The project started at the centre of ADL, and you can find many resources at the Experience API (xAPI) page here. And there is a wiki with lots of working documents and status details as well.
For me, I am interested in the xAPI in combination with its mobile options for: tracking self-reported experiential learning, informal learning capture and 3rd party evaluation. Why? Because I am looking to build a research instrument which sort of combines classic 'learning logs' (i.e. diaries kept by the learner where s/he notes down what they have learned) and the qualtified self (a combination of the qualified and the quantified self which I wrote a post about here).
A good overview to start from are the slides from Nikolaus Hruska:
http://www.adlnet.gov/what-should-i-track-in-my-learning-experiences-to-build-learning-analytics/
And a really nice twitter implemenation example of the xAPI is described by the wonderful Mayra Aixa Villar in her blogpost #xAPI twitter chat - joint venture team - recap.
These parts of the notes are from the first speaker Andy Wooler from Hitachi systems
He starts the
day, framing learning data in general (look for Hitachi bloggers on big data to
keep on top of their ongoing roadmap on data)
Making sense of your data (video)
What data is really important.
What does the technical architecture have
to look like in the next 5 year.
Think
about why I – Inge - want to use it?
- My reason: map the triggers in MOOC learning across multiple devices (in a ubiquitous learning platform like FutureLearn)
- Linking learning data to provide from the ground up research data
- Looking for learning meaning across contemporary social files (social media, email, text files, machine log files)
- Allowing serendipitous learning to occur (so creating an open research instrument, yet based on first findings coming out of the pilot that was performed)
Comparing with Berein by Delottte (Talent analytics
maturity model looks like
At
Hitachi:
ARIES currently: Analytics and Reporting Integrated
Enterprise System:
- LMS
- assessment tool, feeding in an Oracle system.
This enables different yet not ideal dashboards
analysing learning data.
Currently the dashboards on LMS’s are not
ideal as a UI.
Vision for the future at Hitachi (HDS
academy data future)
- ARIES future
- Lms
- It project database
- Hds community
- The loop
- Help desk
- CRM systems
- LRS (Learning Record Stores)
Roadmap towards 2018 advance analytics tools
predicted.
Concept of the never ending course (CPD –
continued professional development with added spaced learning), this poses a
problem for SCORM. Where people are at that point in time in their learning
journey.
(look up the stairway to elearning heaven
(Fiona Leteney @fionaleteney also into Tin Can/xAPI)
Reflection suggestion: what type of
learning would be of interest to you taken into account the full personal,
informal learning as well? What can be taken out of your community tools.
What
do you need to think about when looking for meaningful data/analysis?
- Learning strategy needs to be built to know what you want to look at
- Self-reporting is depending on subjective meaning giving (cfr classic learning diaries that learners provide by self-reporting)
- Validation of actions actually being undertaken by the learner participant
- Coming to a mutual shared meaning of used self-reported vocabulary
- Iteration of tracking due to learning which data does matter, how tin can actually works, how to automate data tracking for self-reported and informal learning.
- Looking for higher level analysis: actively linking the data with discourse research, networked learning, social learning research, looking at the big five traits of a learner
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