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

Patent metadata

Field	Value
Publication number	US-12469401-B2
Application number	US-202418611054-A
Country	US
Kind code	B2
Filing date	Mar 20, 2024
Priority date	Jun 3, 2015
Publication date	Nov 11, 2025
Grant date	Nov 11, 2025

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 method comprising:
receiving an electronic resource comprising a content having 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; 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; determining if the relevance score assigned to the at least one least one content portion satisfies a relevance threshold for that learning objective; in response to determining the relevance score at least satisfies the relevance threshold, assigning the at least one content portion with that learning objective; determining, by semantic analysis, a first semantic correlation score between the at least one content portion and a resource-associated learning objective, and a second semantic correlation score between the at least one content portion and a content-associated learning objective; generating a combined correlation score by combining the first and second semantic correlation scores by one or more weighting factors; and selecting, based on the combined correlation score, the at least one content portion for inclusion in a user-specific learning path associated to the 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 the relevance score, the relevance score representing an estimated degree of correlation between the at least one content portion and the at least one learning objective; and in response to determining the relevance score at least satisfies the relevance threshold, the relevance threshold being a minimum relevance score required for the at least one content portion to be associated with that learning objective, 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 an electronic resource comprising a content having 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;
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;
determine if the relevance score assigned to the at least one least one content portion satisfies a relevance threshold for that learning objective;
in response to determining the relevance score at least satisfies the relevance threshold, assign the at least one content portion with that learning objective
determine, by semantic analysis, a first semantic correlation score between the at least one content portion and a resource-associated learning objective, and a second semantic correlation score between the at least one content portion and a content-associated learning objective; and
generate a combined correlation score by combining the first and second semantic correlation scores by one or more weighting factors; and
select, based on the combined correlation score, the at least one content portion for inclusion in a user-specific learning path associated to the 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 the relevance score, the relevance score representing an estimated degree of correlation between the at least one content portion and the at least one learning objective; and in response to determining the relevance score at least satisfies the relevance threshold, the relevance threshold being a minimum relevance score required for the at least one content portion to be associated with that learning objective, 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