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Towards Open Architectures and
Interoperability for Learning Analytics
Tore Hoel
Oslo and Akershus University College of Applied Sciences
Norway
Institutional Readiness Day, Amsterdam - 2015-12-10
LACE Reports
on Interoperability and Data Sharing
Quick Guides to who is doing
standards and what standards
should be considered
New Report on Interoperability
and Data Sharing:
Requirements, Specification,
Adoption and Practice
3
What are the keys to make LA work?
• Access to data
• Institutional strategies
• Good predictive models
• Engagement and trust among students and faculty
• Interoperability standards
• Well designed tools
4
What is the stumbling block?
Lack of trust
5
The Learning Analytics
Landscape
What
standards
are
needed?
6
An architecture for learning analytics
Which pedagogical scenarios
are we able to accommodate
in an Open LA Architecture?
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?
7
(Cope, B., & Kalantzis, M., 2015)
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
8
(Cope, B., & Kalantzis, M., 2015)
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
9
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.
10
Challenges of design of new interoperable
solutions
• Understanding the processes
• Understanding where the data come from
• Piloting new solutions
• Working with standards organisations to ensure interoperability
• Industry consortia
• Advanced Distributed Learning (ADL)
• IMS Global Learning
• Apereo
• Formal standardisation
• ISO/IEC JTC 1/SC36 Working Group 8 on Learning Analytics
• CEN?
11
Initial understanding of LA process
( ISO/IEC JTC 1/SC36/WG8)
12Draft figure from new LA Framework model
Updated understanding of LA process
(SC36/WG8)
13Draft figure from new LA Framework model
A European Agenda for LA Interoperability?
• European requirements?
• xAPI and IEEE – the European stumbling block
• IMS Caliper – US vendor centric development
• Who will provide leadership in standardisation in Europe?
• CEN instruments are put on hold
• No pan-European instruments for harmonisation in LA
• JISC OLAA is a trail blazer for exploring this field and provide
leadership for Higher Ed in a particular country
• SOLAR and Apereo Foundation offer opportunities for
collaboration between research community and standards
experts
Hoel, T. (2015). Towards Open Architectures and Interoperability for Learning
Analytics. Presentation at Institutional Readiness Days, Amsterdam, the
Netherlands, 2015-12-10
The European LACE project builds a Community of Interest on Learning
Analytics – check out laceproject.eu
tore.hoel@hioa.no
@tore
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh
Framework Programme, grant 619424.
These slides are provided under the Creative Commons Attribution Licence:
http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
www.laceproject.eu
@laceproject
16

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Towards Open Architectures and Interoperability for Learning Analytics

  • 1. Towards Open Architectures and Interoperability for Learning Analytics Tore Hoel Oslo and Akershus University College of Applied Sciences Norway Institutional Readiness Day, Amsterdam - 2015-12-10
  • 2. LACE Reports on Interoperability and Data Sharing Quick Guides to who is doing standards and what standards should be considered New Report on Interoperability and Data Sharing: Requirements, Specification, Adoption and Practice
  • 3. 3
  • 4. What are the keys to make LA work? • Access to data • Institutional strategies • Good predictive models • Engagement and trust among students and faculty • Interoperability standards • Well designed tools 4 What is the stumbling block? Lack of trust
  • 6. 6 An architecture for learning analytics Which pedagogical scenarios are we able to accommodate in an Open LA Architecture?
  • 7. 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? 7 (Cope, B., & Kalantzis, M., 2015)
  • 8. 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 8 (Cope, B., & Kalantzis, M., 2015)
  • 9. 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 9
  • 10. 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. 10
  • 11. Challenges of design of new interoperable solutions • Understanding the processes • Understanding where the data come from • Piloting new solutions • Working with standards organisations to ensure interoperability • Industry consortia • Advanced Distributed Learning (ADL) • IMS Global Learning • Apereo • Formal standardisation • ISO/IEC JTC 1/SC36 Working Group 8 on Learning Analytics • CEN? 11
  • 12. Initial understanding of LA process ( ISO/IEC JTC 1/SC36/WG8) 12Draft figure from new LA Framework model
  • 13. Updated understanding of LA process (SC36/WG8) 13Draft figure from new LA Framework model
  • 14. A European Agenda for LA Interoperability? • European requirements? • xAPI and IEEE – the European stumbling block • IMS Caliper – US vendor centric development • Who will provide leadership in standardisation in Europe? • CEN instruments are put on hold • No pan-European instruments for harmonisation in LA • JISC OLAA is a trail blazer for exploring this field and provide leadership for Higher Ed in a particular country • SOLAR and Apereo Foundation offer opportunities for collaboration between research community and standards experts
  • 15. Hoel, T. (2015). Towards Open Architectures and Interoperability for Learning Analytics. Presentation at Institutional Readiness Days, Amsterdam, the Netherlands, 2015-12-10 The European LACE project builds a Community of Interest on Learning Analytics – check out laceproject.eu tore.hoel@hioa.no @tore This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424. These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms. www.laceproject.eu @laceproject 16