1) The document discusses opportunities for standardization in learning analytics, including harmonizing activity stream specifications, building vocabularies, storage designs, privacy and data protection, and sharing algorithms and predictive models. 2) It analyzes characteristics of educational big data like varied data sources and formats, and calls for standards to bring these diverse data together and make them interoperable and meaningful for learners and teachers. 3) The document reviews several emerging specifications and tools in learning analytics, and identifies challenges for standardization in areas like privacy, personal data stores, data analysis, and sharing models and algorithms.

1. Learning Analytics –
Opportunities for ISO/IEC JTC 1/SC36
standardisation
Tore Hoel
Oslo and Akershus University College of Applied Sciences
Norway
ISO/IEC JTC 1/SC36 WG8 meeting, 29 November 2015
Hangzhou, China

2. 2
The Learning Analytics
Landscape
What
standards
are
needed?

3. Characteristics of Educational Big Data
• Grain size of recordable and analysable data has become smaller
– every pen stroke, every keystroke is recorded
• Sources of evidence are (more) varied
– tests, essay scoring, learning games, social interactions, affects,
body sensors, intelligent tutors, simulations, semantic mapping,
LMS data…
– Unstructured (e.g., . log files, clicks, timestamps)
– When structured different schemas are used
• How do we bring these data together to form a overall view of an
individual learner or a cohort of learners?
3
(Cope, B., & Kalantzis, M., 2015)

4. What data practices are emerging?
• Multi-scalar Data Collection
– Embedded, simultaneous collection of data that can be used
for different purposes at different scales
– Semantically legible datapoint (learner-actionable feedback):
«teachable moment»
• Self-describing, structured data ➔ meanings immediately evident
to learners, teachers, others
• Sample size n= all
• Data and interventions are not separate: Recursive micro
intervention ➔ result➔ redesign cycles
• More widely distributed data collection roles
4
(Cope, B., & Kalantzis, M., 2015)

5. Need for new Education Data Standards
supporting Learning Analytics
• Harmonization of Activity Stream Specifications (ADL xAPI, IMS
Caliper, W3C Activity Streams)
• Building Vocabularies – Profiles – Recipes – Communities of
Practice
• Storage designs – centralised data warehouses or distributed
Learning Record/Event Stores
• Extract, Transform and Load (ETL) tools for data storage
• Privacy and Data Protection – how to do Privacy-by-design in this
field?
• Sharing of Algorithms and Predictive Models
5

6. Harmonization of Activity Streams
6

7. Activity Streams
• Work started around 2009 by a group from IBM, Google,
Microsoft, MySpace, Facebook, VMware a.o.,
• First version published in 2011
• 2014 W3C Social Web Working Group took over the specification
• Working draft version 2.0 published October 2015
7
In its simplest form, an activity consists of an actor, a verb, an an
object, and a target. It tells the story of a person performing an action
on or with an object -- "Geraldine posted a photo to her album" or
"John shared a video". In most cases these components will be explicit,
but they may also be implied.
(Activity Streams Working Group, 2011)

8. Experience API (xAPI)
• 1st version 2013 (component of ADL Training and Learning
Architecture)
• A Statement consists of an <actor (learner)>, a <verb>, an
<object>, with a <result>, in a <context>. There is no constraint on
what these objects should be.
• Learning Record Store: a system that stores learning information
• xAPI is dependent on the presence of LRS to function
• Offered for standardisation in IEEE August 2014 – “it wasn’t the
slam dunk [they were] naively hoping it would be” (Silvers, 2014)
• End of 2015 a new Data Interoperability Standards Consortium
(not-for-profit organization in the State of Pennsylvania, USA) to
be the steward of Experience API
8

9. IMS Caliper Analytics
• White paper 2013
• Public release v 1.0 October 2015
• Information model buried in Sensor APIs
• Metric Profiles
• Base Metric Profile, Session, Annotation, Assignable,
Assessment, Outcome, Reading, Media
• IMS Learning Sensor API: defines basic learning events gathered
as learning metics across learning environments
• Leveraging of IMS LTI/LIS/QTI
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10. IMS Caliper
10

11. 11
Source: Yong-Sang Cho

12. Vocabularies
12

13. Talking about learning activities
• Looser coupled systems, diverse Communities of Practice lead to
more diverse schemas and data models
• Interoperability could be promoted by more efficient sharing of
vocabularies
• Encourage smaller vocabularies / ontologies
13
IMS Caliper
xAPI Communities

14. How to promote more interoperable
vocabularies for education?
• "Document standards" for vocabularies have severe limitations!
• Communities of Practice (ref xAPI) are part of the solution…
• … but serious stewardship issues
• What could ISO offer in terms of dynamic vocabulary
management?
14

15. Storage
15

16. Apereo Dimond model
16

17. Search architecture middle layer
17(Hoel & Chen, 2015)

18. MIT Open Personal Data Store / Safe
Answers
• openPDS allows users to
collect, store, and give fine-
grained access to their data in
the cloud.
• openPDS also protects users’
privacy by only sharing
anonymous answers, not raw
data.
• openPDS can also engage in
privacy-preserving group
computations to aggregate
data across users without the
need to share sensitive data
with an intermediate entity. 18
http://openpds.media.mit.edu/#architecture

19. Extract - Transform - Load tools
• When data are coming from different sources in different
structures, one need tools to extract, transform and load data
into data stores
• There are Open Source ( e.g., Pentaho Kettle and Talend), but
most are commercial
software
• Are ETL tools a possible
hot spot for standards
efforts?
19

20. SC36 20748-1 Data Storing & Processing
20

21. Challenges for standardisation
• Privacy and Data Ownership issues – how to turn these «soft»
requirements into «hard» ones?
• The role of Personal Data Stores in Learning Analytics
• Harmonization of data schemes prior to analysis
• Import / export facilities with ontology building (and automatic
reasoning technologies) as part of the storage solutions
• Publishing and Sharing of data for research and comparison and
testing of predictive models, student models, etc.
21

22. Privacy
22

23. Implications for designs when Surveillance
turns into Sousveillance?
23
Image credit: http://commons.wikimedia.org/wiki/File:SurSousVeillanceByStephanieMannAge6.png

24. When Privacy is affecting all LA processes
• Privacy-By-Design is the
overall design principle. What
does it mean for the LA
processes?
• Data Sharing
• Search
• Storing
• Analysing
• Visualising
24

25. Sharing Algorithms &
Predictive Models
25

26. How to support sharing?
• Exemplar predictive models are needed to advance learning
analytics
• Besides a Culture for sharing data, algorithms and predictive
models, what else is needed?
• Parallel data streams from production systems to support
development and research
• How to deal with anonymization?
• How to get data for R&D from cloud-based systems?
• How do we talk about these algorithms and models (create a
vocabulary for tagging)
• Where to host the resources (stewardship, openness policies,
open repositories)
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27. References
• Cho, Yong-Sang (2015) Quick review xAPI and IMS Caliper -
Principle of both data capturing technologies. Online at
http://www.slideshare.net/zzosang/quick-review-xapi-and-ims-
caliper-principle-of-both-data-capturing-technologies
• Cope, B., & Kalantzis, M. (2015). Sources of Evidence-of-Learning:
Learning and assessment in the era of big data. Open Review of
Educational Research, 2(1)
• Hoel, T. & Chen, W. (2015). Privacy in Learning Analytics –
Implications for System Architecture. In Watanabe, T. and Seta, K.
(Eds.) Proceedings of the 11th International Conference on
Knowledge Management. Online at
http://hoel.nu/publications/Hoel_Chen_ICKM15_final_preprint.p
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