2016-05-31 Venia Legendi (CEITER): Adolfo Ruiz Calleja

A Learning Analytics infrastructure to promote educational innovation in Estonia

Adolfo Ruiz-Calleja31st, May 2016

Index

Previous experience

Current research work and interests

Envision research work in CEITER project

Conclusions

2

Index

Previous experience



Conclusions

3

Short CV 2007: Telecommunications Engineer

2007 – 2008: Master in Logic and Philosophy of Science

2008 – 2013: PhD student at Universidad de Valladolid

Sept 2013 – March 2014: Freelance

April 2014 – now : Researcher at Tallinn University

4

5

TECHNOLOGY METHODOLOGY& CONTEXT

LEARNING

2008 – 2009: Euricles project Ontology-based catalogue of services and

infrastructure elements for a telecommunication company Followed business standards: Shared

Information/Data Model (Brenner, Schaaf & Scherrer, 2009)

6

Ontologyengineering

Privatecompanies

Offer tools

Marie

77

Google Docs

MediaWiki

Hot potatoes

Group Scribbles

Learning situation

Educational ICT tool

pool

SELECT...tool...write ...

collaboration... peer review...

1.- Google Docs2.- MediaWiki

Activities

PeopleGoogle

Docs

??

I need a tool for my students to write a

document collaboratively in a peer review activity

Query: write, collaboration, peer review

Results

Educational ICT tool search system (semantic or keyword-based)

Registry of educational ICT tools

(semantic or not)

2010 – 2013: PhD work

TeacherEducational tool provides

2010 – 2013: PhD work

8

Consumption of Linked Open Data (Heath & Bizer, 2011)

Ontology mapping (Choi, Son & Han, 2006) Understanding teachers needs Participatory design

Linked DataOntologymapping

Participatorydesign

Teachersneeds

2010 – 2013: PhD work

9

Social-semanticinfrastructure

Web technologies

Teachercommunity

2010 – 2013: PhD work

10

Design and development of educational applications

Interface design Mixed methods evaluation (Tashakkori & Teddlie,

2010)

Interface design Mixed methodsMultidisciplinary

researchTeacher

community

2013 – 2014: Coin catalogue

11

Legacy data SMEs

Index

Previous experience



Conclusions

12

2014 – 2016: Learning Layers

13

Observe learning at the workplace (Eraut, 2004) Allow peer collaboration (Schmidt et al., 2009) Scaffolding for meaningful learning (Ley et al., 2010) Scale-up to regional clusters (Ley et al., 2013)

Scale up technologyEstonian context

European projectsDesign-based research

LearnersInformal/Formal learning

Scale up learningThree learning metaphors

2014 – 2016: Social Semantic Server

14

Micro-service Architecture

Actor-Artifact Network

Design-basedresearch

WorkplaceLearning Analytics

15

Ontology engineeringLinked Data

Web technologiesSocial-semantic infrastructures

Legacy dataScale up technology

Actor-Artifact NetworkMicro-Service architecture

Participatory designDesign-based research

Estonian contextEuropean projects

Working with companiesMultidisciplinary research

Focus on teachersFocus on learnersScale up learning

Formal/Informal learningThree learning metaphors

Workplace Learning Analytics

Index

Previous experience



Conclusions

16

Social Semantic Server

Collaboration with Graz University of Technology Workplace Learning Analytics Explore the potential of the SSS for the

industry

Collaboration with Karlsruhe University of Applied Science Maturity indicators

17

"Pilots and Business Models for Training in Industry using Social Semantic and Ubiquitous Technologies"

SHEILA

Offer a general policy development framework for LA to guide higher education institutions

Conduct case studies that follow the framework

Promotion of cross-institutional cooperation on sharing experiences 18

SHEILAOpportunity to work with highest-level

researchers

Understand LA initiatives at TLU

Get advice for initiatives that Estonia is a little bit behind Ethics committee

Understand how to implement LA in the institutional level

19

The CEITER projectCEITER project as a research

infrastructure

Goals: Research contributions in the TEL field Increase chances of getting research funding

for TLU Practical impact on Estonian education

Contextualized in Estonia and TLU Small and technical-advanced country Big effort in Life-Long learning Easy access to policy makers High chances for innovation

20

The CEITER project

21

ekool Stadium EHIS IASÕIS LMSs

CEITER INFRASTRUCTURE

Companies

Universities

Schools Ministry of education

Data InfrastructureAPI

Data model

Learning Analytics service DashboardPolicy support Best practices

A data infrastructure for CEITER

22


Data model

How to structure the data to promote the dataset integration for Learning Analytics?

Definition of an extensible information model

Data model based on Ontologies

Ontologyengineering

Participatorydesign


23


Data model

How to involve stakeholders into the data management cycle?

Social technologiesLearning metaphorsReuse/Propose vocabularies (RIHA

project)

Three learning metaphors

Social technologies

Design-based research

Formal/Informallearning


24


Data model

How to actually integrate datasets?Software architecture to process data, including:

– Vocabulary mapping– Identity resolution– Anonymization– Quality evaluation

Service OrientedArchitecture

OntologymappingLegacy data


25


Data model

How to offer the data? Use of open standards, such as xAPI (Whitaker, 2012) or Caliper (IMS Global, 2015)

Linked Data for Learning Analytics (d’Anquin & Jay, 2013)

Legal and ethical issues (Slade & Prinsloo, 2013)

Linked Data Ontology engineering

Focus on learners and

teachers

WorkplaceLearningAnalytics


26


Data model

How to scale it up to national level?Pay-as-you-go principle (Madhavan et al., 2007)(Micro-)Service architecture

Micro-ServiceArchitecture

Scale-up technology

Scale-up learning

Web technologies


27


Data model

How to promote the infrastructure to analyze and enhance educational innovation in Estonia?

Question-driven approach (Gasevic, Dawson and Siemens, 2015)

Maybe new data sources are required (sensors?)Design-based

researchEstonian context

Multidisciplinaryresearch

Focus on learners and

teachers


28


Data model

How to guarantee privacy and security?

Potential collaboration with CYBERNETICA (Cybernetica, 2016)

Working withcompanies


29


Data model

How to promote the use of the infrastructure from the policy level?

SHEILA project

Relationships and other projectsEuropean networks

Workplace Learning Analytics working group COST Action Proposal “Learning Analytics in

Digital Learning Ecosystems: Frameworks, Methods and Policies”

LACE project Estonian chapter of SOLAR

Potential project calls Swafs-11-2017: Science education outside the

classroom. DL: August 2017 MSCA-ITN-2017: Innovative Training Networks.

DL: January 201730

European projects

Index

Previous experience



Conclusions

31

ConclusionsMy role at CEITER as a continuation of my

current work at TLU Social-semantic infrastructuresWorkplace Learning Analytics

32

CEITER Learning Analytics infrastructure

Goes beyond a technical infrastructureResearch while having a practical

impactIntegrate other projects into CEITER

SHEILASocial-Semantic ServerNew proposals

A Social-Semantic Infrastructure to support Learning Analytics at Tallinn University

Adolfo Ruiz-Calleja31st, May 2016

Baeza-Yates, R. and Ribeiro-Neto, B. (1999). “Modern information retrieval” (1st edition). Vol. 463. New York: ACM press.

Buckingham, S. (2012) "Policy Brief: Learning Analytics". UNESCO Institute for Information Technologies in Education. http://iite.unesco.org/publications/3214711/, last visit May 2016.

Brenner, M., Schaaf, T. and Scherer, A. (2009) "Towards an information model for ITIL and ISO/IEC 20000 processes". In: Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management, IEEE, pp. 113-116.

Choi, N., Song, I.Y. and Han, H. (2006) "A survey on ontology mapping." ACM Sigmod Record 35(3): 34-41

Cybernetica (2016) “Sharemind. Analyze confidencial data without compromising security – Practical secure computation for companies and goberments”. http://cyber.ee/uploads/2016/03/Sharemind-2016-web.pdf last visit May 2016.

d'Aquin, M. and Jay, N. (2013). ”Interpreting data mining results with linked data for learning analytics: motivation, case study and directions”. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 155-164). ACM.

Duval, E. (2011): ”Attention please: Learning Analytics for visualization and recommendation”. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK), ACM, pp 9-17.

Eraut, M. (2004) ”Informal learning in the workplace”. Studies in continuing education, 26(2):247-273.

Gašević, D., Dawson, S. and Siemens, G. (2015). ”Let’s not forget: Learning analytics are about learning”. TechTrends, 59(1):64-71.

References

34

Gómez-Pérez, A., Fernández-López, M. and Corcho, O. (2006) ”Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web” (1st edition). Springer Science & Business Media.

Heath, T. and Bizer, C. (2011) ”Linked Data: Evolving the Web into a Global Data Space” (1st edition). Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1, 1-136. Morgan & Claypool.

IMS Global (2015). “Caliper Analytics™ Background”. https://www.imsglobal.org/activity/caliperram last visit May 2015.

Ley, T., Kump, B. and Gerdenitsch, C. (2010). ”Scaffolding self-directed learning with personalized learning goal recommendations”. In: User modeling, adaptation, and personalization (pp. 75-86). Springer Berlin Heidelberg.

Ley, T., Cook, J., Dennerlein, S., Kravcik, M., Kunzmann, C., Pata, K., Purma, J., Sandars, J., Santos, P., Schmidt, A. and Al‐Smadi, M. (2014) "Scaling informal learning at the workplace: A model and four designs from a large‐scale design‐based research effort" British Journal of Educational Technology 45(6): 1036-1048.

Madhavan, J., Jeffery, S., Cohen, S., Dong, X., Ko, D., Yu, C. and Halevy, A. (2007). “Web-scale data integration: You can only afford to pay as you go”. In: Proceedings of 6thConference on Innovative Data Systems Research (pp. 342-350).

Malcolm, J., Hodkinson, P. and Colley, H. (2003). "The interrelationships between informal and formal learning". Journal of workplace learning 15(7/8):313-318.

Mikroyannidis, A. (2007) "Toward a social semantic web” Computer 40(11):113-115.Newman, S. (2005) “Building Micro-services. Designing Fine-Grained Systems”. O’Relly Media.Paavola, S. and Hakkarainen, K. (2005). “The knowledge creation metaphor, an emergent

epistemological approach to learning”. Science & Education. 14(6):535-557.

References

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Schmidt, J. P., Geith, C., Håklev, S. and Thierstein, J. (2009) ”Peer-to-peer recognition of learning in open education”. The International Review of Research in Open and Distributed Learning, 10(5):1-16.

Siemens, G. (2011). ”Learning and Academic Analytics”. 5 August 2011 http://www.learninganalytics.net/?p=131, last visit May 2016

Slade, S. and Prinsloo, P. (2013). ”Learning analytics ethical issues and dilemmas”. American Behavioral Scientist, 57(10):1510-1529.

Tashakkori, A. and Teddlie, C. (2010) ”Sage handbook of mixed methods in social & behavioral research” (2nd edition). Los Angeles, USA:Sage.

Wang, F. and Hannafin, M. J. (2005). ”Design-based research and technology-enhanced learning environments”. Educational technology research and development, 53(4):5-23.

Whitaker, A. (2012). "An Introduction to the Tin Can API". The Training Business. 19 July 2012 http://www.thetrainingbusiness.com/softwaretools/tin-can-api/, last visit May 2016

References

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http://www.learninganalytics.net/?p=131