on one or more of: (i) analysis of an audio recording of the learning environment, (ii) a lecture plan, (iii) analysis of an image displayed in the learning environment, (iv) analysis of content presented in an application executing on a system of the user, and (v) a search query entered by the user.

13 . The system of claim 8 , the operation further comprising:
subsequent to returning the first item of content, monitoring a set of actions of the user; determining, based on the set of actions of the user, whether the first item of content assisted the user; storing an indication as to whether the first item of content assisted the user; and upon determining that the first item of content did not assist the user, returning a second item of content from the subset to the user.

14 . The system of claim 13 , wherein the set of actions comprise: (i) facial expressions, (ii) speaking, (iii) interacting with the first item of content, (iv) and searches performed by the user.

15 . A computer program product, comprising:
a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising:
identifying, in a corpus comprising a plurality of items of content, a subset of the plurality of items of content having a concept matching a concept in a learning environment, wherein each item of content comprises a respective set of attributes;
computing an assistance score for each item of content in the subset based on the respective set of attributes of the item of content in the subset and a set of attributes of a user in the learning environment; and
upon determining that a first item of content, of the subset of items of content, has as an assistance score greater than the assistance scores of the other items in the subset, returning the first item of content to the user as a learning supplement for the concept in the learning environment.

16 . The computer program product of claim 15 , wherein the set of attributes of the items of content comprise one or more of: (i) a reading level of each item of content, (ii) a format of each item of content, (iii) an instruction type of each item of content, and (iv) feedback reflecting a level of instruction effectiveness of each item of content.

17 . The computer program product of claim 15 , wherein the set of attributes of the user comprise one or more of: (i) a reading level of the user, (ii) a learning classification of the user, (iii) a level of understanding of the user relative to the concept in the learning environment, (iv) a preferred learning format of the user, and (v) a preferred instruction type of the user.

18 . The computer program product of claim 15 , wherein the assistance score of each item is computed based on a machine learning model receiving the set of attributes of the user and the set of attributes of the content as input, wherein the first item of content is returned at a first time, the operation further comprising:
returning, at a second time, subsequent to the first time, at least one of: (i) the first item of content (ii) and a second item of content from the subset.

19 . The computer program product of claim 15 , the operation further comprising:
determining the concept in the learning environment based on one or more of: (i) analysis of an audio recording of the learning environment, (ii) a lecture plan, (iii) analysis of an image displayed in the learning environment, (iv) analysis of content presented in an application executing on a system of the user, and (v) a search query entered by the user.

20 . The computer program product of claim 15 , the operation further comprising:
subsequent to returning the first item of content, monitoring a set of actions of the user; determining, based on the set of actions of the user, whether the first item of content assisted the user; storing an indication as to whether the first item of content assisted the user; and upon determining that the first item of content did not assist the user, returning a second item of content from the subset to the user.

Assignees

• IBM

Inventors

• Canter Alexander J
• Clark Adam T
• Mysak John S
• Payton Aspen L
• Petri John E
• Pfeifer Michael D

Classifications

• G06N99/005

  Physics · mapped topic

• G09B5/00 Primary

  Electrically-operated educational appliances (working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F) · CPC title

• G06F17/3053

  Physics · mapped topic

• G06N5/04

  Inference or reasoning models · CPC title

• G09B5/06

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