This document proposes a metamodel for developing learning ecosystems based on the Model-Driven Architecture. The metamodel aims to improve the definition and development of learning ecosystems by providing a platform-independent solution. It defines key elements of learning ecosystems including software components, the human factor, and relationships between components. The metamodel is demonstrated through a case study modeling a learning ecosystem for knowledge management in a PhD program. The metamodel can be used as a basis for defining a UML profile and model transformations to generate ecosystem prototypes.

1. A metamodel proposal for developing learning
ecosystems
Alicia García-Holgado, Francisco J. García-Peñalvo
GRIAL Research Group,
Computer Sciences Department,
Research Institute for Educational Sciences,
University of Salamanca, Salamanca, Spain
aliciagh@usal.es, fgarcia@usal.es

2. Index
1. Which is a technological ecosystem?
2. Problems
3. Objectives
4. High-level requirements
5. Ecosystem metamodel
6. A case study: modeling a learning ecosystem for Scientific
Knowledge Management in a PhD Program
7. Conclusions
8. References

3. 1. Which is a technological ecosystem? (I)
• The technological ecosystems are the evolution of the
traditional information systems
• The learning ecosystems are a type of technological
ecosystems focus on learning management processes
• The metaphor of technological ecosystem transfers the main
properties of biological or natural ecosystems to the
technological area

4. 1. Which is a technological ecosystem? (II)
• The organisms of the
natural ecosystems are the
users and the software
components from a
technological point of view;
the relationships among
the organisms are the
information flows between
the components; and the
physical environment are
the mechanism to stablish
and support such flows
(García-Holgado & García-Peñalvo,
2013)

5. 1. Which is a technological ecosystem? (III)
• Technological ecosystems
should connect and relate
the different tools and
services that arise and
serve for the knowledge
management, building
technological ecosystems,
increasingly complex
internally, from the
semantic interoperability of
its components to
transparently provide more
functionality and simplicity
to its users

6. 2. Problems
• One of the main problems to define and develop
technological ecosystems is the need to adapt them to
different contexts or domains
• The development of a learning ecosystem is influenced by
many factors, both internal and external

7. 3. Objectives
• To improve the definition and development of learning ecosystems
providing a platform-independent solution
• The main objective of this work is to define a metamodel for
developing learning ecosystems following the Model-Driven
Architecture (MDA) proposed by the Object Management Group
(OMG)
• MDA provides a framework for software development that uses
models to describe the system to be built (Mellor et al., 2002)
• It is based on previous experiences in the development of service-
oriented architectures in eLearning (Casany et al., 2009; Conde et al., 2009a;
2009b;García-Peñalvo et al., 2011) and technological ecosystems for learning
(Cruz-Benito et al., 2014;García-Peñalvo et al., 2014; García-Holgado & García-Peñalvo, 2014; 2016;
García-Holgado et al.,2015a; 2015b)

8. 4. High-level requirements
• The metamodel shall enable capture of the high-level
description of the learning ecosystem components
• The metamodel shall enable capture of the human factor as
part of the learning ecosystem
• The metamodel shall enable capture of the information flows
between the learning ecosystem components
• The metamodel shall enable capture of the configurations of
the software components

9. 5. Ecosystem metamodel (I)
• Learning ecosystems modeling
involves the architectural pattern
proposed in (García-Holgado &
García-Peñalvo, 2016) with a top-
down scheme composed of four
layers: presentation, services, static
data management and
infrastructure; in addition to two
input data flows to introduce the
human factor as a key element
• Also, the pattern provides a set of
software components that should
be part of a learning ecosystem to
resolve some problems of this kind
of technological solutions

10. 5. Ecosystem metamodel (II)
• The metamodel is a
M2-level model in the
four-layer metamodel
architecture. The
Ecosystems
Metamodel is an
instance of the MOF
meta-metamodel
(M3-level model)

11. 5. Ecosystem metamodel (III)
Software components
Human factor
Relationship between components

12. 5. Ecosystem metamodel (IV)
• The metamodel is completed with a set of constraints de-fined
with Object Constraint Language (OCL)
• An ecosystem must have one mail server, one monitoring system,
one user management system, and at least one internal tool, one
management input stream, one methodology input stream and
one user
• The value of the endpoint attribute defined in the service
descriptions should be unique in the whole ecosystem
• A software tool cannot consume a service provided by itself, that
is to say, the information flows always involve two different
software tools
HCI 2017

13. 6. A case study: modeling a learning ecosystem for
Scientific Knowledge Management in a PhD
Program (I)

14. 6. A case study: modeling a learning ecosystem for
Scientific Knowledge Management in a PhD
Program (II)

15. 6. A case study: modeling a learning ecosystem for
Scientific Knowledge Management in a PhD
Program (III)

16. 7. Conclusions
• This paper presents an ecosystems metamodel based on MOF to
model different perspectives of learning ecosystems development
• The metamodel have been used to model a real learning ecosystem for
a PhD Program, which have been transferred to other domains
• The modeling example demonstrate how a real ecosystem could be
modeled using the Ecosystems Metamodel
• This work provides a base to define a UML profile for the Ecosystems
Metamodel in order to leverage existing tool support
• A set of transformation rules using QVT could be defined to transform
the models into executable specifications to provide an ecosystem
prototype

17. 8. References (I)
• Casany, M. J., Alier, M., Conde, M. Á., & García-Peñalvo, F. J. (2009). SOA initiatives for eLearning. A Moodle case 23rd
International Conference on Advanced Information Networking and Applications, AINA 2009, Workshops Proceedings. The
International Symposium on Mining and Web (MAW 2009) (pp. 750-755). Los Alamitos, California, USA: IEEE Computer Society.
• Conde, M. Á., García-Peñalvo, F. J., Casany, M. J., & Alier, M. (2009b). Adapting LMS architecture to the SOA: an Architectural
Approach. In H. Sasaki, G. O. Bellot, M. Ehmann, & O. Dini (Eds.), Proceedings of the Fourth International Conference on Internet
and Web Applications and Services – ICIW 2009 (Venice/Mestre, Italy, 24-28 May 2009) (pp. 322-327). Los Alamitos, California,
USA: IEEE Computer Society.
• Conde, M. Á., Muñoz, C., & García-Peñalvo, F. J. (2008). mLearning, the First Step in the Learning Process Revolution.
International Journal of Interactive Mobile Technologies (iJIM), 2(4), 61-63.
• García-Holgado, A., & García-Peñalvo, F. J. (2013). The evolution of the technological ecosystems: an architectural proposal to
enhancing learning processes Proceedings of the First International Conference on Technological Ecosystem for Enhancing
Multiculturality (TEEM'13) (Salamanca, Spain, November 14-15, 2013) (pp. 565-571). New York, NY, USA: ACM.
• Cruz-Benito, J., Therón, R., & García-Peñalvo, F. J. (2014). Analytics of information flows and decision making in heterogeneous
learning ecosystems. In F. J. García-Peñalvo (Ed.), Proceedings of the Second International Conference on Technological
Ecosystems for Enhancing Multiculturality (TEEM’14) (pp. 703-707). New York, USA: ACM.
• García-Holgado, A., & García-Peñalvo, F. J. (2014a). Architectural pattern for the definition of eLearning ecosystems based on
Open Source developments. In J. L. Sierra-Rodríguez, J. M. Dodero-Beardo, & D. Burgos (Eds.), Proceedings of 2014 International
Symposium on Computers in Education (SIIE), Logrono, La Rioja, Spain, 12-14 Nov. 2014 (pp. 93-98). USA: Institute of Electrical
and Electronics Engineers.

18. 8. References (II)
• García-Holgado, A., & García-Peñalvo, F. J. (2016). Architectural pattern to improve the definition and
implementation of eLearning ecosystems. Science of Computer Programming, 129, 20-34.
doi:10.1016/j.scico.2016.03.010
• García-Holgado, A., García-Peñalvo, F. J., Hernández-García, Á., & Llorens-Largo, F. (2015a). Analysis and
Improvement of Knowledge Management Processes in Organizations Using the Business Process Model
Notation. In D. Palacios-Marqués, D. Ribeiro Soriano, & K. H. Huarng (Eds.), New Information and
Communication Technologies for Knowledge Management in Organizations. 5th Global Innovation and
Knowledge Academy Conference, GIKA 2015, Valencia, Spain, July 14-16, 2015, Proceedings (pp. 93-101).
Switzerland: Springer International Publishing.
• García-Holgado, A., García-Peñalvo, F. J., & Rodríguez-Conde, M. J. (2015b). Definition of a Technological
Ecosystem for Scientific Knowledge Management in a PhD Programme. In G. R. Alves & M. C. Felgueiras (Eds.),
Proceedings of the Third International Conference on Technological Ecosystems for Enhancing Multiculturality
(TEEM’15) (Porto, Portugal, October 7-9, 2015) (pp. 695-700). New York, NY, USA: ACM.
• García-Peñalvo, F. J., Conde, M. A., Alier, M., & Casany, M. J. (2011). Opening Learning Management Systems
to Personal Learning Environments. Journal of Universal Computer Science, 17(9), 1222-1240.
doi:10.3217/jucs-017-09-1222.
• Mellor, S. J., Scott, K., Uhl, A., & Weise, D. (2002). Model-Driven Architecture. In J.-M. Bruel & Z. Bellahsene
(Eds.), Advances in Object-Oriented Information Systems: OOIS 2002 Workshops Montpellier, France,
September 2, 2002 Proceedings (pp. 290-297). Berlin, Heidelberg: Springer Berlin Heidelberg.

19. A metamodel proposal for developing learning
ecosystems
Alicia García-Holgado, Francisco J. García-Peñalvo
GRIAL Research Group, Computer Sciences Department,
Research Institute for Educational Sciences,
University of Salamanca, Salamanca, Spain
aliciagh@usal.es, fgarcia@usal.es