Data Interoperability for Learning Analytics and Lifelong Learning
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Transcript of Data Interoperability for Learning Analytics and Lifelong Learning
D ATA I N T E R O P E R A B I L I T Y F O R L E A R N I N G A N A LY T I C S A N D L I F E L O N G L E A R N I N G
Kirsty Kitto Queensland University of Technology [email protected] @KirstyKitto www.beyondlms.org
L E A R N I N G A N A LY T I C S ?
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their
contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
SoLAR definition
But very little emphasis upon LA for learners at present
L E A R N E R S P E R S P E C T I V E
http://www.elearning.ac.uk/mle.html
W H Y D O N ' T E - P O R T F O L I O S E V E R TA K E O F F ?
T H A T S F R O M 2 0 0 6
A D AY I N M Y L I F E W H E N D O I N G L E A R N I N G A N A LY T I C S …
• jump for joy because you finally got ethics approval for your current research project
• spend ages trying to interface with different APIs for different systems
• extract data that is somehow always in slightly different form
• spend most of your time trying to get it to look like your other data sets so you can use existing tools
• give up and develop a new bespoke solution
http://datascience.la/data-science-toolbox-survey-results-surprise-r-and-python-win/
L I F E L O N G P E R S O N A L I S E D L E A R N I N G
the holy grail of education
a growing obsession with EdTech
impossible without data interoperability
A S A C O M M U N I T Y W E N E E D T O D E S I G N F O R D ATA I N T E R O P E R A B I L I T Y
The killer app of xAPI
H I G H L E V E L P R O F I L E S A N D R E C I P E S
• Analysis across different platforms made easier with early planning
• Recipes are essential! - Microblogging - Content Creation - Collaborative Content
Authoring - Content Curation - Association with Course, Team
and Instructor
Bakharia, Kitto, Pardo, Gašević, Dawson (In press). Recipe for Success — Lessons Learnt from Using xAPI within the Connected Learning Analytics Toolkit, Learning Analytics and Knowledge 2016 (LAK16)
C L A T O O L K I T D A S H B O A R D SReports generated about
- Activity
- Social Networks
- Content Analysis
www.beyondlms.orgOpen dashboards and learning analyticsAnyone could use them if they have the same data structures…
L E S S O N S F R O M T H E C L A T O O L K I T P R O J E C T
• Context is not optional
‣ the more details the better for learning analytics
‣ it gets complicated!
• Recipes are essential for keeping context under control and reusable
• Same thing with timestamps (time series common in LA)
https://github.com/kirstykitto/CLRecipe/blob/master/microblogging/microblogging-tweetwithhashtagsandmentions.json
L I S T E N T O T H E L A C O M M U N I T Y
http://www.laceproject.eu/d7-4-learning-analytics-interoperability-requirements-specifications-and-adoption/
T H I S P R O J E C T I S S U P P O R T E D B Y T H E A U S T R A L I A N G O V E R N M E N T ’ S O F F I C E F O R L E A R N I N G A N D T E A C H I N G
Q U E E N S L A N D U N I V E R S I T Y O F T E C H N O L O G Y:
Kirsty Kitto (Lead Investigator), Mandy Lupton, John Banks, Dann Mallet, Peter Bruza, Aneesha Bakharia
U N I V E R S I T Y O F S O U T H A U S T R A L I A
Shane Dawson, Dragan Gašević (Uni of Edinburgh)
U N I V E R S I T Y O F T E C H N O L O G Y, S Y D N E Y
Simon Buckingham Shum
U N I V E R S I T Y O F S Y D N E Y
Abelardo Pardo
U N I V E R S I T Y O F T E X A S ( A R L I N G T O N )
George Siemens
I D 1 4 - 3 8 2 1 : E N A B L I N G C O N N E C T E D L E A R N I N G V I A O P E N S O U R C E A N A LY T I C S I N T H E W I L D : L E A R N I N G A N A LY T I C S B E Y O N D T H E L M S