Data Interoperability for Learning Analytics and Lifelong Learning
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This document discusses the need for data interoperability to enable lifelong learning analytics. It notes that currently most learning analytics focus on understanding and optimizing formal learning environments rather than the learner perspective. The lack of interoperability between different education systems means data is often stored in incompatible ways, making analysis across platforms difficult. The document proposes using open standards like xAPI to improve interoperability and enable more personalized, lifelong learning through analytics. It highlights lessons from developing the Connected Learning Analytics Toolkit to process multi-source data, including the importance of context and using recipes to define common data structures.
Data Interoperability for Learning Analytics and Lifelong Learning
- 1. 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 kirsty.kitto@qut.edu.au @KirstyKitto www.beyondlms.org
- 2. 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
- 3. 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
- 5. N O I N T E R O P E R A B I L I T Y
- 6. W H AT H A P P E N S AT O N E P O I N T I N T H AT L E A R N I N G J O U R N E Y ?
- 8. 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/
- 9. T H E R E H A S T O B E A B E T T E R WAY
- 10. xAPI could be it but it would need to scale up fast
- 11. 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
- 12. 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
- 13. H O W ?
- 14. 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)
- 15. O N E R E P O S I T O RY T H AT I S P U B L I C LY AVA I L A B L E !
- 16. I N T E R E S T I N G A N A LY T I C S W I L L E N C O U R A G E I N T E R O P E R A B I L I T Y
- 17. C L A T O O L K I T D A S H B O A R D S Reports generated about - Activity - Social Networks - Content Analysis www.beyondlms.orgOpen dashboards and learning analyticsAnyone could use them if they have the same data structures…
- 18. 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
- 19. 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/
- 20. 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