Methods and systems for improving resource content mapping for an electronic learning system

Patent metadata

Field	Value
Publication number	US-11410563-B2
Application number	US-202016922664-A
Country	US
Kind code	B2
Filing date	Jul 7, 2020
Priority date	Jun 3, 2015
Publication date	Aug 9, 2022
Grant date	Aug 9, 2022

Abstract

Methods and systems for improving resource content mapping for an electronic learning system. The methods can include: receiving, by the electronic learning system, a resource for satisfying at least one learning objective of the one or more learning objectives, the resource comprising a content having a content data convertible into a text data and one or more resource property fields defining at least one characteristic of the resource; sectioning the content data into one or more content portions based on an analysis of at least one of the content data and the one or more resource property fields; and assigning at least one content portion of the one or more content portions to at least one learning objective.

First claim

We claim:

1. A method for improving resource content mapping for an electronic learning system, the electronic learning system including a processor and at least one memory in electronic communication with the processor, the at least one memory storing one or more learning objectives, the method comprising:
receiving, by the electronic learning system, an electronic resource for satisfying at least one learning objective of the one or more learning objectives, the electronic resource comprising a content having a content data convertible into a text data and one or more resource property fields defining at least one characteristic of the electronic resource;
sectioning the content data into one or more content portions based on an analysis of at least one of the content data and the one or more resource property fields; and
assigning at least one content portion of the one or more content portions to at least one learning objective.

2. The method of claim 1 , wherein sectioning the content data into the one or more content portions based on the analysis of the at least one of the content data and the one or more resource property fields comprises:
determining whether the one or more resource property fields includes one or more resource structure fields, the one or more resource structure fields defining a content structure of the content data and the content structure including one or more data hierarchy levels; and
in response to determining the one or more resource property fields includes the one or more resource structure fields, sectioning the content data into the one or more content portions according to at least one data hierarchy level of the one or more data hierarchy levels.

3. The method of claim 1 , wherein assigning the at least one content portion to the at least one learning objective comprises:
applying a semantic analysis to the at least one content portion and applying the semantic analysis to each learning objective of the one or more learning objectives;
based on results of the semantic analysis, assigning a relevance score for the at least one content portion in respect of at least one learning objective of the one or more learning objectives, the relevance score representing an estimated degree of correlation between the at least one content portion and the at least one learning objective;
for each learning objective, determining whether the respective relevance score assigned to the at least one content portion at least satisfies a relevance threshold for that learning objective, the relevance threshold being a minimum relevance score required for the at least one content portion to be associated with that learning objective; and
in response to determining the relevance score at least satisfies the relevance threshold, assigning the at least one content portion with that learning objective.

4. The method of claim 1 , wherein the one or more content portions comprises two or more data hierarchy levels.

5. The method of claim 1 , wherein the electronic resource comprises a video, and receiving the electronic resource for satisfying the at least one learning objective comprises transcribing an audio data into the text data.

6. The method of claim 1 , wherein the electronic resource comprises an image comprising at least a portion that is convertible to text data, and receiving the electronic resource for satisfying the at least one learning objective comprises applying an electronic character recognition conversion to the image for generating the text data from the image.

7. An electronic learning system comprising:
a memory for storing one or more learning objectives; and
a processor in electronic communication with the memory, the processor operating to:
receive a electronic resource for satisfying at least one learning objective of the one or more learning objectives, the electronic resource comprising a content having a content data convertible into a text data and one or more resource property fields defining at least one characteristic of the electronic resource;
section the content data into one or more content portions based on an analysis of at least one of the content data and the one or more resource property fields; and
assign at least one content portion of the one or more content portions to at least one learning objective.

8. The electronic learning system of claim 7 , wherein the processor operates to:
determine whether the one or more resource property fields includes one or more resource structure fields, the one or more resource structure fields defining a content structure of the content data and the content structure including one or more data hierarchy levels; and
in response to determining the one or more resource property fields includes the one or more resource structure fields, section the content data into the one or more content portions according to at least one data hierarchy level of the one or more data hierarchy levels.

9. The electronic learning system of claim 7 , wherein the processor operates to:
apply a semantic analysis to the at least one content portion and apply the semantic analysis to each learning objective of the one or more learning objectives;
based on results of the semantic analysis, assign a relevance score for the at least one content portion in respect of at least one learning objective of the one or more learning objectives, the relevance score representing an estimated degree of correlation between the at least one content portion and the at least one learning objective;
for each learning objective, determine whether the respective relevance score assigned to the at least one content portion at least satisfies a relevance threshold for that learning objective, the relevance threshold being a minimum relevance score required for the at least one content portion to be associated with that learning objective; and
in response to determining the relevance score at least satisfies the relevance threshold, assign the at least one content portion with that learning objective.

10. The electronic learning system of claim 7 , wherein the one or more content portions comprises two or more data hierarchy levels.

11. The electronic learning system of claim 7 , wherein the electronic resource comprises a video, and the processor operates to transcribe an audio data into the text data.

12. The electronic learning system of claim 7 , wherein the electronic resource comprises an image comprising at least a portion that is convertible to text data, and the processor operates to apply an electronic character recognition conversion to the image for generating the text data from the image.

Assignees

• D2L Corp

Inventors

• Bilic Jugoslav
• Michaud Stephen John
• Bayly Martin David Goodenough
• Ogg Ryan Clayton

Classifications

• 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