Towards a Hybrid World

The Fuzzy Quality of Collaboration/Interaction (FuzzyQoC/I) Hybrid Model

in the Semantic Web 3.0

Sofia B. Dias

, Sofia J. Hadjileontiadou

, José A. Diniz

and Leontios J. Hadjileontiaids

Faculdade de Motricidade Humana, Universidade de Lisboa, 1499-002 Cruz Quebrada, Lisbon, Portugal

Hellenic Open University, Praxitelous 23, GR-10562, Athens, Greece

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece

Keywords: Cloud Learning Environment, Fuzzy Logic/Ontologies, Hybrid Modelling, i-Treasures, Online Learning

Environment (OLE), Quality of Collaboration (QoC), Quality of Interaction (QoI), Semantic Web 3.0.

Abstract: As a decision support tool, a hybrid modelling can offer the ability to better understand the dynamics of a

particular ecosystem. This paper proposes a hybrid approach that may serve as a means to synthesize/represent

knowledge obtained from the data, in order to explore online learning environment (OLE) states, based on

different scenarios. The potentiality of the quality of collaboration (QoC) within an Internet-based computer-

supported collaborative learning environment and the quality of interaction (QoI) with a learning management

system (LMS), both involving fuzzy logic-based modeling, as vehicles to improve the personalization and

intelligence of an OLE is explored. In this approach, a novel framework could be established, when bridging

the fields of blended- and collaborative-learning into an enhanced educational environment. The combined

measures (i.e., QoC, QoI) can form the basis for a more realistic approach of OLEs within the concept of

semantic Web and the associated Web 3.0 features, as they effectively capture the behaviour of the

stakeholders involved in the context of Higher Education. Finally, a potential case study of the examined

hybrid modelling (FuzzyQoC/I), referring to the “i-Treasures” European FP7 Programme, is discussed, to

explore its functionality/applicability under pragmatic learning scenarios, serving as a proof of concept.

1 INTRODUCTION

The concept “Semantic Web” has been used

inconsistently by academic researchers, holding a

landscape of different fields, technologies, concepts

and applications. From one point of view, Semantic

Web technology could play an important role in the

context of Learning Management Systems (LMSs),

giving the possibility to organize information for easy

retrieval, reuse, and exchange between different

learning systems/tools. From another, combined with

the concept of intelligent LMS (iLMS), blended (b)-

learning scenarios can offer a number of learning

tools, in a wide range of interaction and collaboration

(Dias et al., 2014; Dias, 2014). Lukasiewicz and

Straccia (2008), more pragmatically, have examined

five of the most important challenges facing Semantic

Web, namely: vastness, vagueness, uncertainty,

inconsistency, and deceit. However, nowadays, the

central challenge would be to provide adapted and

personalized alternatives, where intelligent models

could contribute, involving artificial intelligence and

incertitude modeling, e.g., via Fuzzy Logic (FL). The

latter is an efficient field that is suitable for dealing

with vagueness. In addition, it is considered a form of

continuous multi-valued logic allowing “computing

with words” and modeling complex systems,

characterized by imprecise and vague behaviours by

means of a linguistic approach (Zadeh, 1965, 1971).

In general, the whole point of Web 3.0 is to provide

accessible information to people and computers at

anytime from anywhere. Furthermore, with new

technological innovations for applying intelligent

agents (Web 4.0), cloud computing services has been

coined as an umbrella term to describe a category of

sophisticated on-demand computing services,

initially offered by commercial providers (such as

Amazon, Google, and Microsoft) (Voorsluys et al.,

2011). By embedding the cloud computing within

iLMS, access to large amount of data and different

computational learning resources/environments

becomes feasible.

187

B. Dias S., J. Hadjileontiadou S., Diniz J. and J. Hadjileontiaids L..

Towards a Hybrid World - The Fuzzy Quality of Collaboration/Interaction (FuzzyQoC/I) Hybrid Model in the Semantic Web 3.0.

DOI: 10.5220/0005404901870195

In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 187-195

ISBN: 978-989-758-108-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Based on the aforementioned perspectives, this paper

examines the potentiality of the quality of

collaboration (QoC), within an Internet-based

computer-supported collaborative learning (CSCL)

environment, and the quality of interaction (QoI) with

a LMS, both involving FL-based modeling as a

vehicle to improve the personalization and

intelligence of an online learning environment (OLE).

Furthermore, these combined measures, i.e., QoC and

QoI, can form the basis for a more pragmatic

approach of OLEs via Web analytics and Web

controlling/monitoring, within the concept of

semantic Web and the associated Web 3.0 features. A

detailed description of this idea is explored and

described in the succeeding section, towards a hybrid

modelling approach, namely FuzzyQoC/I, where the

term hybrid implies both the modelling of the

interaction between the users (QoC) and between

them and the system (LMS QoI).

2 THE HYBRID APPROACH IN

THE SEMANTIC WEB 3.0

As underlined before, Higher Education paradigms

are shifting to incorporate more online, blended,

collaborative and hybrid learning. An essential factor,

however, in determining the efficacy of online

learning environments towards the creation and

development of sustainable learning communities is

the users’ QoI with LMSs. From this perspective, the

FuzzyQoI model (Dias and Diniz, 2013) has shown

significant potential to: a) handle a multitude of

variables and inference upon them, furnishing us with

a quantitative approach to evaluate the QoI, both in

professors’ and students’ case; and b) function as a

means for better understanding and explaining the

nature of underlying aspects and causalities, which

influence the construction of users’ interaction

behaviour under the LMS-based b-learning approach.

In addition, the collaboration/metacognition-Fuzzy

Inference System (C/M-FIS) model (Hadjileontiadou

et al., 2003) has contributed to the quantitative

evaluation of the QoC, taking into account both the

personal (metacognitive) and the social

(collaborative) contexts.

A more detailed description of the FuzzyQoI and the

C/M-FIS models can be found in Dias and Diniz

(2013) and Hadjileontiadou et al. (2003),

respectively.

The exploratory trajectory followed through the case

studies and the systemic approach adopted in both

models revealed noticeable aspects within the

CSCL/OLE, which all are influenced by the human

behaviour characteristics. OLE usability, profiles and

interaction issues holistically relate with the human

factor. This also holds for the collaborative

interactions within a CSCL environment. Combined

with the boosting of the Internet metamorphosis to an

increasingly social tool, the need for online education

that efficiently incorporates users’ characteristics,

evolving social needs and expectations becomes

apparent. This, really, could transform the perception

of the LMS to a more intelligent tool that functions in

a more “personalized” way.

Talking about personalization, the problem becomes

crucial when authors want to provide materials,

which should support different users in their different

phases of the learning process. The task, thus, is to

find a (technological and procedural) solution in order

to effectively support the learners. The knowledge

society demands competencies and skills that require

innovative educational practices based on open

sharing and the evaluation of ideas, fostering

creativity and teamwork (collaboration) among the

learners.

The vast number of tools supporting collaboration on

the Web is an indicator that social software tools are

not only a flash in the pan, but lead to a new notion of

learning and a measure for sustainable competence

development. Towards such endeavour, ideas like

semantic analysis of learning activities, tagging

opportunities with a focus on appropriateness for

learning, visualization of communities and people

with similar (learning) interests, new approaches to

content and network analysis, and a technical

integration of different LMS, should be considered.

These ideas clearly comply with the emerging

concept of semantic Web 3.0 (Lukasiewicz and

Straccia, 2008). Actually, the Web 3.0 is about

connecting data, all data, everywhere and putting

them in massive graph databases that can be read and

conceptually understood by computers. Currently,

most Web pages are designed to be read by people,

not machines. Nevertheless, because linked, graph-

based data are machine-readable, hence, computers

could be able to answer increasingly sophisticated

questions for the user-to interpret data, understand

context, infer meaning and do reasoning. In other

words, semantic databases, which sprang out of

artificial intelligence, allow computers first to

“think”, to understand big, conceptual queries, and

then find and combine exactly the information

humans need to make ever-smarter decisions.

In this context, teaching-learning process should be

seen as a complex and constantly dynamic reality

(Peters, 2001; Garrison and Kanuka, 2004; Bates,

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

188

2005) that could be supported by Information and

Communication Technologies (ICTs)-based techno-

pedagogical models that include representations,

visions, skills, resources, attitudes and practices of

their social actors, all placed under the concept of the

semantic Web 3.0. In fact, the combination of

traditional Face-to-Face (F2F) and online learning,

within the context of b-learning, offers different

delivery methodologies/modes that have the potential

to optimize the learning development, deployment

costs and time (Oliver and Trigwell, 2005). In

parallel, education paradigms shifted to incorporate

online collaborative learning environments (Johnson

et al., 2013). Actually, collaborative learning can

assist students to feel more interactive and also exerts

a positive influence in terms of motivation, behaviour

and self-determination, as well as engagement in

learning activities (Reeve and Tseng, 2011; Wijnia et

al., 2011).

It is noteworthy that nowadays, digital devices and

ICT, in general, intermediate the relationships

between two or more users, defining a kind of “social

interfacing” (de Souza e Silva, 2006). Within the

latter, not only the communication relationships are

reshaped, but also the space where these interactions

take place. The embedded mobility in the interfacing

allows the connection between physical and digital

spaces, supporting interconnectivity of

social/conceptual and technological interface under

the ubiquity perspective. In this way, interactive

activities, communicative understandings, learning

theories (especially as framed through cognitive

load), self-efficacy and self-regulation, become more

dynamic and challenging issues to be addressed

within an OLE.

Taking the aforementioned perspectives together, an

enhanced LMS-based intelligent teaching/learning

modeling approach could be formed, by suggesting

the incorporation of the hybrid and innovative

processing techniques from the fields of fuzzy

modeling and fuzzy set theory. In this fashion, a novel

research framework could be established, by

exploring the ways effective teaching could be

accomplished, when bridging the fields of b- and

collaborative (c)-learning into a hybrid and enhanced

teaching-learning environment. In this way, a holistic

approach of the fundamental channels from which the

educational process is conveyed could be adopted,

combining cognitive and social information of the

peers’ behaviour and interactions. Consequently, the

following objectives could be set:

• development of an educational and innovative

framework around the online instructional

environments, by exploring the potentialities of

b/c-learning/teaching in the context of Higher

Education and semantic Web 3.0,

• contribution to educational improvement on

teaching practice supported in the LMS Moodle

(or OLEs in general), providing new tools more

suited to users’ QoC and QoI,

• development, application and validation across

a vast number of users (students/professors) of

efficient hybrid modeling approaches of LMS

Moodle data, based on fundamentals of Fuzzy

Logic-based Inference Systems (FISs),

• introduction of extended means, new tools and

pathways for shifting from the typical form of

LMS to the iLMS (Dias et al., 2014),

incorporating issues, such as personalization

and technological adaptiveness,

• course effect analysis using the FuzzyQoI model

(Dias and Diniz, 2013), to examine how the

course content affects the users’ QoI with LMS

Moodle across the years,

• identification of possible macroscopic causal

dependencies, converged or dispersed

interaction trends, periodicities, specific

patterns dominance in the LMS Moodle

interaction/collaborative/metacognitive

attitude, all reflected at the FISs, i.e., C/M-FIS

and FuzzyQoI models response,

• comparative analysis across the forthcoming

hybrid modeling approaches, blending the

benefits of each one and identifying their pros

and cons, and

• construction of new guidelines/

recommendations about the enhancement of

OLE-based teaching/learning processes,

contributing to the enrichment of the higher

education institutions (HEIs) services and

reformulation of educational policies/practices.

Adding to the above, ontologies could be used to link

the quantitative metrics of QoC and QoI to

information coding, so it could easier be processed by

software agents, opening the door for a slew of new

semantic Web 3.0-based applications. In fact,

according to Gruber (1993), an ontology is a formal,

explicit specification of a shared conceptualization.

Pragmatically, a common ontology defines the

vocabulary with which queries and assertions are

exchanged among software entities. An ontology has

concepts that identify the data entities of interest and

these concepts are organized in a hierarchy, called a

taxonomy; concepts might have attributes and

relationships, whereas a data item that has been

marked up with a label corresponding to a concept is

called a data instance. Through this organization,

ontologies could contribute to a shared and common

TowardsaHybridWorld-TheFuzzyQualityofCollaboration/Interaction(FuzzyQoC/I)HybridModelintheSemantic

Web3.0

189

Figure 1: The architecture of the proposed FuzzyQoC/I hybrid model that connects the educational and fuzzy worlds,

integrating the C/M-FIS and FuzzyQoI models. OLE: Online Learning Environment, CLE: Cloud Learning Environment,

CSCL: Computer-Supported Collaborative Learning, LMS: Learning Management System, HEI: Higher Education

Institution, QoC: Quality of Collaboration, QoI: Quality of Interaction.

understanding of QoC and QoI that can be

communicated among the educational stakeholders

and iLMSs/iOLEs. As the latter involve Web-based

educational material, ontologies can be used to

describe relationships between pages and other data

(like QoC and QoI metrics), so to contribute to a

personalized supporting system that could maximize

the QoC and QoI; hence, enhance user’s

teaching/learning experience. They can, therefore, be

used to recommend learning resources of potential

interest to the learner that potentially increase his/her

QoI; even to recommend a “study-buddy”, with

whom the learner shares common abilities and

interests and can maximize his/her QoC when

collaborates with him/her. From a technical point of

view, this could be achieved by employing, for

example, the DARPA Agent Markup

Language/Ontology Inference Layer (DAML+OIL)

ontology language (McGuinness et al., 2002), which

describes structure of the domain, combined with the

Resource Description Framework (RDF), which is

used, in the same time, to describe specific instances,

and Ontology Web Language and Information

Retrieval (OWLIR) that handles the Event Ontologies

(Connolly et al., 2001).

In one step further, using Cloud computing platforms

(e.g., Microsoft Azure) and technologies in

conjunction with semantic Web 3.0 technology and

metadata, a shift from the traditional LMS to Cloud

Learning Environments (CLEs) could be achieved, by

facilitating the autonomous or collaborative study of

user-chosen blends of content and courses from

heterogeneous sources (Mikroyannidis, 2012). In

CLE, semantic knowledge base serves as the core of

the OLE, facilitating learners in finding educational

services and collaborate on the Cloud, evoking

collaborative ontology management techniques. In

this concept, the proposed hybrid QoC/I model could

combine both LMS and CLE in the learning process,

placing the user at the centre and capturing his/her

interaction with both contexts. This could, actually,

assist HEIs to enrich their educational framework,

facilitating, at the same time, the professors’/learners’

interaction (both autonomous and collaborative) with

the OLEs.

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

190

A schematic presentation of the proposed

architectural structure of the FuzzyQoC/I hybrid

model is depicted in Figure 1. Apparently, the online

communication channels considered in Fig. 1 should

not be seen as static; yet with fluidity, directed to

provide flow opportunities of communication in

human-computer interaction in an OLE. From a

common perspective, learners should be

behaviourally, intellectually, and emotionally

involved in online learning tasks. Nevertheless, the

role of educational technology is to improve

academic literacies in students, to create engaging

communities of practice, and to improve learner’s

motivation and self-empowered learners (Wankel and

Blessinger, 2013).

2.1 Fuzzy Ontologies

In the hybrid model presented in Fig. 1, the role of FL

is catalytic. One of the issues that could also be

approached from the FL concept is the ontology one.

As it was mentioned above, the Semantic Web allows

relational knowledge to be embedded as metadata in

Web pages, enabling machines to use ontologies and

inference rules in retrieving and manipulating data. In

this vein, ontologies are a key component of the

Semantic Web.

As an extension of the ontologies, the fuzzy

ontologies could also be defined (Widyantoro and

Yen, 2001), incorporating the functionality of an

ontology with the flexibility of the FL. The main

definitions and characteristics of fuzzy ontologies are

epitomized bellow, as a glimpse to the enhanced

potentialities of the hybrid modeling of Fig. 1.

In general, the definition of a fuzzy ontology structure

includes: concepts, fuzzy relations among concepts, a

concept hierarchy or taxonomy, non-hierarchical

associative relationships and a set of ontology

axioms, expressed in an appropriate logical language.

Consequently, a lexicon for a fuzzy ontology

includes: lexical entries for concepts (knowledge

about them can be given by fuzzy attributes, with

context-dependent values), lexical entries for fuzzy

relations, coupled with weights expressing the

strength of associations, and reference functions

linking lexical entries to concepts or relations they

refer to.

Apart from the structural characteristics of the fuzzy

ontology described above, the issue of fuzzy ontology

mapping should also be considered. In particular,

ontology mapping is the effective method to solve the

problems of knowledge sharing and reusing across

the heterogeneous ontologies in the Semantic Web

(Doan et al., 2002). The current ontology mapping

technologies are not sufficient for fuzzy ontologies

(Ma et al., 2014). Therefore, with the growing

number of heterogeneous fuzzy ontologies in the

Semantic Web, the fuzzy ontology mapping that can

handle fuzzy data becomes an important research

topic. The aforementioned characteristics of the fuzzy

ontologies show the potentiality of FL to handle

heterogeneous data and perform more effective

reasoning at the ontological level. Hence, provisional

embedding within the hybrid model presented in Fig.

1 enriches its ingredients, towards the successful

integration of the knowledge representation in the

Semantic Web within the educational context.

Next, a provisional case study of the proposed hybrid

model is discussed, with regard to the i-Treasures

Programme.

3 THE I-TREASURES CASE

STUDY

The i-Treasures: “Intangible Treasures - Capturing

the Intangible Cultural Heritage (ICH) and Learning

the Rare Know-How of Living Human Treasures” is

an Integrated Project (IP) of the European Union's 7th

Framework Programme ICT for Access to Cultural

Resources (February 1, 2013-2017). The main aim of

i-Treasure is to develop an open and extendable

platform to provide access to intangible cultural

heritage resources and, at the same time, to propose a

novel strategic framework for the safeguarding and

transmission of ICH (http://i-treasures.eu).

Considering the latter, it is apparent that the issues of

personalized learning, LMS interaction, ontologies

coding and enriched feedback (facilitated via a

sensory motor learning approach) are common

elements. Based on this mutuality in the design, direct

analogies could be considered as injection of the

hybrid model fuzziness in the evolution of the i-

Treasures Programme. In particular:

• Since the LMS is one of the main facilitator of

the user’s interaction with the i-Treasures

platform, the simultaneous measurement of

his/her QoI with the LMS via the QoC/I

modeling (see Fuzziness/Hybrid Modeling in

Fig. 1), could be an important enhancement in the

functionality of the i-Treasures platform. This

could also be used as effective feedback to the

user (see Personalized Feedback in Fig. 1), and

combined with the sensorimotor learning

concept, could evoke reflective processes

towards the intention for improvement.

TowardsaHybridWorld-TheFuzzyQualityofCollaboration/Interaction(FuzzyQoC/I)HybridModelintheSemantic

Web3.0

191

Figure 2: The i-Treasures use-cases referring to traditional and contemporary singing, traditional and contemporary dance,

traditional pottery, and contemporary music composition (http://i-treasures.eu).

• Similarly to the case of QoI, the QoC could serve

as dynamic feedback to the i-Treasures user,

reinforcing transitional changes and supporting

knowledge development. As cultural knowledge

is transmitted not only via changes in an

individual across time, but also via the groups’

behavior over time (Flynn and Siegler, 2007), the

dynamic monitoring of the FL-based estimated

QoC and QoI could facilitate the capturing of

such changes (e.g., as transitions across the

ellipses of Fig. 1, both at the individual and at the

group level), exposing attitude shifts and trends,

accompanied by cultural knowledge

development. This approach could also be

combined with the affective information of the i-

Treasures acquisition modules to correlate the

user’s emotional engagement with the evolution

of his/her QoI and QoC trends.

• The MEBNs (Laskey, 2008) used for the

ontology-based knowledge representation in i-

Treasures Programme could be accompanied by

the concept of fuzzy ontologies, described in the

previous section (see Fig. 1), in an effort to

handle uncertainty in alternative to the

probability way, employing the advantageous

characteristics of the FL.

• Since the i-Treasures Programme is multi-

layered, with a variety of acquisition modules

and different use-cases, its performance

evaluation and monitoring (as a whole system) is

quite difficult to be approached in a mono-

dimensional way. In this context, an estimation

of the general performance indices (GPIs) for

each use case, as well as for their sub-use cases

could be achieved based on nested FISs. In

addition, the GPI of the integrated platform could

be estimated, reflecting its overall quality, as

shown in Fig. 3.

From the above, it is clear that the FuzzyQoC/I hybrid

model could be used to capture micro- (localities) and

macro- (generalities) levels of the i-Treasures system

use.

4 FINAL CONSIDERATIONS

The concept of a FL-based hybrid model, which

combines QoC and QoI within the context of

semantic Web 3.0 and CLEs in a holistic perspective

of the online learning educational context, shedding

light upon the requirements for offering personalized

feedback to learners, supporting them throughout

their learning journey, and enriched recommendation

services to HEI policy/service quality managers, was

the focus of this paper. The provisional hybrid model

presented here combines the FL-based approaches in

modelling collaborative/metacognitive and LMS user

interaction data, along with the perspectives of fuzzy

ontologies and fuzzy ontology mapping techniques.

In this way, higher flexibility, more enhanced

modeling capabilities and multidimensional inference

from the fusion of information estimated at various

levels of interaction/collaboration within the co-

existing CSCL environments and CLE/OLEs under

the b-learning concept are provided. The dynamic

feedback of QoI and QoC, combined in the proposed

FuzzyQoC/I hybrid approach, can account for the

non-stationarities seen in students’ learning process,

providing more pragmatic capturing of the underlying

interaction trend-shifts and/or artefacts.

Moreover, the hybrid concept adopted in this paper is

in line with the European Union’s perspective about

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

192

Figure 3: Fusion of the general performance indicators (GPIs) of each use-case of the i-Treasures with Web platform/LMS

performance indicators (PIs) as FIS inputs to output the integrated platform GPI of the i-Treasures.

the trends and evolution of Higher Education in the

next years (Johnson et al., 2015).

The metrics of QoC and QoI (combined in the hybrid

model) are of great importance, as they could become

key-discriminators amongst hybrid learning

environments, as emerging digital tools make it easier

for students to ask and respond to each other’s

questions and for instructors to provide feedback in

real-time. In line with this, the personalized feedback

based on the FL-based estimated QoC and QoI could

also help instructors to leverage components of online

learning and to make personalized learning scalable

in large introductory classes. Compared to the

traditional model of learning, in which space is

needed to accommodate hundreds of students, hybrid

learning can address the learning path of each

individual student. From a motivational point of view,

our approach resembles the efforts for hybrid

modelling of instructional design (ID), such as the 4-

Component/ID (4C/ID) (Van Merriënboer et al.,

2003). In the 4C/ID, a hybrid-modelling framework

for scaffolding practice and just-in-time information

presentation, aiming to control cognitive load

effectively, is presented. Taking this further, the

FuzzyQoC/I model could be seen as a flexible

nutshell, where such ID approaches could be

encapsulated and developed in a synchronized way,

towards the maximization of the learner’s learning

experience, both at the task (cognitive load) and

collaboration/interaction (QoC/QoI) levels.

In the same view of the aforementioned, the

implementation potentiality of the hybrid modeling

was examined in a real case study, i.e., the i-Treasures

programme, which is a currently EU FP7 running (up

to 2017), aiming at the development of an open and

extendable platform to provide access to ICH

resources, enable project knowledge exchange

between researchers, and contribute to the

transmission of rare know-how from Living Human

Treasures to apprentices via sensorimotor learning.

Due to the nature and design of the i-Treasures, its

structural characteristics are in direct connection with

the ones of the FuzzyQoC/I hybrid model. In this

vein, the applicability of the concept behind the

hybrid approach to the real case of the i-Treasures

was explored and possible interactions were

identified. The outcomes of the provisional adoption

of this theoretical approach in a case study will allow

to further validate the proposed hybrid methodology

and expand its database and implementation on case

studies from diverse areas (such as the i-Treasures

Programme), for its further generalization

refinement. It is our hope that this effort could

significantly add to the appreciation of the

potentialities of the newly available technological

means and networking concepts, such as semantic

Web 3.0, in the field of Higher Education. Moreover,

the ideas discussed in the present paper expectantly

could provide an intelligent framework for possible

reforms and alterations to the b- and c-learning

modeling; hence, to effectively affect the educational

processes within online teaching/learning

environments at HEIs.

ACKNOWLEDGEMENTS

The first author has been supported by the Foundation

for Science and Technology (FCT, Portugal)

(Postdoctoral Grant SFRH/BPD/496004/2013).

Moreover, this work was realized within the

framework of the EU FP7-ICT-2011-9-ICT-

2011.8.2, under the grant agreement n° 600676: "i-

Treasures" Project (http://i-treasures.eu).

TowardsaHybridWorld-TheFuzzyQualityofCollaboration/Interaction(FuzzyQoC/I)HybridModelintheSemantic

Web3.0

193

REFERENCES

Bates, T., 2005. Technology, e-learning and distance

education. London, Routledge.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K., 2004.

Learning styles and pedagogy in post-16 learning: a

systematic and critical review. London, UK, Learning

and Skills Research Centre/University of Newcastle.

Connolly, D., van Harmelen, F., Horrocks, I., McGuinness,

D. L., Patel-Schneider, P. F., & Stein, L. A., 2001.

DAML+OIL (March 2001) Reference Description.

W3C Note 18 December 2001.

Davis, E., 1990. Representations of Commonsense

Knowledge. San Mateo, CA, Morgan Kaufmann.

De Souza e Silva, A., 2006. From Cyber to Hybrid Mobile

Technologies as Interfaces of Hybrid Spaces. Space

and culture, 9(3), 261-278.

Dias, S. B., 2014. Towards an intelligent online learning

environment. A systemic approach. Saarbrücken,

Germany, LAP Lambert Academic Publishing.

Dias, S. B., & Diniz, J. A., 2013. FuzzyQoI model: A fuzzy

logic-based modelling of users’ quality of interaction

with a learning management system under blended

learning. Computers & Education, 69, 38-59.

Dias, S. B., Diniz, J. A., & Hadjileontiadis, L. J., 2014.

Towards an intelligent learning management system

under blended learning: trends, profiles and modelling

perspectives. In J. Kacprzyk, & L. C. Jain (Eds.),

Intelligent Systems Reference Library, Volume 59.

Berlin/Heidelberg, Springer-Verlag,

Doan, A., Madhavan, J., Domingos, P., & Halevy, A., 2002.

Learning to map between ontologies on the semantic

Web. Proc. of the 11th International World Wide Web

conference (pp. 662-673).

Felder, R. M., & Silverman, L. K., 1988. Learning and

teaching styles in engineering education. Engineering

Education, 78(7), 674-681.

Flynn, E., & Siegler, R., 2007. Measuring change: Current

trends and future directions in microgenetic research.

Infant and Child Development, 16(1), 135-149.

Garrison, D. R., & Kanuka, H., 2004. Blended learning:

Uncovering its transformative potential in higher

education. The Internet and Higher Educ., 7, 95-105.

Graf, S., 2007. Adaptivity in learning management systems

focussing on learning styles (Unpublished PhD Thesis),

Vienna Univ. Technology, Vienna, Austria.

Gruber, T. R., 1993. A translation approach to portable

ontologies. Knowledge Acquisition, 5(2), 199-220.

Hadjileontiadou, S. J., Nikolaidou, G. N., Hadjileontiadis,

L. J., & Balafoutas, G. N., 2003. A Fuzzy Logic

Evaluating System to Support Web-based

Collaboration Using Collaborative and Metacognitive

Data, In Proc. of the 3rd IEEE International

Conference on Advanced Learning Technologies

(ICALT’03) (pp. 96-100). Athens, Greece.

Johnson, L., Adams Becker, S., Cummins, M., Estrada, V.,

Freeman, A., & Ludgate, H., 2013. NMC Horizon

Report: 2013 Higher Education Edition. Austin, Texas,

The New Media Consortium.

Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A.,

2015. NMC Horizon Report: 2015 Higher Education

Edition. Austin, Texas, The New Media Consortium.

Jonassen, D. H., & Grabowski, B. L., 1993. Handbook of

individual differences, learning, and instruction.

Hillsdale, New Jersey, Lawrence Erlbaum Associates.

Laskey, B. K., 2008. MEBN: A language for first-order

Bayesian knowledge bases. Artificial Intelligence,

172(2-3), 140-178.

Liu, Y., 2014. Meditations on the semantic net: Oriented

library information service in cloud computing era. In

S. Li, Q. Jin, X. Jiang, & J.J. Park (Eds.) Frontier and

future development of information technology in

medicine and education (pp. 1863-1870). LNEE,

Volume 269 (pp. 1863-1870). Netherlands, Springer.

Lukasiewicz, T., & Straccia, U., 2008. Managing

uncertainty and vagueness in description logics for the

Semantic Web. Web Semantics: Science, Services and

Agents on the World Wide Web, 6(4), 291-308.

Ma, Z., Zhang, F., Yan, L., & Cheng, J., 2014. Fuzzy

Semantic Web Ontology Mapping. In Fuzzy Knowledge

Management for the Semantic Web (pp. 157-180).

Berlin-Heidelberg, Springer.

McGuinness, D. L., Fikes, R., Hendler, J., & Stein, L. A.,

2002. DAML+OIL: An ontology language for the

semantic Web. IEEE Intelligent Syst., 17(5), 72-80.

Mikroyannidis, A., 2012. A semantic framework for cloud

learning environments. In C. Lee (Ed.), Cloud

computing for teaching and learning: strategies for

design and implementation (pp. 17–31). Hershey, PA,

IGI Global.

Oliver, M., & Trigwell, K., 2005. Can ‘blended learning’ be

redeemed?. E-learning and Dig. Media, 2(1), 17-26.

Paquette, G., 2014. Technology-based instructional design:

evolution and major trends. In J. M. Spector, M. D.

Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of

Research on Educational Communications and

Technology (pp. 661-671). New York, Springer.

Peters, O., 2001. Learning and teaching in distance

education: Analysis and interpretations from an

international perspective. London, Kogan Page.

Reeve, J., & Tseng, C. -M., 2011. Agency as a fourth aspect

of students’ engagement during learning activities.

Contemporary Educational Psychol., 36(4), 257-267.

Van Merriënboer, J. J., Kirschner, P. A., & Kester, L., 2003.

Taking the load off a learner's mind: Instructional

design for complex learning. Educational psychologist,

38(1), 5-13.

Voorsluys, W., Broberg, J., & Buyya, R., 2011.

Introduction to cloud computing. Cloud Comp., 1-41.

Wankel, C., & Blessinger, P., 2013. Increasing student

engagement and retention in e-learning environments

(Web 2.0 and blended learning technologies). Howard

House, Wagon Lane, Bingley, UK, Emerald Group.

Whittaker, J., 2009. Graphical models in applied

multivariate statistics. Hoboken, NJ, Wiley Pub.

Widyantoro, D. H., & Yen, J., 2001. Incorporating Fuzzy

Ontology of Term Relations in a Search Engine. Proc.

of the 1st BISC International Workshop on Fuzzy Logic

and the Internet (pp. 155-160). Berkeley, USA.

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

194

Wijnia, L., Loyens, S. M. M., & Derous, E., 2011.

Investigating effects of problem-based vs. lecture-

based learning environments on student motivation.

Contemporary Educ. Psychology, 36(2), 101-113.

Zadeh, L. A., 1965. Fuzzy sets. Inf. Control, 8, 338-353.

Zadeh, L. A., 1971. Toward a theory of fuzzy systems. In

R. E. Kalman, & N. De Claris (Eds.), Aspects of

Network & System Theory. NY, Rinehart & Winston.

TowardsaHybridWorld-TheFuzzyQualityofCollaboration/Interaction(FuzzyQoC/I)HybridModelintheSemantic

Web3.0

195