Methods and systems for providing evaluation resources for users of an electronic learning system

Patent metadata

Field	Value
Publication number	US-2016358486-A1
Application number	US-201514729303-A
Country	US
Kind code	A1
Filing date	Jun 3, 2015
Priority date	Jun 3, 2015
Publication date	Dec 8, 2016
Grant date	—

Abstract

Methods and systems for providing an evaluation resource for a user of an electronic learning system. The methods can include identifying a user profile associated with the user, each user profile including user interaction data representing an interaction by a respective user with one or more learning paths provided by the electronic learning system; detecting, from the user interaction data in the identified user profile, an evaluation request from the user in respect of a learning path, the evaluation request indicating the evaluation resource is to be provided and the evaluation request is associated with at least one resource in the learning path; and in response to detecting the evaluation request, generating the evaluation resource, otherwise, continuing to monitor the user interaction data, wherein generating the evaluation resource comprises selecting one or more evaluation items based on, at least, resource data associated with the at least one resource.

First claim

We claim:

1 . A method for providing an evaluation resource for a user of an electronic learning system, the electronic learning system including a processor and a memory in electronic communication with the processor, the memory storing, at least, one or more user profiles for one or more respective users, the method comprising:
identifying, from the one or more user profiles, a user profile associated with the user, each user profile comprising user interaction data representing an interaction by a respective user with one or more learning paths provided by the electronic learning system; detecting, from the user interaction data in the identified user profile, an evaluation request from the user in respect of a learning path of the one or more learning paths, the evaluation request indicating the evaluation resource is to be provided and the evaluation request being associated with at least one resource in the learning path; and in response to detecting the evaluation request, generating the evaluation resource, otherwise, continuing to monitor the user interaction data, wherein generating the evaluation resource comprises selecting one or more evaluation items based on, at least, resource data associated with the at least one resource.

2 . The method of claim 1 , wherein:
the memory stores, at least, a plurality of evaluation items for the electronic learning system; and generating the evaluation resource further comprises:
conducting a semantic analysis of the resource data with each evaluation item of the plurality of evaluation items, the semantic analysis comprising a comparison of a content of the resource data with a content of each evaluation item;
generating a relevance score for each evaluation item based on the semantic analysis; and
selecting the one or more evaluation items from the plurality of evaluation items based on, at least, the respective relevance score.

3 . The method of claim 2 , wherein:
the resource data comprises: (i) a resource content of the at least one resource and (ii) one or more resource learning objectives of a set of learning objectives associated with the learning path, the at least one resource being associated with the learning path for fulfilling the one or more resource learning objectives; conducting the semantic analysis of the resource data with each evaluation item comprises:
conducting the semantic analysis of the resource content with the content of each evaluation item; and
conducting the semantic analysis of a content of each resource learning objective with the content of each evaluation item; and
generating the relevance score for each evaluation item based on the semantic analysis comprises:
generating a resource relevance score for each evaluation item based on the semantic analysis of the resource content with the content of each evaluation item;
generating a learning objective relevance score for each evaluation item based on the semantic analysis of the content of each resource learning objective with the content of each evaluation item; and
combining the resource relevance score and the learning objective relevance score to generate the relevance score.

4 . The method of claim 3 , wherein combining the resource relevance score and the learning objective relevance score to generate the relevance score comprises:
applying a resource weight to the resource relevance score; applying a learning objective weight to the learning objective relevance score; and summing the weighted resource relevance score and the weighted learning objective relevance score; wherein a sum of the resource weight and the learning objective weight is one.

5 . The method of claim 4 , wherein each of the resource weight and the learning objective weight is variable by one of (i) the one or more respective users and (ii) an operator of the electronic learning system.

6 . The method of claim 3 , wherein selecting the one or more evaluation items from the plurality of evaluation items comprises:
selecting a set of evaluation items from the plurality of evaluation items based on the respective relevance score; determining whether each evaluation item of the set of evaluation items is associated with a respective system learn value, wherein the system learn value for each evaluation item represents a likelihood of that evaluation item in determining a level of understanding the user has in respect of the one or more learning objectives; and in response to determining each feedback item is associated with the system learn value, selecting the one or more evaluation items from the set of evaluation items based on the respective system learn values, otherwise, selecting the one or more evaluation items from the plurality of evaluation items based on the respective relevance score.

7 . The method of claim 6 , wherein selecting the one or more evaluation items from the set of evaluation items based on the system learn values comprises:
determining a median value for the system learn values associated with the set of evaluation items; and selecting the one or more evaluation items from the set of evaluation items based on the median value, wherein selecting the one or more evaluation items from the set of evaluation items based on the median value comprises selecting an evaluation item associated with a first system learn value prior to selecting another evaluation item associated with a second system learn value, the first system learn value being at least closer in value to the median value than the second system learn value.

8 . The method of claim 2 , wherein selecting the one or more evaluation items from the plurality of evaluation items based on, at least, the respective relevance score comprises, for each evaluation item,
determining whether the respective relevance score exceeds a relevance threshold value assigned to the evaluation resource, the relevance threshold value being a minimum relevance score required for that evaluation item to be included in the evaluation resource; and in response to determining the respective relevance score exceeds the relevance threshold value assigned to the evaluation resource, including that evaluation item in the one or more evaluation items, otherwise, excluding that evaluation item from the one or more evaluation items.

9 . The method of claim 8 , wherein:
the evaluation resource is associated with a desired number of evaluation items; and generating the evaluation resource further comprises:
determining a number of relevant evaluation items from the plurality of evaluation items, the number of relevant evaluation items corresponding to a number of evaluation items associated with the relevance score exceeding the relevance threshold value;
determining whether the number of relevant evaluation items is less than the desired number of evaluation items; and
in response to determining the number of relevant evaluation items is less than the desired number of evaluation items, varying the relevance threshold value, otherwise, maintaining the relevance threshold value.

10 . An electronic learning system comprising:
a memory for storing, at least, one or more user profiles for one or more respective users; and a processor in electronic communication with the memory, the processor operating to:
identify, from the one or more user profiles, a user profile associated with the user, each user profile comprising user interaction data representing an interaction by a respective user with one or more learning paths provided by the electronic learning system;
detect, from the user interaction data in the identified user profile, an evaluation request from the user in respect of a learning path of t

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