Kneaver Corp

We provide a Knowledge Management Toobox for all. Learning, Kneaver is a tiny company represented by Bruno Winck it’s founder. xAPI offers a versatility attractive for us. Kneaver is strong in Social Media using technology similarity with ActivityStreams, Kneaver is based on a Semantic Database and favor semantic web solutions.

Kneaver

It’s our flagship product. Tracking, Annotating and sharing are important aspects of Knowledge Management. Kneaver is offered as SAAS. Customers are teams and individuals. Our users are life long learners, knowledge workers. They will use Kneaver to build Knowledge base, best practices, taxonomies and maintain their peers network. Kneaver includes connections to social media, other services like Buffer, GitHub, mail. Knowledge can be shared on Wordpress (as you read now), Slidedecks, mindmaps, Twitter or on our plateform kpm.

There are 4 main uses cases for xAPI inside Kneaver.

• Onboarding Tutorials xAPI use case

  When any user register on Kneaver he will be presented with a product tour. The user will be the actor of this use case.

  The tour is a typical boilerplate learning experience. behind the scene it is authored using Kneaver as a set of small sequences of text anchored on the page.

  Scenario

  • User enter the page, from which ever device
  • System will query the LRS to see which steps have already been presented to the user and accepted.
  • System will query the Learning meta processes and see where a new lesson can start based on the current page and continuous learning experience already achieved.
  • A new lesson will be produced as a json chunk.
  • Lesson will be served to the page using ajax
  • Client side lesson executor will run through the lesson
  • Whenever User will skip or validate a step a xAPI call is emitted creating a new statement

  Benefits of using xAPI

  • The tracking of the learnin experience is device independant. A user starting on one device will continue his lesson at the very exact point he left it on another device and can resume it again on the first device.
  • Lessons are easy to represent as URI since they are items with URI on their own anyway.

  Statements used

  actor: {“objectType”:“Agent”,“account”: {“homePage”:“http://kneaver.com”,“name”:“Me”} }
  verb: {“id”:“http://adlnet.gov/expapi/verbs/completed”}
  object: {“id”:“http://kneaver.com/KNVGuides/Intro1/ReloadButton/next/Intro1:6”}

  Limitations observed

  Some users quickly came to us asking to replay the tutorial once again.The first solution that came was to delete the statements relative to the onboarding tour of this user. An alternative solution came later was to attach a state to the user and increment a current tour iteration number.

  While some xAPI statements are generated from client side they are not complete. The actor is not informed. The actor is added by our customer LRS based on the authentification, like the time.

  Our embeded LRS serves mostly self directed learning. It is not build for performance or scaling. It can be used both via an API (with c++, php, NodeJS mapping) or via REST. Authentification is based on tokens and more advanced than what xAPI 1.0 allows. Tokens are very short term tokens.

• Evaluation of items xAPI use case

  Kneaver is made of a network items corresponding to Concepts, persons, Topics, Nothions, Organizations.  Whenever a user is evaluating himself or an item an xAPi statement is issued to record this event.  This allows us to present users with items they acknowledge as being necessary to know but not yet memorized or turned into their practice. The variety of verbs and objects allows us to extend the intraction in an introspective and reflective way.

• Annotation xAPI use case

  Annotations of tweets, blogs and findings on the web are captured as xAPI statements

• TwitterChat xAPI use case

  Kneaver as a feature in the Kneaver for Chat Hosting option available in the Professional package to run Twitter chats.

  Chats are defined as actvities. Each question being a sub activity on its turn.

  When the runner send the questions it will use the xAPI  document API.

  When the chat takes place the statistics can be kept as xAPI collective statements.

  The chat itself, once kept as statements, Is no more kept as xAPI statements because it was too voluminous and because each run of the analyze was producing statements impossible to delete. anyway the benefit of keeping them as statements was limited.