Difference between revisions of "Aggregate"

Latest revision as of 03:38, 5 April 2016

We developed an adaptive social learning architecture Aggregate to support Mastery Grids Interface. We design this architecture to integrate content from diverse sources. The explanation of the procedures and links to associated resources are given below.

Mastery Grids Interface receives all the information to display (this is course structure, links to content and progress levels of the learner and the group of learners) from services hosted in Aggregate (a in the diagram). This information is passed in JSON format.

To let Mastery Grids know the level of progress of the learners in the topics and the content, Aggregates call services from a User Model (UM in the figure). See point b) in the figure. These services are documented in User State Protocol

The figure shows several different Content apps (QuizJet, QuizPet, Webex, etc.). Each of these applications are integrated in 2 ways:
- The content application needs to log information of the learner activity within the content to the User Model (letter c in the diagram). Currently, the content applications are using our ADAPT2 CUMULATE protocol.
- The content application provides a service to list all the available content. This service implement the content brokering specification, which defines a JSON-format protocol to allow external applications to discover content. Both Aggregate and the User Model use the content brokering to register new content.

Several content application and their respective content brokering are implemented in ACOS server.

Associated resources

Mastery Grids repository (contains aggregate, mastery grids interface, documentation and course authoring tool)
User State Services repository
user modeling specification

Content application resources

Smart content : provides an explanation of content applications
ACOS server : provides details of ACOS infrastructure, content and protocols
QuizJet repository
QuizPet repository
Annotated Examples (Webex) repository
Educational Videos repository

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-We developed an adaptive social learning architecture Aggregate to support [[Mastery Grids Interface]]. This architecture supports several kinds of open student modeling, social comparison, and recommendation. The architecture fulfills a major objective, portability, which is the ability to be integrated to other systems with little set up and modification. The architecture is modular and includes different software components as follows:
+[[Image:arch_v2.png|Aggregate Architecture]]
-*frontend user-system interaction interface, the [[Mastery Grids Interface]]
+We developed an adaptive social learning architecture Aggregate to support [[Mastery Grids Interface]]. We design this architecture to integrate content from diverse sources. The explanation of the procedures and links to associated resources are given below.
-*backend '''''Aggregate''''' services communicating between the main interface and user modeling services [https://github.com/PAWSLabUniversityOfPittsburgh/AggregateUMServices  ==> source code]
+* [[Mastery Grids Interface]] receives all the information to display (this is course structure, links to content and progress levels of the learner and the group of learners) from services hosted in Aggregate (a in the diagram). This information is passed in JSON format.
-*backend [[user modeling]], [[personalization]] (recommendation, navigation support) services
+* To let Mastery Grids know the level of progress of the learners in the topics and the content, Aggregates call services from a User Model (UM in the figure). See point b) in the figure. These services are documented in [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids/blob/master/documentation/User_State_protocol.pdf User State Protocol]
-*backend [[content]] providing applications
+* The figure shows several different Content apps (QuizJet, QuizPet, Webex, etc.). Each of these applications are integrated in 2 ways:
+** The content application needs to log information of the learner activity within the content to the User Model (letter c in the diagram). Currently, the content applications are using our ADAPT2 [[CUMULATE protocol]].
+** The content application provides a service to list all the available content. This service implement the [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids/blob/master/documentation/Content_Brokering_API_2016_01_21.pdf content brokering specification], which defines a JSON-format protocol to allow external applications to discover content. Both Aggregate and the User Model use the content brokering to register new content.
-An overall architecture of the system can be sen in the following figure.
+Several content application and their respective content brokering are implemented in [http://acos.cs.hut.fi/ ACOS server].
-[[Image:architecture_v1.png|1000x1500px|Aggregate Architecture]]
+== Associated resources ==
+* [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids Mastery Grids repository] (contains aggregate, mastery grids interface, documentation and course authoring tool)
+* [https://github.com/PAWSLabUniversityOfPittsburgh/AggregateUMServices  User State Services repository]
+* [[CUMULATE|user modeling specification]]
+== Content application resources ==
+* [[Smart Content|Smart content]] : provides an explanation of content applications
+* [http://acos.cs.hut.fi ACOS server] : provides details of ACOS infrastructure, content and protocols
+* [https://github.com/PAWSLabUniversityOfPittsburgh/quizjet QuizJet repository]
+* [https://github.com/PAWSLabUniversityOfPittsburgh/quizpet QuizPet repository]
+* [https://github.com/PAWSLabUniversityOfPittsburgh/annotated-examples Annotated Examples (Webex) repository]
+* [https://github.com/PAWSLabUniversityOfPittsburgh/educational-videos Educational Videos repository]