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The slidewiki platform service will then be able to send any data relating to activities performed by registered users to the LRS. This could will be performed either by:
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via the activity-service in such a way as do decouple the event triggering with the event handling. The platform (or possibly any other service) will simply prepare the event using a custom model and send it as an activity to the activities-service. Then, the activities service will have proper handlers for each activity type that will transform it to a properly defined xAPI event and forward it to the slidewiki LRS. Alternatively, the xAPI-service could receive the activity data and the transformation and forwarding would be performed there.
The Slidewiki LRS could be further exploited by receiving user activity data external to Slidewiki, e.g. from a Learning Management System (LMS) that would integrate with Slidewiki content.
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- User Profiling & Skills Recognition (ATHENA)
This task will first extend the current Slidewiki user profile with properties for skills, preferences, etc. These properties will be inferred by user activity data (stored in LRS). Also it will provide a skill recognition method for learners segmentation and enrichment of their profiles. - Content Recommendation (UPV)
This task will set up a content recommendation module that will provide content (e.g., decks, slides, etc) recommendations based on user profiles. These recommendations will be available in slidewiki UI (for example, you-may-also-like recommendations) - Advanced Analytics (InfAI)
This task will provide learning analytics methods (regression, prediction, trend analysis, etc) wrt the slidewiki learners activity and performance. It will combine activity and usage data (stored in LRS) with the learners’ profiles and learning content (stored in Slidewiki’s db) for extracting insights such as:- Individual's’ performance (e.g., course progress) prediction
- Early Alerting for educators interventions, etc….
- Interactive Visual Analytics (ATHENA, mainly Stavros)
This task will provide an interactive web-based (based on JS & HTML5 viz libraries) module for the user to visualize, explore and analyse SlideWiki activity data. It can provide simple usage reports and stats as well as more complex visual operations, such as faceted browsing, multidimensional analysis, roll-up, drill down and analytic window functions, trend visual analysis, graph analysis, timelines, etc.
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- the LRS instance that we are going to be using as the SlideWiki user activity data store
- the definition of user activity data we wish to collect; this could will be done on in the platform service level if directly communicating with the LRS, or on the proxy service we might use for data collection as proposed aboveactivity service that is going to receive such data
- the slidewiki platform service methods/components via which data collection will be performed for the specific activity data type
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Action items
- Setup the SlideWiki LRS instance ( Learning Locker ) Benjamin Wulff, Allan Third
Jira Legacy server JIRA (slidewiki.atlassian.net) serverId 497b9b88-e2b8-32ee-be05-4d265f33883f key SWIK-977 - Define the process by which activity data will be (a) required by an analytics module, (b) defined and modeled, and (c) transmitted by the platform service Benjamin Wulff, Kostis Pristouris, and Dejan Paunović, and Ali Khalili
Jira Legacy server JIRA (slidewiki.atlassian.net) serverId 497b9b88-e2b8-32ee-be05-4d265f33883f key SWIK-1111 - Specify a service for User Profiling and Skills Recognition Kostis Pristouris, Marios Meimaris, Abi James
- Specify a service for Content Recommendation Soledad Valero
- Specify a service for Advanced Analytics (there is interest by INFAI regarding this, Antje Schlaf, Roy Meissner or Kurt Junghanns could provide more info)
- Specify a service for Interactive Visual Analytics Stavros Maroulis
- Specify development for a platform module for Interactive Visual Analytics Stavros Maroulis
- Provide a way for user activity data from an LMS to be collected in the SlideWiki LRS Allan Third