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The low completion rate for Massive Open Online Courses (MOOCs), averaging 10 % across total enrolment, highlights a need for close analysis of the underlying formative model. The methodology used here involves cooperation among MOOC participants to introduce new resources through social networks and the integration of these resources with previous teacher materials. The paper describes two MOOCs on distinct topics using this methodology and implemented on the same platform. The observed outcomes indicate increased completion rates for both courses as compared with other MOOCs developed on the same platform. Additionally, although participants in the two MOOCs differed in profile and personal goals, they reported similar perceptions of the quality of the learning experience, which was influenced by the knowledge management approach developed in the proposed methodology.
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R E S E A R C H A R T I C L E Open Access
From massive access to cooperation:
lessons learned and proven results of
a hybrid xMOOC/cMOOC pedagogical
approach to MOOCs
Ángel Fidalgo-Blanco
1*
, María Luisa Sein-Echaluce
2
and Francisco José García-Peñalvo
3
* Correspondence:
angel.fidalgo@upm.es
1
Department of Geological and
Mining Engineering, School of
Mining and Energy Engineering,
Technical University of Madrid, Ríos
Rosas, 21, 28003 Madrid, Spain
Full list of author information is
available at the end of the article
Abstract
The low completion rate for Massive Open Online Courses (MOOCs), averaging 10 %
across total enrolment, highlights a need for close analysis of the underlying
formative model. The methodology used here involves cooperation among MOOC
participants to introduce new resources through social networks and the integration
of these resources with previous teacher materials. The paper describes two MOOCs
on distinct topics using this methodology and implemented on the same platform.
The observed outcomes indicate increased completion rates for both courses as
compared with other MOOCs developed on the same platform. Additionally,
although participants in the two MOOCs differed in profile and personal goals,
they reported similar perceptions of the quality of the learning experience,
which was influenced by the knowledge management approach developed in
the proposed methodology.
Keywords: MOOC, Collaborative learning, Learning communities, Online education,
Informal learning, Learning environments, Educational strategies, Case studies,
Social networks
Introduction
In 1999, online technologies enabled one of the most important disruptive innovations
in education (García-Peñalvo & Seoane-Pardo, 2015), allowing many people to access
learning opportunities that would not otherwise have been possible (Weise & Christensen,
2014). The recent emergence of Massive Open Online Courses (MOOCs) represents a
major step forward for education. Hundreds of thousands of users access these online
learning platforms, with thousands of enrolees in each MOOC and academic offerings from
some of the worlds most prestigious universities (Sharples et al. 2013).
There is a widespread view that MOOCs are a disruptive innovation with the potential
to revolutionize and transform training (DiSalvio, 2012; Harden, 2012; Mazoue, 2013).
The social success of MOOCs has emerged alongside open training (open source
software and open resources) (Atenas, 2015; Fidalgo-Blanco, Sein-Echaluce, Borrás Ge
& García- Peñalvo, 2014; García-Peñalvo, García de Figuerola, & Merlo-Vega, 2010), the
growth of social networks and the drive for universal education (Downes, 2012; Yuan &
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indicate if changes were made.
Fidalgo-Blanco et al. International Journal of Educational Technology
in Higher Education (2016) 13:24
DOI 10.1186/s41239-016-0024-z
Powell, 2013). These emerging ideas of change promise mid-term consequences such as
new economic models for universities, new models of academic-social accreditation, im-
provement in the quality of university branding and a tendency towards democratization
and improved training for all (Daniel, Vázquez Cano, & Gisbert, 2015).
However, another strand of thought, mainly academic, questions the validity of the
MOOC model as transformative for training and learning. This view is based on evidence
of low MOOC completion rates, difficulties in verifying the identity of participants, low
validity of accreditations, low quality of educational resources, among other issues
(Bartolomé-Pina & Steffens, 2015; Zapata-Ros, 2013) and essentially highlights the
absence of any clear pedagogical model in this type of training (Aceto, Borotis,
Devine, & Fischer, 2014; Guàrdia, Maina, & Sangrà, 2013). In this regard, proposals
have been developed for indicators of the pedagogical quality of MOOCs. These
specify dimensions that include pedagogical approach, tutorial activity, evaluation,
user experience, motivation and resources (Alemán, Sancho-Vinuesa, & Gómez
Zermeño, 2015), planning and management, learning design and communication/
interaction (Guerrero, 2015).
Transformer of training or education bubble, new learning or marketing model
(Cabero, 2015; Salzberg, 2015)whatever ones view, MOOCs feature prominently in
conferences and scientific journals (Chiappe Laverde, Hine, & Martínez Silva, 2015;
Jacoby, 2014; Martínez Abad, Rodríguez Conde, & García-Peñalvo, 2014), with huge
interest in acquiring reliable data to better understand the MOOC phenomenon and its
possible impact on learning strategy.
As noted above, one of the most negative aspects of MOOCs is the low completion rate;
according to various studies, this varies between 5 and 15 % (Belanger & Thornton, 2013;
Jordan, 2013). Although there are other definitions of completion(Jordan, 2013), for
present purposes, the term is taken to mean completion of specified activities that enable
participants to obtain a certificate. This cannot be interpreted as a direct indicator of
MOOC quality, but it is not the main reason for criticism of underlying model.
The failure is often attributed to MOOC methodology, to the theme, to the hetero-
geneity of participants, to massification or to the curiosity aroused in people who
have no real intention of taking the course (Aguaded Gómez, 2013). The most
characteristic features of MOOCsmassification, heterogeneity and the absence of
atutor,differentirelyfromonlineacademic training, and these extreme training
characteristics present greater difficulties for the design of MOOCs than for other
online courses (Fidalgo-Blanco, García-Peñalvo & Sein-Echaluce, 2013).
The two main types of MOOCs are xMOOCs and cMOOCs. While xMOOCs are
instructivist and individualist, use classic e-learning platforms and are based on resources,
cMOOCs are connectivist and are based on social learning, cooperation and use of web
2.0 (Castaño Garrido, Maiz, & Garay Ruiz 2015; Downes, 2012, Fidalgo-Blanco, Sein-
Echaluce & García-Peñalvo, 2015b). Technologies for xMOOCs (X platforms) offer classic
learning (e.g. Coursera, MiriadaX) and focus on improving technologies rather than peda-
gogical models (Zapata-Ros, 2013).
Technologies based on social software, such as social networks (C platforms), enable
new ways of learning. In that sense, Adell and Castañeda (2010) suggested that social
networks have directed our attention to informal learning, which occurs outside the
institution or classroom.
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 2 of 13
Table 1 summarises the main features of formal, informal and non-formal learning in
relation to the environment in which it occurs, the existence or otherwise of learning
planification and training structure (objectives, duration, or educational resources), the
intentionality of the learner and the courses accreditation (CEDEFOP 2014; Muñoz, 2016).
Given the blurred boundary between these types of learning when it comes to virtual
learning (Adell & Castañeda, 2010; García-Peñalvo & Griffiths, 2014; Griffiths & García-
Peñalvo, 2016), the present research examines MOOCs as non-formal training that
enhances informal learning through social interactions in practical communities and
social networks. Llorens and Capdeferro (2011) showed that social networks promote
informal learning, in turn enabling knowledge construction and skills development. This
also offers individuals a user-managed approach to open and cooperative learning. Beyond
the interaction between students (Gros Salvat 2007), the cooperative model has been
shown to be superior to other educational approaches based on competitiveness. This is
especially the case in respect of academic performance, higher order thinking, knowledge
generation and transfer of ideas to different contexts (Barkley, Major, & Cross 2014;
Bauerova & Sein-Echaluce, 2007).
With due regard to all the above concerns, the objective of this paper is to
propose a new pedagogical model for MOOCs, supported by empirical investiga-
tion of questions related to dropout rate, including the following. What MOOC
factors exert greater influence on dropout rate: participant profile or the
underlying model? Are current models valid or should more specific models be
generated? Does cooperation affect completion rates? Can MOOCs be made
sustainable over time?
The proposed hybrid pedagogical model incorporates cooperation to create know-
ledge sharing among participants and combines characteristics of xMOOCs and
cMOOCs. An analysis is presented of the models impact on perceptions of learning
and cooperation in two real cases. The following section describes the research method
and the proposed pedagogical model.
Method
Hybrid pedagogical model: xMOOC/cMOOC
The proposed model is based on the use of an X platform (for e-learning) and a C
platform (e.g. a social network), combining formal and non-formal learning activities
(in the X platform) with informal learning (in the C platform) and cooperation among
participants to generate a continuous flow of knowledge between platforms.
Cooperation is a pedagogical resource that directly involves participants and re-
duces MOOC dropout rate, focusing on three dimensions described by Suárez
Guerrero (2010):
Table 1 Characteristics of formal, informal and non-formal learning
Learning Environment Planning Training structure Intentional Academic certificate
Formal Classroom in regulated institution YES YES YES YES
Informal Out of classroom (work,
family, leisure, etc.)
NO NO NO NO
Non-formal Classroom in non-regulated
institution
YES YES/NO YES NO
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 3 of 13
1. Measured achievement: here, the rate of participants who successfully complete the
course and fulfil their objectives;
2. Social integration: promotion of relationships through participation in the social
network (the social component of the MOOC);
3. Personal development: here, the achievement of learning objectives by combining
course content with cooperative interaction, as structured and defined by faculty.
The flow of knowledge resources generated in the model is based on the following
steps (see Fig. 1).
1. The teaching team adds learning resources to the X platform (e.g. e-learning
platform) or to the C platform (e.g. social network). These resources can also be
provided by professionals in the sector.
2. MOOC participants generate new resources and add these to the C platform, both
through activities planned by the teaching team on the X platform and during
social network use. The teaching team may choose to incorporate these to enrich
the available resources on the X platform before commencing the MOOC, which
can be simultaneously added to C platforms.
In this way, cooperation creates a continuous flow of knowledge between the X and
C platforms. The more varied the resources generated, the more effective they become,
enhanced by the massification and heterogeneity of MOOC participants Two case
studies using this model are described below.
Cases
The present study is based on case studies of two MOOCs implemented on the
MiriadaX platform (MiríadaX 2015), providing data for comparison of completion rates
against the average for MiriadaX MOOCs. These data were obtained from MiriadaX,
based on its own criteria, and from a survey of participants to assess their satisfaction
with the learning experience.
Case 1. MOOC Free Software and Open Knowledge (FS&OK). Objectives: Training in
the concepts and components of free software and open knowledge; participation in
the free software movement (training to create open knowledge in blogs and wikis);
provision of criteria and recommendations for application of course themes in
Fig. 1 Flow of resource creation for the proposed hybrid model
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 4 of 13
different contexts. Duration: 6 weeks (12 March23 April 2013). Composition: Five
modules, the first of which is the presentation. Platform: MiriadaX.
Case 2. MOOC Applied Educational Innovation (AEI). Objectives: Identification and
relation of the components of educational innovation; learning about the latest
methods and techniques for educational innovation in daily teaching. Duration:Six
weeks (6 March10 April 2014). Composition: Six modules, the first of which is the
presentation. Platform: MiriadaX.
With regard to technology, MiriadaX was used as the X platform in both cases;
the C platform differed in each case.
Case 1. Four social networks (Linkedin, Elgg, Identi.ca and Twitter) and a wiki were
used to organize and integrate results from the learning community with course
educational resources.
Case 2. Using the social network Google+, the following resources were integrated
and organized: results from the learning community, some teaching resources from
the course and a blog to provide an element of reflection.
The applied learning strategy was identical in both cases: integration of non-formal
learning activities (i.e. not regulated courses) in MiriadaX with informal learning
activities in the social network, generating a flow of knowledge among participants,
faculty and professionals from the sector. In this sense, each module involved a linked
spiral of between 2 and 6 groups of non-formal and informal activities.
For the cooperative strategy, resources generated by participants, faculty and
industry professionals were integrated in both cases, but there was significant
variation between them.
Case 1. The spiral for each module was continuously created as the course
proceeded. The most meaningful content generated in the social network (case
studies, discussions, tools) was incorporated into MiriadaX to complement the
initial resources (videos, presentations, etc.).
Case 2. Once the course began, the teaching team not could edit it (a new
MiriadaX policy). This affected the flow by including in MiriadaX those resources
generated within the social network. Similarly, the wiki was not used to organize
content, as the social network Google + enabled better organization of content
provided by participants.
Results
Participantsdata
The data in Table 2 were obtained from the MiriadaX platform and a survey ad-
ministered to participants in each MOOC. These data reflect the heterogeneous
profiles within each course in terms of participantsage, origin, profession, learning
preferences and academic level.
In both cases, participation by country is similar, but there are very significant differ-
ences between cases on the remaining input variables. One possible explanation for this
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 5 of 13
effect is that while FS&OK has a technological theme (free software), AEIs focus is
social (educational innovation). This may explain why FS&OK attracted more male
participants (72.04 %) than AEI (58.03 %). In relation to profession, 50.40 % of
participants in AEI were teachers, as against 11.18 % for FS&OK. With regard to
qualifications, 13.94 % of FS&OK participants were postgraduates, as against 31.15 %
for AEI. As to educational interests, 53.92 % sought to apply AEI in any context while
63.64 % of FS&OK participants (even those marking several options) hoped to apply
what they learned to their work, 42.21 to their studies and 25.06 % to their organisation.
Table 2 Entry data of participants in MOOCs FS&OK and AEI
Case 1. FS&OK Case 2. AEI
Number of enrolees 3,754 Number of enrolees 6,149
Completed surveys 1,708 Completed surveys 3,236
Gender Gender
Male 72.04 % Male 58.03 %
Female 27.96 % Female 41.97 %
Country. Top 7 Country. Top 7
Spain 60.29 % Spain 55.62 %
Colombia 8.56 % Mexico 10.35 %
Mexico 7.04 % Colombia 7.69 %
Peru 6.62 % Peru 5.62 %
Argentina 3.16 % Argentina 3.71 %
Venezuela 2.73 % Venezuela 2.69 %
Bolivia 1.76 % Brasil 2.44 %
Job Job
Teacher 11.18 % Teacher 50.40 %
Student 21.20 % Student 21.82 %
No activity 22.05 % Non-teacher 27.78 %
Various 45.57 %
Learning interest in FS&OK (multiple options) Learning interest in AEI (single option)
To gain basic knowledge
To apply to studies
To apply in a job context
To apply in an organization
To publish in open access
36.71 %
42.21 %
63.64 %
25.06 %
18.68 %
To gain basic knowledge
To apply in other contexts
To gain a new perspective
To obtain course materials
16.01 %
53.92 %
24.07 %
6%
Qualification Qualification
Primary 1.47 % Primary 0.34 %
Secondary 25.06 % Secondary 5.62 %
University 28 % University 52.75 %
Post-university 13.94 % Post-university 31.15 %
Vocational 31.54 % Vocational 10.14 %
Age Age
< 20 4.21 % <20 0.88 %
2029 39.88 % 2029 30.47 %
3039 30.61 % 3039 31.12 %
4049 18.54 % 4049 24.23 %
5059 5.91 % 5059 11.30 %
> 60 0.85 % >60 2 %
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 6 of 13
There was a marked difference in the number of enrolees, with 3,754 in FS&OK and
6,149 in AEI. Curiously, while the primary AEI stakeholders would be teachers, 21.82 of
participants were students and 27.78 % were non-teachers.
Two information sources were used to compare the two cases: results from the
platform itself (completion rates and dropout trend) and results of a satisfaction survey
in both cases.
Completion rates and dropout trend
Using MiríadaX statistics, Table 3 shows completion rates for total enrolment, the
number of participants who entered the course at least once and the number who
started at least the first training module. Similarly, Table 3 includes the average rate of
completion, for total enrolment in MiriadaX international MOOCs.
The main indicator generally used as a standard measure of a MOOCs success is the
completion rate for enrolees on the X platform. The global rate ranges from 5 % to
15 % (Jordan, 2013). The rate for MiriadaX MOOCs is in the upper part, with 13.47 in
April 2013 (for 58 courses) and 13.95 % in February 2014 (for 121 courses). For both
cases presented here, completion rates are very similar at 27.8 and 28.2 % (see Table 3)
roughly double the average rate of completion for other MiriadaX MOOCs.
The most significant difference between the two cases relates to participation in
social networks in terms of total enrolment in the course (28.3 for FS&OK and 32.2 %
for AEI). The FS&OK course allowed participants to use several C platforms, which
caused some dispersion. In contrast, AEI offered only one C platform, leading to
increased involvement.
Figures 2 and 3 show dropout patterns, reflecting participation of enrolees in both
MOOCs at the beginning of each module. In both cases, there is dropout relative to
number of modules and elapsed time. Figures 2 and 3 show that the most significant
decline appears in the presentation + module 1. After module 1, the dropout rate
decreases considerably, although it remains progressive throughout.
Participant satisfaction
At the end of each case, a satisfaction survey was sent to all students enrolled in either
MOOC. This was an adaptation of the Student Evaluation of Educational Quality
(SEEQ) questionnaire, designed and validated by Marsh and Roche (1997). Tables 4, 5
and 6 show the results for questions measuring participation level, learning perception
and cooperation level, respectively, for C platforms included in each course.
Table 3 Completion and participation rates for FS&OK and AEI MOOCs
Case 1 FS&OK Case 2 AEI
Enrolled in the course 3,754 6,149
Percentage completion among enrolees on X platform 27.8 % 28.2 %
Percentage completion among all who watched the presentation
on X platform
45.7 % 46.4 %
Percentage completion among all who started training modules
on X platform
54.4 % 53.7 %
Percentage participation on C platforms among enrolees on X platfom
(C platforms data)
28.3 % 32.8 %
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 7 of 13
Percentage survey completion is similar in both cases (17.20 and 17.08 %). Likewise,
the results for participation level in each MOOC (from no participationto very
regularly) are also almost identical (see Table 4).
Table 5 includes answers to questions about participantsperceptions in terms
of their learning and interests (5-point Likert scale, from 1 = strongly disagree to
5=strongly agree). For example:
Q1. I have learned and understood the contents of the course;
Fig. 2 Dropout pattern during implementation of the MOOC Free Software and Open Knowledge (case 1)
Fig. 3 Dropout pattern during the implementation of the MOOC Applied Educational Innovation (case 2)
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 8 of 13
Q2. I have learned things that I consider valuable;
Q3. My interest in the topics covered has increased with the course.
Table 6 includes answers to questions about cooperation grade in the social networks
used for course activities for each MOOC (5-point Likert scale, from 1 = strongly
disagree to 5 = strongly agree):
Q36/Q44. Q36 (FS&OK) I have cooperated with other participants in the proposed
activities. Q44 (AEI) I have participated in the suggested social networks.
Q38 (FS&OK)/Q51 (AEI). Sharing resources and interacting through social networks
improve learning.
Q39 (FS&OK)/Q52 (AEI). Sharing resources and interacting through social networks
improve initial course resources.
The results are again almost identical, with percentage differences of less than 1 % in
all cases. The results for similar questions Q36/Q44 indicate that participation was
similar in both cases. Regarding Q38, 85 % of FS&OK participants and 53 % of AEI
participants believed that their learning had improved somewhat or a lot (Likert values
4 or 5). For Q39, 78 % of participants in FS&OK thought that cooperation had
influenced their learning a lot or enough (5 or 4), as against only 54 % in AEI. The
better results for FS&OK reflect the availability of significant resources, created coopera-
tively in social networks, for inclusion in MiriadaX, which was not the case for AEI.
Final resources
Finally, this methodology generates two products: a learning community and a space
where the generated resources are organized.
Table 4 Participation on C platforms
Participation data Case 1 (FS&OK) Case 2 (AEI)
Number of enrolees 3,754 6,149
Completed surveys/(percentage of enrolees) 641/17.20 % 1,050/17.08 %
Participation level in cooperative activities (based on completed surveys)
No participation 3 % 3 %
Low participation 10 % 10 %
Regular participation 27 % 27 %
Rather regular participation 33 % 32 %
Very regular participation 27 % 28 %
Table 5 Perception of learning
Likert Q1 (FS&OK) Q1 (AEI) Q2 (FS&OK) Q2 (AEI) Q3 (FS&OK) Q3 (AEI)
1 0.30 % 0.10 % 0.15 % 0.29 % 0.31 % 0.48 %
2 0.77 % 0.67 % 0.92 % 1.43 % 1.39 % 2.10 %
3 6.03 % 7.24 % 7.59 % 7.05 % 10.53 % 9.62 %
4 39.93 % 39.71 % 32.82 % 33.33 % 32.82 % 33.43 %
5 52.94 % 52.00 % 58.51 % 57.90 % 54.95 % 54.38 %
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 9 of 13
Case 1 (FS&OK). Learning community from Linkedin and Twitter; Wiki acts as the
storage space for resources generated during the course.
Case 2 (AEI). Learning community and organization of resources in Google+; this
social network allows combination of tags with categories to index all resources
created before and during the course.
In both cases, social networks continued to grow independently. In the case of
FS&OK, from April 2013 (when the course ended) to November 2015, LinkedIn
has grown from 698 to 1100 participants and Twitter from 200 to 456. Two
editions of AEI have been implemented. In April 2014 (when the course ended),
the learning community had 2,107 participants, increasing to 10,889 in November
2015. About 3,700 participants came from the two editions of AEI, and about
7,100 have been included in the learning community in various ways. The learning
community and generated resources have proved useful and efficient for use both
during the MOOC and afterwards.
Discussion
With respect to the research questions, these results suggest that MOOC comple-
tion rate relates more to methodology than to the platform, theme or profile of
enrolled participants. In both study cases, the proposed hybrid methodology
produced very similar results (i.e. participation and completion rates) and doubled
the completion rate for MiriadaX MOOCs (Table 3). This effect was independent
of the input variables (i.e. heterogeneous profiles, Table 1) and supports other
claims about the influence of course design and social relations on completion rate
(Sánchez-Vera, León-Urrutia, & Davis, 2015). To that extent, it justifies the gener-
ation of models adapted to particular features of MOOCs and addressing their
shortcomings (Zapata-Ros, 2013).
These two distinct study cases show that the highest dropout rate occurs after
the first module and then stabilizes to the end of the course (regardless of the
number of modules). This suggests that the number of dropouts decreases as
cooperation level increases. Furthermore, collaboration is not confined to shared
resourcesin fact, the creation of knowledge sharing underlies the collaborative
strategy (Fidalgo-Blanco, Sein-Echaluce, & García-Peñalvo 2015a). This is per-
formed through the interaction and integration of learning resources between X
and C platforms, significantly influencing cooperation and completion rates (Suárez
Guerrero 2010).
The proposed hybrid model can be said to have generated sustainable resources
during MOOC implementation and subsequently through social networks. Llorens and
Table 6 Cooperation results
Likert Q36 (FS&OK) Q44 (AEI) Q38 (FS&OK) Q51 (AEI) Q39 (FS&OK) Q52 (AEI)
1 14.09 % 14.29 % 1.55 % 9.10 % 1.08 % 8.42 %
2 15.48 % 16.57 % 1.55 % 13.00 % 1.70 % 12.02 %
3 23.84 % 25.90 % 11.76 % 24.80 % 12.38 % 25.75 %
4 26.78 % 24.67 % 37.62 % 30.10 % 35.45 % 30.86 %
5 19.81 % 18.57 % 47.52 % 23.00 % 49.38 % 22.95 %
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 10 of 13
Capdeferro (2011) noted that social networks that include learning guides and
facilitators can support lifelong learning. In the FS&OK case only, the transfer of
resources generated by participants from the social network to MiriadaX positively
influenced both the participation level in C platforms and participantsperceptions
of the influence of cooperation on their learning.
Limitations
Some weak points were detected in the model, and the research team is addressing
these in light of previous experiences.
High rate of commencing participants who drop out after the first module (54.4 and
53.7 %in FS&OK and AEI,respectively). This may be related to the heterogeneity of
participantsprofiles; if, after the first module, they detect the absence of learning
resources and objectives appropriate to their learning style and other
characteristics, they may drop out. This explanation finds support in the low
dropout rate from the second module. The requirement is to prove that an X
platform, adapted to the differing profiles and interests of MOOC participants, can
reduce the dropout rate.
Difficulty in managing flows of knowledge in learning cooperatively. It has been
shown that the flow of cooperatively created knowledge between platforms affects
perceptions of learning outcomes. However, social networks do not facilitate the
organization of resources cooperatively generated by participants. This difficulty
increases in attempting to organize resources between X and C platforms. Future
research must focus on how best to use knowledge management systems in
MOOCs to classify and organize resources, as well as to facilitate search and
subsequent implementation.
Acknowledgements
We would like to thank the support of the Government of Aragon, the European Social Fund and the Ministry of
Education of the Region of Castilla-León for their support, as well as the research groups (LITI, http://www.liti.es;
GIDTIC, http://gidtic.com and GRIAL, http://grial.usal.es).
Authorsinformation
Ángel Fidalgo-Blanco is Director of the Laboratory for Innovation in Information Technology at the Polytechnic
University of Madrid and has participated actively as principal investigator in R&D projects. He has organised seminars
and conferences over many years and is currently President of the organising committee for the International
Conference of Learning, Innovation and Competitiveness (CINAIC, Spanish abbreviation). His work as an active
researcher in educational innovation, knowledge management, educational technologies and educational
communities based on social networks has generated numerous publications and information products.
María Luisa Sein-Echaluce is Director of Virtual Campus and Professor of Applied Mathematics in the School of
Engineering and Architecture at the University of Zaragoza. She is principal researcher in the Research and Innovation
Group in Training supported by Information and Communication Technology(GIDTIC, Spanish abbreviation). She is
President of the Scientific Committee of the International Conference of Learning, Innovation and Competitiveness
(CINAIC, Spanish abbreviation) and sits on evaluation committees for calls for local innovation projects and for
international conferences. Her research currently focuses on the application of technologies to cooperative
methodologies and usage of Open Source LMS and other tools for online adaptive learning.
Francisco José, García-Peñalvo completed his undergraduate studies in Informatics at the University of Salamanca
and the University of Valladolid and his PhD at the University of Salamanca. He is head of the GRIAL research group
(Research Group on Interaction and eLearning). His main research interests include eLearning, Computers and
Education, Adaptive Systems, Web Engineering, Semantic Web and Software Reuse. He has led and participated in
more than 50 research and innovation projects and was Vice Chancellor Innovation at the University of Salamanca
between March 2007 and December 2009. He has published more than 300 articles in international journals and
conferences and has been guest editor of several special issues of international journals, including Online Information
Review,Computers in Human Behaviour and Interactive Learning Environments. He is also a member of the program
committee of several international conferences and is a reviewer for several international journals. He is currently
coordinator of the Education in Knowledge Society PhD Programme at the University of Salamanca.
Fidalgo-Blanco et al. International Journal of Educational Technology in Higher Education (2016) 13:24 Page 11 of 13
Author details
1
Department of Geological and Mining Engineering, School of Mining and Energy Engineering, Technical University of
Madrid, Ríos Rosas, 21, 28003 Madrid, Spain.
2
Department of Applied Mathematics, School of Engineering and
Architecture, University of Zaragoza, María de Luna, 3, 50018 Zaragoza, Spain.
3
Department of Computer Science,
Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain.
Received: 27 November 2015 Accepted: 30 March 2016
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Resumen Los " Cursos Masivos en Abiertos " o MOOC se están convirtiendo en una de las tecnologías de gran penetración en el terreno educativo en los últimos tiempos, considerándose por algunos como una tecnología que creará un tsunami sobre la educación universitaria, transformándola como no había ocurrido anteriormente y cambiando sus bases de actuación. Por el contrario, su puesta en acción nos está aportando referencias contrarias, y desde las grandes esperanzas depositadas en los mismos se está pasando a situaciones más realistas. En el artículo se analizan algunas de sus posibilidades y se presentan diferentes reflexiones para su aplicación educativa apoyándonos en modelos emergentes de aprendizaje. Palabras clave: MOOC; innovación educativa; aprendizaje móvil. Abstract The MOOC or " Mass in Open Courses " are becoming one of the technologies of great insight into the educational field in recent times, considered by some as a technology that will create a tsunami on transforming university education as he had not previously occurred and changing their bases of operation. Conversely putting it into action is bringing us contrary references, and from the high hopes for them are switching to more realistic situations. The article discusses some of its possibilities and different reflections for educational application are presented relying on emerging learning models.