1. Hendrik Drachsler, Sander Latour, Alan Berg
The Caliper and XApi frameworks what are they and why they are important
Hendrik Drachsler, Sander Latour, Alan Berg

2. Tracking of data in the LMS
Dyckhoff, A. L., Zielke, D., Buelltmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Educational Technology & Society, 15 (3), 58–76.

3. Tracking of Social Metadata in community environments

4. Do we track the Learning Process with these data?
Real learning takes places everywhere but for sure not ONLY in the LMS
Quantified Self of Learning (Games, Books, Talks, TV, Wearable's, mobiles)
Learning Analytics Summer Institute NL, #LASI_NL,

5. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
Very little learning takes place in the LMS, we need to be able to track ANY learning activity EVERYWHERE.
Learning Analytics Summer Institute NL, #LASI_NL,

6. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
Xapi aka. Tin Can API
Learning Analytics Summer Institute NL, #LASI_NL,

7. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
How Tin Can API works
Tin Can enabled activities send simple statements to a Learning Record Store.
LRS
Elearning
Game
Simulator
Blog
YouTube
CC:
Learning Analytics Summer Institute NL, #LASI_NL,

8. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
Xapi Statements
John added a photo to Open U Community Environment
Jim commented on John’s photo on Community Environment
John watched How to save energy video on ARLearn at pm
John subscribed to Sustainable Energy on Open U at pm
John posted My first blog post in Open U Community Environment
Learning Analytics Summer Institute NL, #LASI_NL,

9. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
Xapi aka. Tin Can API
Learning Analytics Summer Institute NL, #LASI_NL,

10. Learning Locker (OS Learning Record Store)
Learning Analytics Summer Institute NL, #LASI_NL,

11. Learning Record Store
Dash boards
MLN / MOOC Sensors
LX Sensors
Mobile Sensors
LMS

12. SANDER LATOUR - PERCEPTUM

16. NOTE: MORE SLIDES THAN TIME.
Alan Berg
NOTE: MORE SLIDES THAN TIME.

17. What’s Tin Can API then?
Tin Can API is the code name that Experience API was developed under. Still commonly used but the proper name is Experience API or xAPI (according to the spec and ADL ).
(Rustici disagrees though)

18. a.k.a. TinCan API or Nextgen SCORM
Experience API
a.k.a. TinCan API or Nextgen SCORM

19. What is a Learning Record Store?
Rest Services for adding or querying Learning
Record Store
System X,
Mobile, etc.
LMS
Webpages
LTI
tools

20. Experience API (xAPI) protocol
JSON: actor verb object context extensions etc.
{ "actor": { "name": "Sally Glider", "mbox": }, "verb": { "id": " "display": {"en-US": "experienced"} }, "object": { "id": " "definition": { "name": { "en-US": "Solo Hang Gliding" } } } }

21. xAPI Benefits
Decouples activity streams from specific software (centralizes data)
Standards approach
Scalable - But {the LRS market needs to mature}
Structured data - But {depends on what you send}
Can work well with LTI dashboards
Supports community
Clean data for research
Growing market enthusiasm

22. ACTIVITY SOURCES
UvA have already invested in Sakai CLE and Apereo OAE TinCAN API support, uPortal being built:
MOOC like Example: Adding Youtube activity to a Learning Record Store
Embed a Youtube player in a webpage
Add some enriching Javascript
Record to an LRS
Adopters:

23. xAPI Risks Security (is the data secure?)
Privacy and ethics issues vary widely from country to institution
Still early on in its maturity model
Requires an initial investment
Data in the cloud (is your data safe?)
The reference implementation (ADL_LRS) is not production ready.

24. ADL LRS Open Source Python installed reference
There is a simple LRS (created by ADL) available in github but it is not scaleable and also not very easy to use
Reference implementation
Free open source (Apache license)
Python based
Minimal functionality
OK for testing compatibility with xAPI spec

25. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
Experimenting
Sakai as LRS
ADLNET as reference implementation
Learning Analytics Summer Institute NL, #LASI_NL,

26. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
LTI  Chaining dashboard parts
Learning Analytics Summer Institute NL, #LASI_NL,

27. What does a POTENTIAL CAMPUS WIDE deployment look like?
UvAInform
Dashboard A
UvAInform
Dashboard z
COACH
Warehouse
Prediction framework + Fact generations
OAAI
Blackboard Analytics
Course Signals
Caliper
FACTS
Learning Record Store
SIS
Service A
Activity
Service X
Video
LMS next gen
BasicLTI tools
Research Services
STAGE 1

28. https://confluence. sakaiproject. org/pages/viewpage. action
→ OAAI

29. LRS options (partial list)
Rustici ScormCloud
Commercial cloud or installed
WaxLRS
Commercial cloud
Learning Locker
Open Source PHP installed
UvA LRS
Open Source Java installed

30. Something to take away
Without an LRS we are stuck with data silo’s, dirty data no clear strategy to work together
Standards decouples components in the infrastructure
Standards supports validation across services
Standards and guidelines are mutually supportive
Researcher friendly
Lets look towards a uniform set of guidelines for products services
Lets work with an International community
Let’s look towards data democracy between facilities
Text here is only an example. Please delete what you don’t need.

31. Caliper
Learning Analytics Workgroup Charter and Workstreams focused on Caliper design and build, integration with other IMS services such as LTI
Workgroup has two distinct workstreams working in parallel focused on the Sensor API and the Metric Profile definition
Engaging other IMS service areas such as LTI to integrate Caliper services so as to drive intrinsic learning measurement
Leveraging the IMS member community and its diverse domain expertise with specialized learning activity area such as Assessment, Media, Collaboration etc to real-world influence Caliper Metric Profiles and adoption of Caliper Sensor API
Working on defining a Caliper development community to drive real-world adoption
EDUPUB
Integrating latest Caliper framework (along with other IMS services such as LTI, QTI etc) into a successive set of milestone EDUPUB PoC/demos

32. xAPI + Open Badges http://learninglocker
Learning Locker Learning Record Store (LRS)
“An xAPI statement essentially records an event related to learning. The big idea (I think) is to facilitate the effortless capture of learning events, regardless of learning system or environment. But lonely xAPI statements by themselves are potentially just a sea of meaningless event records
An open badge aims to recognise something, whether it be achievement, attendance or behaviour. But badges gained without authenticated evidence inside are potentially valueless.
Together, they provide a meaningful record of achievement, with an authenticated audit trail of activity. The granular statements that xAPI provides support the assessment process of deciding whether someone has achieved a particular badge – or not yet achieved it. Essentially this results in a mapping between the criteria stored in a badge and the activity stored in xAPI statements. This mapping can also be stored inside the badge itself when issued
ADL Design Group
We’ve put together a working group for the second round of the ADL’s Experience API design group to do exactly this. Our team, The Experience Badgers, will be working to deliver two key outcomes:
1. To create a prototype for issuing Open Badges based on a pre-specified set of criteria that are evidenced in xAPI statements (i.e. Data comes in, when the criteria is met, the badge is issued to the individual)
2. And to suggest a standard method by which Open Badges are identified in xAPI statements (i.e. what is the appropriate context description and method to describe an Open Badge being issued to a learner, such that 3rd party systems can always ‘detect’ when a statement refers to a badge and potentially always reach the badge assertion).”

33. Plug for the next presentation Look to collaborate with other Universities.
We are not Unique, being pulled into the drift of the LA trend
Apereo has a proven track record
Share risks and requirements
Avoid duplication
Build industrial software
Share costs
More eyeballs more security
Consistent guidelines for Ethics and Security practices
Scale up validation (it ain’t all about visualizations)
More (NO DOUBT)

34. Questions?

35. Learning Analytics Summer Institute NL, #LASI_NL, 30.06-01.07.2014
REFERENCE MATERIAL
Learning Analytics Summer Institute NL, #LASI_NL,

36. Experience API a.k.a Tin Can API, xAPI http://tincanapi.com
The Tin Can API is a set of RESTful web services. The services carry a JSON payload that allows a “Learning Activity Provider” to make a series of “Statements” to a Learning Record Store (LRS). Each “Statement” describes a learning experience and consists of three parts, an “Actor,” a “Verb” and an “Object”. A “Statement” conveys something like “Mike passed Introduction to REST”.

37. What is the Experience API (xAPI)
Standard communication protocolhttps://github.com/adlnet/xAPI-Spec
Define statements about learning activity: Actor Verb Object - “I did this”
REST service for writing and querying
Developed by ADL (with Rustici)
Version 1.0 (Apr 2013), (Oct 2013)
Good introduction at:

38. actor + verb + object RDF Triple RDF triple statement
RDF (W3C spec) = general method for describing or modeling information made available as web resources.
RDF triple = subject + predicate + object
Jack attended Apereo14

39. Learning Record Stores
A non-comprehensive list

40. Instrumentation code (a. k. a
Instrumentation code (a.k.a. Sensors) to drop into learning platforms, apps and content to capture learning events encapsulating standardized granular metrics and context
Multiple code bindings (i.e. JS, Java, Python, PHP, Ruby etc) to accommodate wide spectrum of systems and applications)
Common foundational metrics that apply to all learning activities
Specific learning activity type metrics and context to drive consistent measures
Leverage existing and drive new learning metrics to increasingly standardize measurement across wide spectrum of learning activity and systems
Apply context via complementary IMS standards based services and information models, outcomes taxonomies etc into metrics
Exploring / pursue alternate “transport” layer use of xAPI (TinCan), ActivityStreams etc. Caliper provides comprehensive, learning optimized, measurement framework

43. Enable collection of rich, contextual data about learning interactions.
Enrich the “triple” statement: inject context into the statements
Controlled vocabularies with permitted extensions
IMS Learning Metric Profiles are intended to define a standardized, structured collection of Learning Activity Metrics that represent granular measurements specific to actions within each genre of activity including all relevant context for any given action on an activity. In addition, there are pan-genre Foundational Metrics such as engagement, performance, etc., that support common measurement of more generally applicable metrics across all activities. Any given Learning Activity can have (one or more) Metric Profiles associated with it to collect and exchange whatever measurements which need to captured and managed.

44. LTI Sensor payloads
metric profile(s)
sensor endpoint