Future Trends in the Design Strategies 
and Technological Affordances
of E-Learning
Begoña Gros and Francisco J. García-Peñalvo
Abstract
E-learning has become an increasingly important learning and teaching mode in
recent decades and has been recognized as an efficient and effective learning
method. The rapidly rising number of Internet users with smartphones and tablets
around the world has supported the spread of e-learning, not only in higher
education and vocational training but also in primary and secondary schools.
E-learning and traditional distance education approaches share the emphasis
on “any time, any place” learning and the assumption that students are at a
distance from the instructor. The design of the initial e-learning courses tended
to replicate existing distance education practice based on content delivery. How-
ever, long textual lectures were clearly not suitable for the online environment.
These early insights guided the development of e-learning (technical and peda-
gogical) and emphasized the need for communication and interaction.
B. Gros
Universidad de Barcelona, Barcelona, Spain 
e-mail: bgros@ub.edu
F.J. García-Peñalvo
Universidad de Salamanca, Salamanca, Spain
e-mail: fgarcia@usal.es
Gros, B., & García-Peñalvo, F. J. (2016). Future trends in the design strategies and technological affordances of e-learning. In M. 
Spector, B. B. Lockee, & M. D. Childress (Eds.), Learning, Design, and Technology. An International Compendium of Theory, Research, 
Practice, and Policy (pp. 1-23). Switzerland: Springer International Publishing. doi:10.1007/978-3-319-17727-4_67-1
E-learning describes learning delivered fully online where technology medi-
ates the learning process, teaching is delivered entirely via Internet, and students
and instructors are not required to be available at the same time and place.
E-learning practices are evolving with the mutual influence of technological
e-learning platforms and pedagogical models. Today, the broad penetration and
consolidation of e-learning needs to advance and open up to support new
possibilities. Future e-learning should encompass the use of Internet technologies
for both formal and informal learning by leveraging different services and
applications.
The purpose of this chapter is to provide a general analysis of the evolution
and future trends in e-learning. The authors intend to summarize findings from
contemporary research into e-learning in order to understand its current state and
to identify the main challenges in the technological and pedagogical affordances
of e-learning.
Keywords
E-learning development • E-learning technology • E-learning models • Learning
digital ecosystems
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Introduction
Advances in educational technology and an increasing interest in the development of
asynchronous spaces influenced the rise of the term e-learning in the mid-1990s as a
way to describe learning delivered entirely online where technology mediates the
learning process. The pedagogical design and technology behind e-learning have
gradually evolved to provide support and facilitate learning.
E-learning has become an increasingly important learning and teaching mode, not
only in open and distance learning institutes but also in conventional universities,
continuing education institutions and corporate training, and it has recently spread to
primary and secondary schools. Moreover, greater access to technological resources
is providing e-learning not only in formal education but also in informal learning.
The evolution of e-learning has evolved from instructor-centered (traditional
classroom) to student-centered approaches, where students have more responsibility
for their learning. This evolution has been made possible due to the technological
platforms that support e-learning. Learning management systems (LMS) provide the
framework to handle all aspects of the e-learning process. An LMS is the infrastruc-
ture that delivers and manages instructional content, identifies and assesses individ-
ual and organizational learning or training goals, tracks progress toward meeting
those goals, and collects and presents data to support the learning process.
It is also important to stress the influence of social media on users’ daily habits, as
this has led to increased demand for learning personalization, social resources to
interact with peers, and unlimited access to resources and information (Siemens,
2014). Moreover, e-learning is also being called on to offer flexibility in the way and
place people learn and permit a natural and necessary coexistence of both formal and
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informal learning flows. Thus, the “traditional” e-learning platforms, despite their
extensive penetration and consolidation, need to evolve and open themselves up to
supporting these new affordances to become another component within a complex
digital ecosystem. This, in turn, will become much more than a sum of its indepen-
dent technological components due to the interoperability and evolution properties
orientated to learning and knowledge management, both at institutional and personal
levels.
The continued growth and interest in e-learning have raised many questions
related to learning design and technology to support asynchronous learning: What
are the best instructional models in online settings? How have the roles of instructors
and learners evolved? What are the most appropriate forms of interaction and
communication? How can formal and informal learning be combined? What is the
most appropriate technology to support e-learning? The main goal of this chapter is
to describe the evolution of e-learning and to analyze the current situation and future
trends in the design strategies and technological affordances of e-learning.
The chapter is divided into four sections. Firstly, we describe the meaning of the
term e-learning and its evolution from the early 1990s until today. In the second part,
we focus on the evolution of pedagogical approaches in e-learning. The third part
analyzes learning technologies with particular emphasis on the development of the
learning ecosystem as a technological platform that can provide better services than
traditional LMS. Finally, in the fourth part, based on the resulting analysis, the
authors offer some general remarks about the future of e-learning.
The Concept of E-Learning
In this section we analyze the meaning of the term e-learning in relation to other
similar terminologies (distance education, online learning, virtual learning, etc.) and
the evolution of e-learning generations from the early 1990s until today.
Evolution of the Concept
A major confusion in the discourse on e-learning is its blurring with distance
education: e-learning and distance education are not synonymous. Distance educa-
tion can be traced back to ancient times, whereas e-learning is a relatively new
phenomenon associated with the development of the Internet in the 1990s. However,
it is undeniable that the origins of e-learning lie in distance education and share the
idea that the use of media can support massive learning without face-to-face
interaction.
The first documented example of training by correspondence (as distance educa-
tion was known for many years) dates back to 1828, when Professor C. Phillips
published an advertisement in the Boston Gazette offering teaching materials and
tutorials by correspondence. In 1843, the Phonographic Correspondence Society
was founded, which could be considered the first official distance education
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institution as it would receive, correct, and return shorthand exercises completed by
students following a correspondence course.
The idea that technology such as radio and television could be used to bring
education to a wide audience began to surface as long ago as the 1920s, but it was not
until the early 1960s that the idea gained momentum, with the landmark creation of
the Open University in the UK, with a manifesto commitment in 1966 that became a
reality in 1971 when this university started to accept its first students.
The e-learning concept has evolved alongside the evolution of its supporting
technology, from the early concept linked to the introduction of personal computers
up to today’s distributed systems, which have favored learning networks and the
roots of connectivism (Siemens, 2005). However, the most outstanding and impor-
tant event in the history of e-learning is the emergence of the Web, after which the
evolution of the e-learning model has been inextricably linked to the evolution of the
Web (García-Peñalvo & Seoane-Pardo, 2015).
When a time approach is used to classify e-learning models according to their
technological evolution, the most suitable metaphors are generations (Downes,
2012; García-Peñalvo & Seoane-Pardo, 2015; Garrison & Anderson, 2003; Gros
et al., 2009) or timelines (Conole, 2013), as opposed to other taxonomies that use
variables such as centrality (Anderson, 2008) or the pedagogical model (Anderson &
Dron, 2011).
Garrison and Anderson (2003) refer to five stages, or generations, of e-learning,
each with its own theoretical model. The first is based on a behaviorist approach; the
second appears as a result of the influence of new technologies and an increasing
acceptance of the cognitive theory, including strategies focused on independent
study; the third generation is based on constructivist theories and centers on the
advantages of synchronous and asynchronous human interaction; the fourth and fifth
generations have no theoretical background, and the authors considered that their
main characteristics were not yet present in training programs, but they would be
based on a huge volume of content and distributed computer processing to achieve a
more flexible and intelligent learning model.
Gros et al. (2009) present three generations, each with a different e-learning
model. The first generation is associated with a model focused on materials, includ-
ing physical materials enriched with digital formats and clearly influenced by the
book metaphor. The second generation is based on learning management systems
(LMS) inspired by the classroom metaphor, in which huge amounts of online
resources are produced to complement other educational resources available on the
Internet known as learning objects (Morales, García-Peñalvo, & Barrón, 2007;
Wiley, 2002). In this generation the interaction dynamics start through messaging
systems and discussion forums. The third generation is characterized by a model
centered on flexibility and participation; the online content is more specialized and
combines materials created both by the institution and the students. Reflection-
orientated tools, such as e-portfolios and blogs (Tan & Loughlin, 2014), and more
interactive activities, such as games (Minović, García-Peñalvo, & Kearney, 2016;
Sánchez i Peris, 2015), are also introduced to enrich the learning experience with a
special orientation toward the learning communities model (Wenger, 1998). In
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addition, web-based solutions are expanded to other devices which leads to the
development of mobile learning training activities (Sánchez Prieto, Olmos
Migueláñez, & García-Peñalvo, 2014).
Stephen Downes (2012) starts with a generation zero based on the concept of
publishing multimedia online resources with the idea that computers can present
content and activities in a sequence determined by the students’ choices and by the
results of online interactions, such as tests and quizzes. This foundational basis is the
point of departure for all subsequent developments in the field of online learning.
Generation one is based on the idea of the network itself, with tools such as websites,
e-mail, or gopher to allow connection and virtual communication through special-
ized software and hardware. Generation two takes place in the early 1990s and is
essentially the application of computer games to online learning. Generation three
places LMS at the center of e-learning, connecting the contents of generation zero
with the generation one platform, the Web. Generation four is promoted by the Web
2.0 concept, which in online education is known as e-learning 2.0 (Downes, 2005).
One of the most significant characteristics of e-learning 2.0 is the social interaction
among learners, changing the nature of the underlying network where the nodes are
now people instead of computers. This social orientation also causes a real prolifer-
ation of mobile access and the exploitation of more ubiquitous approaches in
education and training (Casany, Alier, Mayol, Conde, & García-Peñalvo, 2013).
Generation five is the cloud-computing generation (Subashini & Kavitha, 2011) and
the open-content generation (García-Peñalvo, García de Figuerola, & Merlo-Vega,
2010; McGreal, Kinuthia, & Marshall, 2013; Ramírez Montoya, 2015). Finally,
generation six is fully centered on Massive Open Online Courses (MOOC) (Daniel,
Vázquez Cano, & Gisbert, 2015; SCOPEO, 2013).
Gráinne Conole (2013) presents a timeline to introduce the key technological
developments in online education over the last 30 years (see Fig. 1).
E-Learning Generations
Based on the generation metaphor presented above, García-Peñalvo and Seoane-
Pardo (García-Peñalvo & Seoane-Pardo, 2015) reviewed the e-learning conceptual-
ization and definition according to three different generations or stages that are
consistent with the broad proposals of the different authors and particularly with
Stephens Downes’ idea that generations are not replaced but coexist, and the
maturity of the first brings the evolution of the following and the emergence of
new generations (Downes, 2012). In fact, the term “e-learning” have been used as a
teaching and learning method but also as a learning and teaching approach.
The first generation is characterized by the emergence of online learning plat-
forms or LMS as the evolution of a more generic concept of the virtual learning
environments that were set up after the Web appeared, with the broad (and poor) idea
that e-learning is a kind of teaching that uses computers (Mayer, 2003). These
learning environments are too centered on content and overlook interaction. The
technological context is more important than the pedagogical issues. The classic
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definitions of e-learning are generally associated with this e-learning generation. For
example, Betty Collis (1996) defines tele-learning as “making connections among
persons and resources through communication technologies for learning-related
purposes.” Marc Rosenberg (2001) confines e-learning to the Internet as the use of
Internet technologies to deliver a broad array of solutions that enhance knowledge
and performance. He bases his idea on three fundamental criteria: (1) networked,
(2) delivered to the end user via a computer using standard Internet technology, and
(3) focused on the broadest view of learning. García-Peñalvo (2005) defines
e-learning with a perspective focused on interaction, a characteristic of the next
generation, “non-presential teaching through technology platforms that provides
flexible access any time to the teaching and learning process, adapting to each
student’s skills, needs and availability; it also ensures collaborative learning envi-
ronments by using synchronous and asynchronous communication tools, enhancing
in sum the competency-based management process.”
The second generation underlines the human factor. Interaction between peers
and communication among teachers and students is the essential elements for high-
quality e-learning that seeks to go beyond a simple content publication process. Web
2.0, mobile technologies, and open knowledge movement are significant factors that
help this e-learning generation to grow. Based on this, LMS evolved to support
socialization, mobility, and data interoperability facilities (Conde et al., 2014).
Examples of e-learning definitions that are congruent with these second generation
principles include: “training delivered on a digital device such as a smart phone or a
laptop computer that is designed to support individual learning or organisational
performance goals” (R. C. Clark & Mayer, 2011) or “teaching-to-learning process
aimed at obtaining a set of skills and competences from students, trying to ensure the
Multimedia resources
The Web
Learning objects
Learning Management Systems
Mobile devices
Learning Design
Gaming technologies
Open Educational Resources
Social and participatory media
Virtual worlds
eBooks and smart devices
Massive Open Online Courses
Learning Analytics
80s
93
94
95
98
99
00
01
04
05
07
08
10
Fig. 1 The e-learning
timeline adapted from Conole,
2013
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highest quality in the whole process, thanks to: predominant use of web-based
technologies; a set of sequenced and structured contents based on pre-defined but
flexible strategies; interaction with the group of students and tutors; appropriate
evaluation procedures, both of learning results and the whole learning process; a
collaborative working environment with space-and-time deferred presence; and
finally a sum of value-added technological services in order to achieve maximum
interaction” (García-Peñalvo, 2008).
The third and last generation of e-learning is characterized by two symbiotic
aspects. The first is technological: the LMS concept as a unique and monolithic
component for online education functionality is broken (Conde-González, García-
Peñalvo, Rodríguez-Conde, Alier, & García-Holgado, 2014). Since the emergence
of Web 2.0 and social tools, the e-learning platform has become another component
in a technological ecosystem orientated toward the learning process (García-
Holgado & García-Peñalvo, 2013), transcending the mere accumulation of trending
technology. This learning ecosystem should facilitate interaction and offer greater
flexibility for any educational teaching.
The second aspect implies a loss of verticality in the e-learning concept to become
a broader and more transverse element that is at the service of education in its wider
sense. Both from an intentional (formal and informal) and unintentional (informal)
view, learning ecosystems are at the service of people involved in teaching and
learning processes or in self-learning. Thus, e-learning is integrated into educational
designs or learning activities in a transparent way. It reveals the penetration of
technology into people’s everyday lives, making it easier to break down the barriers
between formal and informal learning (Griffiths & García-Peñalvo, 2016).
Technological learning ecosystems facilitate this globalization of the e-learning
notion, either to support an institutional context (García-Holgado & García-
Peñalvo, 2014; García-Peñalvo, Johnson, Ribeiro Alves, Minovic, & Conde-
González, 2014; Hirsch & Ng, 2011) or a personal one through the concept, more
metaphorical than technological, of the personal learning environment (PLE) (Wil-
son et al., 2007).
Nevertheless, technological learning ecosystems are supporting other approaches
to using technology in the classrooms, such as flipped teaching (Baker, 2000; Lage,
Platt, & Treglia, 2000). Flipped teaching methodology is based on two key actions:
moving activities that are usually done in the classroom (such as master lectures) to
the home and moving those that are usually done at home (e.g., homework) into the
classroom (García-Peñalvo, Fidalgo-Blanco, Sein-Echaluce Lacleta, & Conde-
González, 2016). The Observatory of Education Innovation at the Tecnológico de
Monterrey (2014) has also detected a tendency to integrate inverted learning with
other approaches, for example, combining peer instruction (Fulton, 2014), self-
paced learning according to objectives, adaptive learning (Lerís López, Vea
Muniesa, & Velamazán Gimeno, 2015), and the use of leisure to learn. Thus, the
flipped teaching model is based on the idea of increasing interaction among students
and developing their responsibility for their own learning (Bergmann & Sams, 2012)
using virtual learning environments as supported tools. These virtual environments
allow students to access learning resources, ask questions, and share material in
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forums, as it is mandatory for students to have help available while studying at home
(Yoshida, 2016).
In this last stage, the MOOC concept has broken out strongly, perhaps with no
new e-learning approach, but with sufficient impact to make institutions reflect on
their e-learning processes and conceptions.
The term MOOC appeared for the first time in 2008 to describe the connectivism
and connected knowledge course by George Siemens and others (http://cckno8.
wordpress.com). This course gave rise to cMOOCs, where “c”means that the course
is based on the connectivist approach (Siemens, 2005). A second type of MOOC
appeared in 2011 under the name xMOOC, which is based on digital content and
individualized learning as opposed to cMOOCs, which are more related to collab-
orative learning. There is currently a great deal of interest in MOOCs among the
e-learning community. Other proposals for improving MOOCs have introduced the
use of associated learning communities (Alario-Hoyos et al., 2013), adaptive capa-
bilities (Fidalgo-Blanco, García-Peñalvo, & Sein-Echaluce Lacleta, 2013; Sein-
Echaluce Lacleta, Fidalgo-Blanco, García-Peñalvo, & Conde-González, 2016;
Sonwalkar, 2013), and gamification capabilities (Borrás Gené, Martínez-Nuñez, &
Fidalgo-Blanco, 2016).
However, the existing dichotomy between cMOOCs and xMOOCs is questioned
by different authors due to its limitations. Thus, Lina Lane (2012) proposes the
sMOOC (skill MOOC) as a third kind of MOOC based on tasks; Stephen Downes
(2013) suggests four criteria to describe an MOOC’s nature, autonomy, diversity,
openness, and interactivity; Donald Clark (2013) defines a taxonomy with eight
types of MOOC, transferMOOC, madeMOOC, synchMOOC, asynchMOOC,
adaptiveMOOC, groupMOOC, connectivistMOOC, and miniMOOC; and finally
Conole (2013) provides 12 dimensions to classify MOOCs, openness, massivity,
multimedia usage, communication density, collaboration degree, learning path,
quality assurance, reflection degree, accreditation, formality, autonomy, and
diversity.
With regard to the core elements that define this third generation, García-Peñalvo
and Seoane-Pardo (2015, 5) propose a new definition of e-learning as “an educa-
tional process, with an intentional or unintentional nature, aimed at acquiring a range
of skills and abilities in a social context, which takes place in a technological
ecosystem where different profiles of users interact sharing contents, activities and
experiences; besides in formal learning situations it must be tutored by teachers
whose activity contributes to ensuring the quality of all involved factors.”
Pedagogical Approaches in E-Learning
In the previous section, we described the evolution of e-learning and noted the
existence of different educational approaches over time. In this section, we focus on
the evolution of e-learning, taking into account the pedagogical approach.
Pedagogical approaches are derived from learning theories that provide general
principles for designing specific instructional and learning strategies. They are the
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mechanism to link theory with practice. Instructional strategies are what instructors
or instructional designers create to facilitate student learning. According to Dabbagh
(2005, p. 32), “there are three key components working collectively to foster
meaningful learning and interaction: (1) pedagogical models; (2) instructional and
learning strategies and, (3) pedagogical tools or online learning technologies (i.e.,
Internet and Web-based technologies). These three components form an iterative
relationship in which pedagogical models inform the design of e-learning by leading
to the specification of instructional and learning strategies that are subsequently
enabled or enacted through the use of learning technologies” (see Fig. 2). Due to the
fact that learning technologies have become ubiquitous and new technologies
continue to emerge bringing new affordances, pedagogical practices are continu-
ously evolving and changing. This does not mean that some designs and pedagogical
practices have disappeared. As we have mentioned, generations of e-learning coex-
ist. For example, some instructive models based on the transmission of knowledge
are still used but, sometimes, they incorporate new strategies such as gamification.
Conole (2014) divided pedagogies of e-learning into four categories:
1. Associative – a traditional form of education delivery. Emphasis is on the
transmission of theoretical units of information learning as an activity through
structured tasks, where the focus is on the individual, with learning through
association and reinforcement.
2. Cognitive/constructivist – knowledge is seen as more dynamic and expanding
rather than objective and static. The main tasks here are processing and under-
standing information, making sense of the surrounding world. Learning is often
task orientated.
Fig. 2 A theory-based design framework for e-learning (Source: Dabbagh (2005, p. 32))
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3. Situative – learning is viewed as social practice and learning through social
interaction in context. The learner has a clear responsibility for his/her own
learning. This approach is therefore “learner centered.”
4. Connectivist – learning through a networked environment. The connectivist
theory advocates a learning organization in which there is not a body of knowl-
edge to be transferred from educator to learner and where learning does not take
place in a single environment; instead, it is distributed across the Web and
people’s engagement with it constitutes learning.
Each of these theories has a number of approaches associated with it which
emphasize different types of learning (Fig. 3). For example, the associative category
includes behaviorism and didactic approaches, the cognitive/constructivist category
includes constructivism (building on prior knowledge) and constructionism (learn-
ing by doing), etc.
The development of the first e-learning platforms supported an instructional
design based on the associative/behaviorist approach. The design process follows
a sequential and linear structure driven by predetermined goals, and the learning
output is also predefined by the learning designer. The designers organize the content
and tasks and break them down from simple to complex. Information is then
delivered to the learner from the simplest to the most complex depending on the
learner’s knowledge.
Constructivist
Building on prior
knowledge
Task-orientated
Associative
Focus on individual
Learning through
association and
reinforcement
Situative
Learning through
social interaction
Learning in context
Connectivist
Learning in a
networked
environment
Reflective & dialogical
learning,
Personalised learning
Inquiry learning
Resource-based
E-training
Drill & practice
Experiential,
problem-based,
role play
Fig. 3 The pedagogies of e-learning. Source: teachertrainingmatters.com/blog-1/2015/12/19/learn
ing-theories-in-practice
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This type of approach has major limitations because it is not really suited to the
needs of the learner. The evolution of technology allows the development of
approaches that accommodate constructivist and connectivist perspectives that
engage learners and give them more control over the learning experience.
Choosing the pedagogical approach is obviously related to what we want to
achieve. However, it is important to establish a clear difference between designing
face to face or e-learning. Many of the studies into the effectiveness of e-learning
(Noesgaard & Ørngreen, 2015) have employed a comparative methodology. This
means that the effectiveness of e-learning is based on the comparison between
traditional face-to-face teaching and online learning. Along these lines, Noesgaard
and Ørngreen (2015, p 280) ask “should different modalities have the same measures
of performance, or should we consider e-learning to be a unique learning process and
thus use different definitions of effectiveness?” This question is important because
the effectiveness of e-learning can be analyzed in different ways. For instance, we
can design e-learning to improve learning retention, work performance, or social
collaboration. The measure to assess effectiveness will be different in each case.
However, what is clear is that there are still some research gaps regarding the impact
of e-learning on educational and training environments, as well as insufficient
studies on cost-effectiveness and long-term impact.
Research on e-learning design points out that one of the most significant require-
ments for further adoption of e-learning is the development of well-designed courses
with interactive and engaging content, structured collaboration between peers, and
flexible deadlines to allow students to pace their work (Siemens, 2014). Certainly,
every aspect of such a design can be interpreted in different ways. Nevertheless,
research shows that structured asynchronous online discussions are the most prom-
inent approach for supporting collaboration between students and to support learn-
ing. Darabi et al. (2013) consider that the greatest impact on student performance is
gained through “pedagogically rich strategies” that include instructor participation,
interaction with students, and facilitation of student collaboration as well as contin-
uous monitoring and moderating discussions. A promising approach to developing
self-regulatory skills using externally facilitated scaffolds is presented in Gašević,
Adescope, Joksimović, and Kovanović’s (2015) study. Their research shows that
meaningful student-student interaction could be organized without the instructor’s
direct involvement in discussions. There is a significant effect of instructional design
that provides students with qualitative guidelines on how to discuss, rather than
setting quantitative expectations only (e.g., number of messages posted) (Gašević
et al., 2015). The provision of formative and individualized feedback has also been
identified as an important challenge in e-learning (Noesgaard & Ørngreen, 2015).
In addition to support from the theories of learning, we can also find e-learning
models that provide specific support for designing effective learning experiences for
students participating in online courses. Bozkurt et al. (2015) provide a content
analysis of online learning journals from 2009 to 2013. In their study, they found that
the Community of Inquiry model has been particularly relevant to the successful
implementation of e-learning.
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In the Community of Inquiry model (Garrison, Anderson & Archer, 2003),
learning is seen as both an individual and a social process, and dialogue and debate
are considered essential for establishing and supporting e-learning. The Community
of Inquiry model defines a good e-learning environment through three major
components:
1. Cognitive presence: the learners’ ability to construct knowledge through com-
munication with their peers
2. Social presence: the learners’ ability to project their personal characteristics and
identities in an e-learning environment
3. Teaching presence: defined as the design, facilitation, and direction of cognitive
and social processes for the purpose of realizing personally meaningful and
educationally worthwhile learning outcomes
Teaching presence provides the necessary structures for a community’s forma-
tion, social presence fosters a community’s development by introducing students and
instructor to each other, and cognitive presence ensures the community’s continuing
usefulness to its participants.
After undertaking an extensive review of the literature on online interactions and
communities, Conole (2014) developed a new Community Indicators Framework
(CIF) for evaluating online interactions and communities. Four community indica-
tors appear to be common: participation, cohesion, identity, and creative capability.
Participation and patterns of participation relate to the fact that communities develop
through social and work activity over time. Different roles are evident, such as
leadership, facilitation, support, and passive involvement. Cohesion relates to the
way in which members of a community support each other through social interaction
and reciprocity. Identity relates to the group’s developing self-awareness and in
particular the notion of belonging and connection. Creative capability relates to
how far the community is motivated and able to engage in participatory activity.
The Community Indicators Framework (CIF) provides a structure to support the
design and evaluation of community building and facilitation in social and partici-
patory media. Research shows that structured asynchronous online discussions are
the most prominent approach for supporting collaboration between students and to
support learning.
The approaches described are based on a conception of the use of e-learning in
formal learning contexts. However, the broad penetration of e-learning prompts the
need to develop designs that allow formal and informal settings to be linked. In this
sense, we maintain that an ecological approach can be useful to support the systemic
perspective needed to integrate formal and informal processes.
Brown (2000) uses the term ecology as a metaphor to describe an environment
for learning. “An ecology is basically an open, complex adaptive system compris-
ing elements that are dynamic and interdependent. One of the things that makes an
ecology so powerful and adaptable to new contexts is its diversity.” Brown further
describes a learning ecology as “a collection of overlapping communities of
interest (virtual), cross-pollinating with each other, constantly evolving, and largely
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self-organizing.” The ecology concept requires the creation and delivery of a
learning environment that presents a diversity of learning options to the student.
This environment should ideally offer students opportunities to receive learning
through methods and models that best support their needs, interests, and personal
situations.
The instructional design and content elements that form a learning ecology need
to be dynamic and interdependent. The learning environment should enable instruc-
tional elements designed as small, highly relevant content objects to be dynamically
reorganized into a variety of pedagogical models. This dynamic reorganization of
content into different pedagogical models creates a learning system that adapts to
varying student needs.
Barron (2006) defines personal learning ecologies as “the set of contexts found in
physical or virtual spaces that provide opportunities for learning. Each context is
comprised of a unique configuration of activities, material resources, relationships
and the interactions that emerge from them” (Barron, 2006, p. 195).
From this perspective, learning and knowledge construction are located in the
connections and interactions between learners, teachers, and resources and seen as
emerging from critical dialogues and enquiries. Knowledge emerges from the
bottom-up connection of personal knowledge networks. Along these lines, Chatti,
Jarke, and Specht (2010, p. 78) refer to the learning as a network (LaaN) perspective.
“Each of us is at the centre of our very own personal knowledge network (PKN). A
PKN spans across institutional boundaries and enables us to connect beyond the
constraints of formal educational and organisational environments. Unlike commu-
nities, which have a start-nourish-die life cycle, PKNs develop over time.”
Knowledge ecologies lie at the heart of the LaaN perspective as a complex,
knowledge-intensive landscape that emerges from the bottom-up connection of
personal knowledge networks.
The value of the ecological perspective is that it provides a holistic view of
learning. In particular, it enables us to appreciate the ways in which learners engage
in different contexts and develop relationships and resources. The emphasis is on
self-organized and self-managed learning. The learner is viewed as the designer and
implementer of their own life experience.
The important question here is whether we are using the appropriate technology
in e-learning to support an ecological approach. In the next section, we analyze the
use of learning management systems (LMS) and propose new technological inno-
vations and solutions to improve e-learning.
Learning Ecosystems
There are very few technological innovations that reach a sufficient level of maturity
to be considered as consolidated technologies in the productive sector. It is also true
that some of these technologies arrive on the scene surrounded by a halo of
fascination that leads to the creation of different ad hoc practices, often resulting in
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unfulfilled expectations and eventually the complete disappearance of said
technology.
In e-learning, LMS are a paradigmatic case. They are a fully consolidated
educational technology, although the educational processes in which they are
involved could improve substantially. E-learning platforms are well established in
the higher education area and enjoy very significant adoption in other educational
levels and the corporate sector.
Although LMS are very complete and useful as course management tools, they
are too rigid in terms of communication flow, limiting participants’ interaction
capabilities too much. For this reason, teachers and students tend to complement
e-learning platforms with other tools, thereby creating personal learning networks
(Couros, 2010).
It would seem that LMS have lost their appeal as a trending or research topic due
to their known limitations, while different approaches and technologies are
appearing in the education sector to claim the apparently empty throne. Various
reports on educational technology trends underline topics such as MOOCs
(SCOPEO, 2013), gamification (Lee & Hammer, 2011), learning analytics
(Gómez-Aguilar et al. 2014), adaptive learning (Berlanga & García-Peñalvo,
2005), etc., but none of these proposed technologies, by themselves, have achieved
the disruptive effect that allows them to substantially improve or change teaching
and learning processes.
Consequently, LMS can no longer be regarded as the only component of tech-
nological/educational innovation and corporate knowledge management strategy
(García-Peñalvo & Alier, 2014). Nevertheless, these platforms should be a very
important component of a new learning ecosystem in conjunction with all the
existing and future technological tools and services that may be useful for educa-
tional purposes (Conde-González et al., 2014).
Technological ecosystems are the direct evolution of the traditional information
systems orientated toward supporting information and knowledge management in
heterogeneous contexts (García-Peñalvo et al., 2015).
Recently, there has been a fundamental change of approach in debates on
innovation in academic and political systems toward the use of ecologies and
ecosystems (Adkins, Foth, Summerville, & Higgs, 2007; Aubusson, 2002; Crouzier,
2015). The European Commission has adopted these two concepts as regional
innovation policy tools according to the Lisbon Declaration, considering that a
technological ecosystem has an open software component-based architecture that
is combined to allow the gradual evolution of the system through the contribution of
new ideas and components by the community (European Commission, 2006).
In fact, the technological ecosystem metaphor comes from the field of biology
and has been transferred to the social area to better capture the evolutionary nature of
people’s relationships, their innovation activities, and their contexts (Papaioannou,
Wield, & Chataway, 2009). It has also been applied in the services area as a more
generic conceptualization of economic and social actors that create value in complex
systems (Frow et al., 2014) and in the technological area, defining Software
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Ecosystems (SECO) (Yu & Deng, 2011) inspired by the ideas of business and
biological ecosystems (Iansiti & Levien, 2004).
These software ecosystems may refer to all businesses and their interrelations
with respect to a common product software or services market (Jansen, Finkelstein,
& Brinkkemper, 2009). Also, from a more architecture-orientated point of view, a
technological ecosystem may be studied as the structure or structures in terms of
elements, the properties of these elements, and the relationships between them, that
is, systems, system components, and actors (Manikas & Hansen, 2013).
Dhungana et al. (2010) state that a technological ecosystem may be compared to a
biological ecosystem from resource management and biodiversity perspectives, with
particular emphasis on the importance of diversity and social interaction support.
This relationship between natural and technological is also presented by other
authors who use the natural ecosystem concept to support their own definition of
technological ecosystems (Chang & West, 2006; Chen & Chang, 2007). Although
there are various definitions of natural or biological ecosystems, there are three
elements that are always present in all of them: the organisms, the physical environ-
ment in which they carry out their basic functions, and the set of relationships
between organisms and the environment. Thus, the technological ecosystem may
be defined as a set of software components that are related through information flows
in a physical medium that provides support for these flows (García-Holgado &
García-Peñalvo, 2013).
The ecosystem metaphor is suitable for describing the technological background
of educational processes because the ecosystem may recognize the complex network
of independent interrelationships among the components of its architecture. At the
same time, it offers an analytic framework for understanding specific patterns in the
evolution of its technological infrastructure, taking into account that its components
may adapt to the changes that the ecosystem undergoes and not collapse if they
cannot assume the new conditions (Pickett & Cadenasso, 2002). On the other hand,
the users of a technological ecosystem are also components of the ecosystem
because they are repositories and generators of new knowledge, influencing the
complexity of the ecosystem as artefacts (Metcalfe & Ramlogan, 2008).
From the learning technologies perspective, the past has been characterized by the
automation that spawned the development of e-learning platforms. The present is
dominated by integration and interoperability. The future challenge is to connect and
relate the different tools and services that will be available to manage knowledge and
learning processes. This requires defining and designing more internally complex
technological ecosystems, based on the semantic interoperability of their compo-
nents, in order to offer more functionality and simplicity to users in a transparent
way. Analyses of the behavior of technological innovations and advances in cogni-
tive and education sciences indicate that the (near) future use of information tech-
nology in learning and knowledge management will be characterized by
customization and adaptability (Llorens, 2014).
The learning ecosystem as a technological platform should be organized into a
container, the architectural framework of the ecosystem, and its functional compo-
nents (García-Holgado & García-Peñalvo, 2016).
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T
The framework should involve the integration, interoperability, and evolution of
the ecosystem components and a correct definition of the architecture that supports it
(Bo, Qinghua, Jie, Haifei, & Mu, 2009). The current status and technical and
technological evolution of technological ecosystems show very pronounced paral-
lelism with all the technology developing around the Internet and cloud services.
More specifically, the evolution in data collection, analysis procedures, and decision-
making drink from the same fountain as certain types of emerging technologies such
as the Internet of things, the processes that extract concepts from business intelli-
gence, or data mining processes applied to knowledge management.
Figure 4 presents the essential architecture of a learning ecosystem,
distinguishing the framework and a set of basic components for analytics, adaptive
knowledge management, gamification, and evidence-based portfolios.
The interconnection of platforms, tools, and services requires communication
protocols, interfaces, and data and resource description standards that enable data to
be entered and transmitted with minimal quality requirements that allow its meaning
and context to be preserved. Interconnection protocols and data collection rely on
platform interoperability, on the possibility of using sensors and other ways of
gathering evidence of learning, on open data with standard semantic content, and
even on descriptors and evidence linked to knowledge acquisition processes (Retalis,
Papasalouros, Psaromiligkos, Siscos, & Kargidis, 2006). The current state of devel-
opment of e-learning ecosystems and their extension to different learning method-
ologies and paradigms pinpoints the relevance of this research area for the process,
Fig. 4 Ecosystem architecture
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because data is the raw material (U.S. Department of Education - Office of Educa-
tional Technology, 2012) for designing the learning cycle (data-driven design),
assessing learning tasks and activities (learning analytics), and even as a means of
providing real-time feedback (data-driven feedback) and tailoring the learning
environment to the learner’s needs.
The most outstanding characteristic of these learning ecosystems is that they are a
technological approach but they are not an end in themselves. Instead, they serve the
pedagogical processes that teachers want to organize in the technological contexts
they provide, masking the internal difficulty of the technology itself.
Concluding Remarks
In the 1990s, student profiles in e-learning were similar to those of classic distance
education: most learners were adults with occupational, social, and family commit-
ments (Hanson et al., 1997). However, the current online learner profile is beginning
to include younger students. For this reason, the concept of the independent adult,
who is a self-motivated and goal-orientated learner, is now being challenged by
e-learning activities that emphasize social interaction and collaboration. Today’s
online learners are expected to be ready to share their work, interact within small
and large groups in virtual settings, and collaborate in online projects. According to
Dabbagh (2007, p. 224), “the emerging online learner can be described as someone
who has a strong academic self-concept; is competent in the use of online learning
technologies, particularly communication and collaborative technologies; under-
stands, values, and engages in social interaction and collaborative learning; pos-
sesses strong interpersonal and communication skills; and is self-directed.” Stöter,
Bullen, Zawacki-Richter, and von Prummer (2014) identify a similar list to Dabbagh
and also include learners’ personality traits and disposition for learning, their self-
directedness, the level of motivation, time (availability, flexibility, space) and the
level of interaction with their teachers, the learning tools they have at their disposal,
and the level of digital competency, among many other characteristics.
The research into learner characteristics identifies behaviors and practices that
may lead to successful online learning experiences for learners. However, it is
important to emphasize that due to today’s greater diversity of profiles, there are
many influences on students’ individual goals and success factors that are not easy to
identify. As Andrews and Tynan (2012) pointed out, part-time online learners are a
very heterogeneous group. Due to this diversity of e-learners, it is not appropriate to
privilege a particular pedagogical model, instead it is very important to design
learning environments that take learners’ needs and the context into account.
Providing formative, timely, and individualized feedback has also been identified
as an important challenge in the online learning environment. Likewise, more recent
studies have also highlighted the importance of timely, formative, effective, and
individualized feedback in order to efficiently support learning.
As Siemens (2014) argues, there is also a great opportunity for further research to
examine how (and whether) institutions are redesigning online courses based on the
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lessons learned from MOOCs. Moreover, another potential line of research might be
investigating how universities position online learning with respect to on-campus
learning. Finally, current research also shows that higher education has been primar-
ily focused on content design and curriculum development. However, in order to
develop personalization, adaptive learning is crucial.
References
Adkins, B. A., Foth, M., Summerville, J. A., & Higgs, P. L. (2007). Ecologies of innovation:
Symbolic aspects of cross-organizational linkages in the design sector in an Australian inner-
city area. American Behavioral Scientist, 50(7), 922–934. doi:10.1177/0002764206298317.
Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada, H. A., Muñoz-Organero, M.,
& Rodríguez-de-las-Heras, A. (2013). Analysing the impact of built-in and external social tools
in a MOOC on educational technologies. In D. Hernández-Leo, T. Ley, R. Klamma, & A. Harrer
(Eds.), Scaling up learning for sustained impact. 8th European conference, on technology
enhanced learning, EC-TEL 2013, Paphos, Cyprus, September 17–21, 2013. Proceedings
(Vol. 8095, pp. 5–18). Berlin Heidelberg: Springer.
Anderson, T. (2008). Toward a theory of online learning. In T. Anderson (Ed.), Theory and practice
of online learning (2nd ed., pp. 45–74). Edmonton, AB: AU Press, Athabasca University.
Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. The Interna-
tional Review of Research in Open and Distance Learning, 12(3), 80–97.
Aubusson, P. (2002). An ecology of science education. Int J Sci Educ, 24(1), 27–46. doi:10.1080/
09500690110066511.
Andrews, T., & Tynan, B. (2012). Distance learners: Connected, mobile and resourceful individ-
uals. Australasian Journal of Educational Technology, 28(4), 565–579.
Baker, J. W. (2000). The ‘Classroom Flip’: Using web course management tools to become the
guide by the side. In J. A. Chambers (Ed.), Selected papers from the 11th international
conference on college teaching and learning (pp. 9–17). Jacksonville, FL: Community College
at Jacksonville.
Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning
ecology perspective. Human development, 49(4), 193–224.
Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every
day. New York: Buck Institute for International Society for Technology in Education.
Berlanga, A. J., & García-Peñalvo, F. J. (2005). Learning technology specifications: Semantic
objects for adaptive learning environments. International Journal of Learning Technology, 1
(4), 458–472. doi:10.1504/IJLT.2005.007155.
Bo, D., Qinghua, Z., Jie, Y., Haifei, L., & Mu, Q. (2009). An E-learning ecosystem based on cloud
computing infrastructure. In Ninth IEEE International Conference on Advanced Learning
Technologies, 2009 (pp. 125–127). Riga: Latvia.
Borrás Gené, O., Martínez-Nuñez, M., & Fidalgo-Blanco, Á. (2016). New challenges for the
motivation and learning in engineering education using gamification in MOOC. International
Journal of Engineering Education, 32(1B), 501–512.
Bozkurt, A., Kumtepe, E. G., Kumtepe, A. T., Aydın, İ. E., Bozkaya, M., & Aydın, C. H. (2015).
Research trends in Turkish distance education: A content analysis of dissertations, 1986–2014.
European Journal of Open, Distance and E-learning, 18(2), 1–21.
Brown, J. S. (2000). Growing up: Digital: How the web changes work, education, and the ways
people learn. Change: The Magazine of Higher Learning, 32(2), 11–20.
Casany, M. J., Alier, M., Mayol, E., Conde, M. Á., & García-Peñalvo, F. J. (2013). Mobile learning
as an asset for development: Challenges and oportunities. In M. D. Lytras, D. Ruan,
R. Tennyson, P. Ordoñez de Pablos, F. J. García-Peñalvo, & L. Rusu (Eds.), Information
PR
E-
PR
INT
systems, E-learning, and knowledge management research. 4th World Summit on the Knowl-
edge Society, WSKS 2011, Mykonos, Greece, September 21–23, 2011. Revised Selected Papers
(Mykonos, Greece, 21–23 September 2011) (Vol. CCIS 278, pp. 244–250). Berlin/ Heidelberg:
Springer .
Chang, E., & West, M. (2006). Digital ecosystems a next generation of the collaborative environ-
ment. In G. Kotsis, D. Taniar, E. Pardede, & I. K. Ibrahim (Eds.), Proceedings of iiWAS'2006 -
The Eighth International Conference on Information Integration and Web-based Applications
Services, 4–6 December 2006, Yogyakarta, Indonesia (pp. 3–24): Austrian Computer Society.
Chatti, M. A., Jarke, M., & Specht, M. (2010). The 3P learning model. Educational Technology &
Society, 13(4), 74–85.
Chen, W., & Chang, E. (2007). Exploring a digital ecosystem conceptual model and its simulation
prototype. In Proceedings of IEEE international symposium on industrial electronics, 2007
(ISIE 2007) (pp. 2933–2938). Spain: University of Vigo.
Clark, D. (2013).MOOCs: Taxonomy of 8 types of MOOC. Retrieved from http://donaldclarkplanb.
blogspot.com.es/2013/04/moocs-taxonomy-of-8-types-of-mooc.html
Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines
for consumers and designers of multimedia learning (3rd ed.). San Francisco, USA: Pfeiffer.
Collis, B. (1996). Tele-learning in a digital world. The future of distance learning. London:
International Thomson Computer Press.
Conde, M. Á., García-Peñalvo, F. J., Rodríguez-Conde, M. J., Alier, M., Casany, M. J., &
Piguillem, J. (2014). An evolving learning management system for new educational environ-
ments using 2.0 tools. Interactive Learning Environments, 22(2), 188–204. doi:10.1080/
10494820.2012.745433.
Conde-González, M. Á., García-Peñalvo, F. J., Rodríguez-Conde, M. J., Alier, M., & García-
Holgado, A. (2014). Perceived openness of learning management Systems by students and
teachers in education and technology courses. Computers in Human Behavior, 31, 517–526.
doi:10.1016/j.chb.2013.05.023.
Conole, G. (2013). Digital identity and presence in the social milieu. Paper presented at the Pelicon
conference, 2013, 10–12th April, Plymouth.
Conole, G. (2014). Learning design: A practical approach. London: Routledge.
Couros, A. (2010). Developing personal learning networks for open and social learning. In
G. Veletsianos (Ed.), Emerging technologies in distance education (pp. 109–127). : Athabasca:
Canadá Athabasca University Press/Edmonton.
Crouzier, T. (2015). Science Ecosystem 2.0: How will change occur? Luxembourg: Publications
Office of the European Union.
Dabbagh, N. (2005). Pedagogical models for E-Learning: A theory-based design framework.
International Journal of Technology in Teaching and Learning, 1(1), 25–44.
Dabbagh, N. (2007). The online learner: Characteristics and pedagogical implications. Contempo-
rary Issues in Technology and Teacher Education, 7(3), 217–226.
Daniel, J., Vázquez Cano, E., & Gisbert, M. (2015). The future of MOOCs: Adaptive learning or
business model? RUSC. Universities and Knowledge Society Journal, 12(1), 64–73 doi:http://
dx.doi.org/10.7238/rusc.v12i1.2475.
Darabi, A., Liang, X., Suryavanshi, R., & Yurekli, H. (2013). Effectiveness of online discussion
strategies: A meta-analysis. American Journal of Distance Education, 27(4), 228–241.
Dhungana, D., Groher, I., Schludermann, E., & Biffl, S. (2010). Software ecosystems vs. natural
ecosystems: Learning from the ingenious mind of nature ECSA '10 Proceedings of the Fourth
European Conference on software architecture: Companion Volume (pp. 96–102). New York,
NY: ACM.
Downes, S. (2005). E-learning 2.0. eLearn Magazine (October).
Downes, S. (2012). E-Learning generations. Retrieved from http://halfanhour.blogspot.be/2012/
02/e-learning-generations.html
Downes, S. (2013). Week 2: The quality of massive open online courses. Retrieved from http://
mooc.efquel.org/week-2-the-quality-of-massive-open-online-courses-by-stephen-downes/
PR
E-
PR
INT
European Commission. (2006). A network of digital business ecosystems for Europe: Roots,
processes and perspectives. Brussels/Belgium: European Commission, DG Information Society
and Media Introductory Paper.
Fidalgo-Blanco, Á., García-Peñalvo, F. J., & Sein-Echaluce Lacleta, M. L. (2013). A methodology
proposal for developing adaptive cMOOC. In F. J. García-Peñalvo (Ed.), Proceedings of the
First International Conference on Technological Ecosystems for Enhancing Multiculturality
(TEEM’13) (pp. 553–558). New York: ACM.
Frow, P., McColl-Kennedy, J. R., Hilton, T., Davidson, A., Payne, A., & Brozovic, D. (2014). Value
propositions: A service ecosystems perspective. Marketing Theory, 14(3), 327–351.
doi:10.1177/1470593114534346.
Fulton, K. P. (2014). Time for learning: Top 10 reasons why flipping the classroom can change
education. Thousand Oaks, CA: Corwin Press.
García-Holgado, A., & García-Peñalvo, F. J. (2013). The evolution of the technological ecosystems:
An architectural proposal to enhancing learning processes. In F. J. García-Peñalvo (Ed.), Pro-
ceedings of the First International Conference on Technological Ecosystems for Enhancing
Multiculturality (TEEM’13) (Salamanca, Spain, November 14–15, 2013) (pp. 565–571).
New York: ACM.
García-Holgado, A., & García-Peñalvo, F. J. (2014). Knowledge management ecosystem based on
drupal platform for promoting the Collaboration between public administrations. In F. J. García-
Peñalvo (Ed.), Proceedings of the Second International Conference on Technological Ecosys-
tems for Enhancing Multiculturality (TEEM’14) (Salamanca, Spain, October 1–3, 2014)
(pp. 619–624). New York: ACM.
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:http://dx.doi.org/10.1016/j.scico.2016.03.010.
García-Peñalvo, F. J. (2005). Estado actual de los sistemas E-Learning. Education in the Knowledge
Society, 6(2).
García-Peñalvo, F. J. (Ed.) (2008). Advances in E-learning: Experiences and methodologies.
Hershey, PA, USA: Information Science Reference (formerly Idea Group Reference).
García-Peñalvo, F. J., & Alier, M. (2014). Learning management system: Evolving from silos to
structures. Interactive Learning Environments, 22(2), 143–145. doi:10.1080/
10494820.2014.884790.
García-Peñalvo, F. J., Fidalgo-Blanco, Á., Sein-EchaluceLacleta, M., & Conde-González, M. Á.
(2016). Cooperative micro flip teaching. In P. Zaphiris & I. Ioannou (Eds.), Proccedings of the
Learning and collaboration technologies. Third international conference, LCT 2016, held as part
of HCI international (Toronto, ON, Canada, July 17–22, 2016) (pp. 14–24). Cham, Switzerland:
Springer International Publishing.
García-Peñalvo, F. J., García de Figuerola, C., & Merlo-Vega, J. A. (2010). Open knowledge:
Challenges and facts. Online Information Review, 34(4), 520–539. doi:10.1108/
14684521011072963.
García-Peñalvo, F. J., Hernández-García, Á., Conde-González, M. Á., Fidalgo-Blanco, Á., Sein-
Echaluce Lacleta, M. L., Alier-Forment, M., ... Iglesias-Pradas, S. (2015). Learning services-
based technological ecosystems. 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. 467–472). New York: ACM.
García-Peñalvo, F. J., Johnson, M., Ribeiro Alves, G., Minovic, M., & Conde-González, M. Á.
(2014). Informal learning recognition through a cloud ecosystem. Future Generation Computer
Systems, 32, 282–294 doi:http://dx.doi.org/10.1016/j.future.2013.08.004.
García-Peñalvo, F. J., & Seoane-Pardo, A. M. (2015). Una revisión actualizada del concepto de
eLearning. Décimo Aniversario. Education in the Knowledge Society, 16(1), 119–144 doi:http://
dx.doi.org/10.14201/eks2015161119144.
Garrison, D. R., & Anderson, T. (2003). E-Learning in the 21st century: A framework for research
and practice. New York: RoutledgeFalmer.
PR
E
PR
INT
Garrison, D. R., Anderson, T., & Archer, W. (2003). A theory of critical inquiry in online distance
education. In M. G. Moore & W. G. Anderson (Eds.), Handbook of distance education
(pp. 113–127). Mahwah, NJ: Lawrence Erlbaum Associates.
Gašević, D., Adesope, O., Joksimović, S., & Kovanović, V. (2015). Externally-facilitated regulation
scaffolding and role assignment to develop cognitive presence in asynchronous online discus-
sions. The Internet and Higher Education, 24, 53–65.
Gómez-Aguilar, D. A., García-Peñalvo, F. J., & Therón, R. (2014). Analítica Visual en eLearning.
El Profesional de la Información, 23(3), 236–245.
Griffiths, D., & García-Peñalvo, F. J. (2016). Informal learning recognition and management.
Computers in Human Behavior, 55A, 501–503. doi:10.1016/j.chb.2015.10.019.
Gros, B., Lara, P., García, I., Mas, X., López, J., Maniega, D., & Martínez, T. (2009). El modelo
educativo de la UOC. Evolución y perspectivas (2nd ed.). Barcelona, Spain: Universitat Oberta
de Catalunya.
Hanson, D., Maushak, N. J., Schlosser, C. A., Anderson, M. L., Sorensen, C., & Simonson,
M. (1997). Distance education: Review of the literature (2nd ed.). Bloomington, IN: Associa-
tion for Educational Communications and Technology.
Hirsch, B., & Ng, J. W. P. (2011). Education beyond the cloud: Anytime-anywhere learning in a
smart campus environment. In Proceedings of 2011 International Conference for Internet
Technology and Secured Transactions (ICITST) (pp. 718–723). Abu Dhabi, United Arab
Emirates: Conference on IEEE.
Iansiti, M., & Levien, R. (2004). Strategy as ecology. Harvard Business Review, 82(3), 68–78.
Jansen, S., Finkelstein, A., & Brinkkemper, S. (2009). A sense of community: A research agenda
for software ecosystems. In 31st International Conference on Software Engineering - Compan-
ion Volume (pp. 187–190). Vancouver/Canada: ICSE-Companion 2009.
Lage, M. J., Platt, G. J., & Treglia, M. (2000). Inverting the classroom: A gateway to creating an
inclusive learning environment. The Journal of Economic Education, 31(1), 30–43.
Lane, L. (2012). Three Kinds of MOOCs. Retrieved from http://lisahistory.net/wordpress/2012/08/
three-kinds-of-moocs/.
Lee, J. J., & Hammer, J. (2011). Gamification in education: What, how, why bother?. Academic
Exchange Quarterly, 15(2), 146.
Lerís López, D., Vea Muniesa, F., & Velamazán Gimeno, Á. (2015). Aprendizaje adaptativo en
Moodle: Tres casos prácticos. Education in the Knowledge Society, 16(4), 138–157 doi: http://
dx.doi.org/10.14201/eks201516138157.
Llorens, F. (2014). Campus virtuales: De gestores de contenidos a gestores de metodologías. RED,
Revista de Educación a distancia, 42, 1–12.
Manikas, K., & Hansen, K. M. (2013). Software ecosystems – A systematic literature review.
Journal of Systems and Software, 86(5), 1294–1306 doi:http://dx.doi.org/10.1016/j.
jss.2012.12.026.
Mayer, R. E. (2003). Elements of a science of e-learning. Journal of Educational Computing, 29(3),
297–313. doi:10.2190/YJLG-09F9-XKAX-753D.
McGreal, R., Kinuthia, W., & Marshall, S. (Eds.). (2013). Open educational resources: Innovation,
research and practice. Vancouver: Commonwealth of Learning and Athabasca University.
Metcalfe, S., & Ramlogan, R. (2008). Innovation systems and the competitive process in develop-
ing economies. The Quarterly Review of Economics and Finance, 48(2), 433–446. doi:10.1016/
j.qref.2006.12.021.
Minović, M., García-Peñalvo, F. J., & Kearney, N. A. (2016). Gamification in engineering educa-
tion. International Journal of Engineering Education (IJEE), 32(1B), 308–309.
Morales, E. M., García-Peñalvo, F. J., & Barrón, Á. (2007). Improving LO quality through
instructional design based on an ontological model and metadata. Journal of Universal Com-
puter Science, 13(7), 970–979. doi:10.3217/jucs-013-07-0970.
Noesgaard, S. S., & Ørngreen, R. (2015). The effectiveness of e-learning: An explorative and
integrative review of the definitions, methodologies and factors that promote e-learning effec-
tiveness. Electronic Journal of e-Learning, 13(4), 278–290.
PR
E-
PR
INT
Observatory of Educational Innovation of the Tecnológico de Monterrey. (2014). Flipped learning.
Retrieved from Monterrey, México: http://observatorio.itesm.mx/edutrendsaprendizajeinve
rtido.
Papaioannou, T., Wield, D., & Chataway, J. (2009). Knowledge ecologies and ecosystems? An
empirically grounded reflection on recent developments in innovation systems theory. Environ-
ment and Planning C: Government and Policy, 27(2), 319–339. doi:10.1068/c0832.
Pickett, S. T. A., & Cadenasso, M. L. (2002). The Ecosystem as a multidimensional concept:
Meaning, model, and metaphor. Ecosystems, 5(1), 1–10. doi:10.1007/s10021-001-0051-y.
Ramírez Montoya, M. S. (2015). Acceso abierto y su repercusión en la Sociedad del Conocimiento:
Reflexiones de casos prácticos en Latinoamérica. Education in the Knowledge Society (EKS), 16
(1), 103–118 doi:http://dx.doi.org/10.14201/eks2015161103118.
Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006).
Towardsnetworked learning analytics—A concept and a tool. In Proceedings of the fifth
international conference on networked learning (pp. 1–8). UK: Lancaster.
Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age.
New York: McGraw-Hill.
Sanchez i Peris, F. J. (2015). Gamificación. Education in the Knowledge Society, 16(2), 13–15.
Sánchez Prieto, J. C., Olmos Migueláñez, S., & García-Peñalvo, F. J. (2014). Understanding mobile
learning: Devices, pedagogical implications and research lines. Education in the Knowledge
Society, 15(1), 20–42.
SCOPEO. (2013). MOOC: Estado de la situación actual, posibilidades, retos y futuro. Retrieved
from Salamanca, Spain: http://scopeo.usal.es/wp-content/uploads/2013/06/scopeoi002.pdf.
Sein-Echaluce Lacleta, M. L., Fidalgo-Blanco, Á., García-Peñalvo, F. J., & Conde-González, M. Á.
(2016). iMOOC Platform: Adaptive MOOCs. In P. Zaphiris & I. Ioannou (Eds.), Proceedings of
the learning and collaboration technologies. Third international conference, LCT 2016, held as
part of HCI international 2016 (Toronto, ON, Canada, July 17–22, 2016) (pp. 380–390). Cham,
Toronto, Canada: Springer International Publishing.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of
Instructional Technology and Distance Learning, 2(1), 3–10.
Siemens, G. (2014). Digital Learning Research Network (dLRN). Retrieved from http://www.
elearnspace.org/blog/2014/11/18/digital-learning-research-network-dlrn/.
Sonwalkar, N. (2013). The First Adaptive MOOC: A case study on pedagogy framework and
scalable cloud architecture—Part I. MOOCs Forum, 1(P), 22–29. doi:10.1089/
mooc.2013.0007.
Stöter, J., Bullen, M., Zawacki-Richter, O., & von Prümmer, C. (2014). From the back door into the
mainstream: The characteristics of lifelong learners. In O. Zawacki-Richter & T. Anderson
(Eds.), Online distance education: Towards a research agenda. Athabasca, Canada: Athabasca
University Press.
Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud
computing. Journal of Network and Computer Applications, 34(1), 1–11.
Tan, E., & Loughlin, E. (2014). Using ‘Formally’ informal blogs to reate learning communities for
students on a teaching and learning programme: Peer mentoring and reflective spaces. In F. J.
García-Peñalvo & A. M. Seoane-Pardo (Eds.),Online tutor 2.0: Methodologies and case studies
for successful learning (pp. 163–175). Hershey: IGI Global.
U.S. Department of Education - Office of Educational Technology. (2012). Enhancing teaching and
learning through educational data mining and learning analytics: An issue brief. Retrieved
from Washington, D.C.: https://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf.
Wenger, E. C. (1998). Communities of practice: Learning, meaning, and identity. New York:
Cambridge University Press.
Wiley, D. A. (2002). Connecting learning objects to instructional design theory: A definition, a
metaphor, and a taxonomy. In D. A. Wiley (Ed.), The instructional use of learning objects.
Bloomington, Indiana: Agency for Instructional Technology.
PR
E-
PR
NT
Wilson, S., Liber, O., Johnson, M., Beauvoir, P., Sharples, P., & Milligan, C. (2007). Personal
learning environments: Challenging the dominant design of educational systems. Journal of
e-Learning and Knowledge Society, 3(3), 27–38.
Yoshida, H. (2016). Perceived Usefulness of “Flipped Learning” on instructional design for
elementary and secondary education: With focus on pre-service teacher education. International
Journal of Information and Education Technology, 6(6), 430–434. doi:10.7763/IJIET.2016.
V6.727.
Yu, E., & Deng, S. (2011). Understanding software ecosystems: A strategic modeling approach. In
S. Jansen, J. Bosch, P. Campbell, & F. Ahmed (Eds.), IWSECO-2011 Software Ecosystems 2011.
Proceedings of the Third International Workshop on software ecosystems. Brussels, Belgium,
June 7th, 2011 (pp. 65–76). Aachen, Germany: CEURWorkshop Proceedings.
PR
E-
PR
INT