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Computers in Human Behavior

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Creation of Web 2.0 tools ontology to improve learning

http://dx.doi.org/10.1016/j.chb.2014.10.0260747-5632/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author at: Vilnius University Institute of Mathematics andInformatics, Akademijos str. 4, 08663 Vilnius, Lithuania.

E-mail addresses: [email protected] (E. Kurilovas), [email protected] (A. Juskeviciene).

Please cite this article in press as: Kurilovas, E., & Juskeviciene, A. Creation of Web 2.0 tools ontology to improve learning. Computers in Human B(2014), http://dx.doi.org/10.1016/j.chb.2014.10.026

Eugenijus Kurilovas a,b,⇑, Anita Juskeviciene a

a Vilnius University Institute of Mathematics and Informatics, Akademijos str. 4, 08663 Vilnius, Lithuaniab Vilnius Gediminas Technical University, Sauletekio ave. 11, 10223 Vilnius, Lithuania

a r t i c l e i n f o

Article history:Available online xxxx

Keywords:OntologyWeb 2.0Collaborative learningSemantic searchLearning stylesLearning activities

a b s t r a c t

The aim of the paper is to present systematic review results on ontology development tools, to establishinterconnections between learning styles, preferred learning activities and related Web 2.0 tools, and alsoto create Web 2.0 tools ontology to interconnect learning activities with relevant Web 2.0 tools. Thisontology is necessary for learners to semantically search for suitable Web 2.0 tools while learning invirtual learning environments (VLEs). Suitability of Web 2.0 tools depends on preferred types of learningactivities which in its turn depend on preferred learning styles. The research results include: (1) system-atic review results on ontology development tools and ontology representation language/formats; (2)established interconnections between learning styles, preferred learning activities, and relevant Web2.0 tools using sets portrait method, and (3) creating Web 2.0 tools ontology to interconnect preferredlearning activities with relevant Web 2.0 tools in VLE. The research results will be implemented in iTEC– pan-European research and development project focused on the design of the future classroom fundedby EU 7FP. The research results presented are absolutely novel in scientific literature, and this makes thecurrent study distinct from all other works in the area.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The aim of the paper is to investigate and present systematicreview results on ontology development tools, to establish inter-connections between learning styles, preferred learning activitiesand related Web 2.0 tools, and to create Web 2.0 tools ontologyto interconnect learning activities with relevant Web 2.0 tools.

The proposed approaches to solve the problem are systematicreview, Triangular Fuzzy Numbers (TFN) method to select the bestrelevant ontology development tool, sets portrait method to inter-connect learning styles, preferred learning activities and Web 2.0tools in Moodle v2.2 virtual learning environment (VLE), and ontol-ogy creation using Protégé tool. The practical problem analysed inthe paper is how to create the ontology for the semantic searchengine necessary for learners to quickly and qualitatively find rel-evant Web 2.0 tools while learning in VLE Moodle. Suitability ofWeb 2.0 tools depends on preferred types of learning activitieswhich in its turn depend on preferred learning styles.

One of the more recent developments with the Web is an activ-ity known as the Semantic Web (or Web 3.0). The Semantic Web is

not a separate Web but an extension of the current one, in whichinformation is given well-defined meaning, better enabling com-puters and people to work in cooperation (Berners-Lee, Hendler,& Lassila, 2001). Two important technologies for developing theSemantic Web are XML and RDF, and a third important aspect ofthe Semantic Web is a set of ontologies. Ontology is a specificationof a conceptualisation (Gruber, 1993). It describes the concepts andrelationships of some phenomenon in the world. By using well-defined ontologies on the Web, it is possible for computers tomeaningfully process data since there is a common understandingof terms used and the relationships between these terms (Mohan &Brooks, 2003).

VLE is referred here as a single piece of software, accessed viastandard Web browser, which provides an integrated onlinelearning environment (Kurilovas & Dagiene, 2010). One of the mainparts of each VLE is Collaborative Web (or Web 2.0) tools. There-fore, in order to improve the adaptation quality of VLEs it is veryimportant to improve semantic search for Web 2.0 tools in VLEs.These tools support interaction, communication and collaborationamongst students and educators. As contemporary students areeducational content creators, consumers and distributors via Inter-net it becomes obvious that Web 2.0 tools play an important role inVLEs and learning process.

In the paper, a special attention is paid to improving VLEsuitability for different learning styles, i.e. VARK (Fleming,

ehavior

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Table 1Conversion of linguistic variables and into non-fuzzy values(according to Kurilovas and Dagiene (2010)).

Linguistic variables Triangular non-fuzzy values

Excellent 0.850Good 0.675Fair 0.500Poor 0.325Bad 0.150

2 E. Kurilovas, A. Juskeviciene / Computers in Human Behavior xxx (2014) xxx–xxx

2001). The acronym VARK stands here for Visual (V), Aural (A),Read/Write (R), and Kinaesthetic (K). Fleming (2001) defineslearning style as ‘‘an individual’s characteristics and preferredways of gathering, organising, and thinking about information. Itis focused on the different ways that we take in and give outinformation.

A number of the other learning styles models were analysed ine.g. Beres, Maguar, and Turcsanyj-Szabo (2012), Dorca, Lima,Fernandes, and Lopes (2012), Lubchak, Kupenko, and Kuzikov(2012).

The rest of the paper is organised as follows: methodology ofthe research is presented in Section 2, research results arepresented in Section 3, discussion – in Section 4, and conclusion– in Section 5.

Section 3 containing research results and is divided into threeseparate parts:

(1) Systematic review results on definition and the roles of anontology, ontology development tools, and ontology repre-sentation language/formats.

(2) Established interconnections between learning styles, pre-ferred learning activities, and related Web 2.0 tools usingsets portrait method.

(3) Created Web 2.0 tools ontology to interconnect preferredlearning activities with relevant Web 2.0 tools in VLEMoodle v2.2.

2. Research methods

In order to specifically find clear definitions and the roles ofontology, ontology development tools, and ontology representa-tion language/formats, an exhaustive search conducting a System-atic Review was performed. This systematic review was conductedfollowing the process proposed by Kitchenham, Dyba, andJorgensen (2004) and Biolchini, Mian, Natali, and Travassos(2005). According to Biolchini et al. (2005), the term SystematicReview in software engineering is used to refer to a specific meth-odology of research, developed in order to gather and evaluate theavailable evidence pertaining a focused topic.

In contrast to the usual topic of literature review, unsystemati-cally conducted whenever one starts a particular investigation, aSystematic Review was developed, as the term denotes, in a formaland systematic way. This means that the research conduction pro-cess of a systematic type of review follows a very well defined andstrict sequence of methodological steps, according to aprioristicallydevelop protocol.

This instrument is conducted around the central issue, whichrepresents a core of the investigation, and which is expressed byusing specific concepts and terms, that must be addressed towardsinformation related to a specific, pre-defined, focused, and struc-tured question.

The methodological steps, the strategies to retrieve theevidence, and the focus of the question are explicitly defined inBiolchini et al. (2005). According to Kitchenham et al. (2004), thisprocess presents three main phases:

(1) Phase 1 – Planning: In this phase, the research objectivesand the review protocol are defined. The protocol constitutesa pre-determined plan that describes the research questionsand how the systematic review will be conducted.

(2) Phase 2 – Conduction: During this phase, the primary stud-ies are identified, selected and evaluated according to theinclusion and exclusion criteria established previously. Foreach selected study, data are extracted and synthesized; and

Please cite this article in press as: Kurilovas, E., & Juskeviciene, A. Creation of W(2014), http://dx.doi.org/10.1016/j.chb.2014.10.026

(3) Phase 3 – Reporting: In this phase, a final report is formattedand presented.

In the paper, TFN method is used to select the best relevantontology development tool. According to Kurilovas and Dagiene(2010), there is scientific evidence that this method is convenientfor evaluating the quality of many different kinds of software alter-natives in the market.

According to Ounaies, Jamoussi, and Ben Ghezala (2009), thewide-used measurement criteria of the decision attributes qualityare mainly qualitative and subjective. In this context, decisions areoften expressed in the natural language, and evaluators are unableto assign exact numerical values to different criteria. Assessmentcan be often performed by the linguistic variables such as ‘‘bad’’,‘‘poor’’, ‘‘fair’’, ‘‘good’’ and ‘‘excellent’’. These linguistic variablesallow reasoning with imprecise information, and they are com-monly called fuzzy values. Integrating these different judgmentsto obtain a final evaluation is not evident. In order to solve thisproblem, Ounaies et al. (2009) suggest using the fuzzy group deci-sion making theory to obtain final assessment measures. First, lin-guistic variable values should be mapped into non-fuzzy values. Inthe case of using the average TFNs, linguistic variables conversioninto triangular non-fuzzy values of the software quality evaluationcriteria should be as follows (see Table 1):

In order to obtain final evaluation results, one should use theexperts’ additive utility function, i.e. add all the numerical ratings(values) of the quality criteria multiplied by their normalisedweights (Kurilovas & Dagiene, 2010). The major is the meaningof the utility function the better is alternative.

For establishing interconnections between the sets of learningstyles, preferred learning activities, and related Web 2.0 tools setsportrait method was used.

Web 2.0 tools ontology to interconnect preferred learning activ-ities with relevant Web 2.0 tools in VLE Moodle v2.2 was createdusing the best selected tool Protégé.

3. Presentation and discussion

3.1. Systematic review results

The research questions addressed were as follows: What kind ofontology definitions is given in the literature? Which existingontology application area could be applied to develop technologiesontology based on learning activities? Which existing ontologydevelopment tool could be applied to develop technologies ontol-ogy based on learning activities? Which existing ontology develop-ment methodology could be applied to develop technologiesontology based on learning activities? Which existing ontologyrepresentation language could be applied to develop technologiesontology based on learning activities?

The keywords and related concepts dealing with these researchquestions and used during the review execution were as follows:ontology definition (what is an ontology/what are ontologies);

eb 2.0 tools ontology to improve learning. Computers in Human Behavior


E. Kurilovas, A. Juskeviciene / Computers in Human Behavior xxx (2014) xxx–xxx 3

ontology application (why do we need them/types of ontology/What are the roles of an ontology); ontology development (build-ing/creating) methodology and tools; ontology representationlanguages.

To find the works (also primary studies in the systematic reviewcontext), main publication databases were used: IEEEXplore, Sprin-gerLink and Google Scholar.

Systematic review results are as follows.The word ‘‘ontology’’ was taken from Philosophy, where it

means a systematic explanation of being. One of the first defini-tions was given by Neches et al. (1991): ‘‘an ontology defines thebasic terms and relations comprising the vocabulary of a topic areaas well as the rules for combining terms and relations to defineextensions to the vocabulary’’.

Since, ontologies are widely used for different purposes (e.g. nat-ural language processing, knowledge management, e-commerce,intelligent integration information, the Semantic Web, etc.) in dif-ferent communities (i.e., knowledge engineering, databases andsoftware engineering). Uschold and Jasper (1999) provided anew definition of the word ontology to popularise it in otherdisciplines: ‘‘An ontology may take a variety of forms, but it willnecessarily include a vocabulary of terms and some specificationof their meaning. This includes definitions and an indication ofhow concepts are inter-related which collectively impose astructure on the domain and constrain the possible interpretationsof terms’’.

According to Mizoguchi (2003), ontologies are used for variouspurposes: as a common vocabulary, data structure, explication ofwhat is left implicit, semantic interoperability, explication ofdesign rationale, systematisation of knowledge, meta-model func-tion, theory of content, etc.

Many disciplines now develop standardised ontologies thatdomain experts can use to share and annotate information in theirfields (Tankeleviciene & Damaševicius, 2010). It includes machine-interpretable definitions of basic concepts in the domain and rela-tions amongst them. In the current study, we use the following

Table 2Evaluation values of ontology development tools.

Criteria Ontolingua WebOn

1. General1.1 Interface clarity 0.150 0.8501.2 Interface consistency 0.850 0.8501.3 Speed of updating 0.150 0.5001.4 Overview 0.500 0.8501.5 Meaning of commands 0.850 0.8501.6 Identifiability of changes 0.500 0.5001.7 Stability 0.850 0.8501.8 Help system 0.850 0.150

2. Ontology2.1 Multiple inheritance 0.850 0.8502.2 Decomposition types 0.850 0.8502.3 Consistency checking 0.850 0.8502.4 Example ontologies 0.850 0.8502.5 Reusable ontologies 0.850 0.8502.6 High level primitives 0.850 0.8502.7 Ontological help 0.150 0.150

3. Cooperation3.1 Synchronous editing 0.850 0.8503.2 Ontology locking 0.850 0.8503.3 Browsing when locked 0.850 0.8503.4 Change recognition 0.150 0.1503.5 Export facilities 0.850 0.1503.6 Import facilities 0.850 0.150


ontology definition: ‘‘An ontology defines a common vocabularyfor researchers who need to share information in a domain’’. Inour case, there are two domain experts: informatics engineersand educators.

A series of approaches have been reported for developing ontol-ogies. In Corcho, Fernández-López, and Gómez-Pérez (2003), anoverview of the most known methodologies since 1990 ispresented.

In the current study, we develop ontology mainly based onUschold and King (1995) method which proposes four activities:(1) to identify the purpose of the ontology, (2) to build it, (3) toevaluate it, and (4) to document it. During the building activity,Uschold and King (1995) propose capturing knowledge, coding itand integrating other ontologies inside the current one. Uscholdand King (1995) also propose three strategies for identifying themain concepts in the ontology: a top-down approach, where themain abstract concepts are identified and then specialised intomore specific concepts; a bottom-up approach, in which the mostspecific concepts are identified and then generalised into moreabstract concepts; and a middle-out approach, in which the mostimportant concepts are identified and then generalised and specia-lised into other concepts. In the current study, we also focus ontools for our domain ontology development.

In the recent years, researchers have developed a lot of tools fordeveloping ontology e.g. Protégé, SWOOP, Top Braid composer,OilED, WebODE, Ontolingua, Internet Business Logic, OntoTrack,and IHMC Cmap Ontology Editor (Khondoker & Mueller, 2010).However, it is not easy to say which one is the best. In this paper,we assume that the particular tool is the best if it satisfies therequirements (criteria) determined. By systematic review, we haveselected the most popular tools and evaluated it according threedimensions/groups of criteria (see Table 2). First, there is a generaldimension, which refers to the aspects of the tools that can also befound in the other types of programs. This dimension refers toinformation about the user interface and the different actions theuser can perform (8 criteria). The second – the ontology dimension,

to OntoSaurus ODE Protégé

0.150 0.150 0.8500.850 0.850 0.8500.150 0.850 0.8500.150 0.150 0.8500.850 0.500 0.8500.500 0.500 0.5000.850 0.150 0.8500.850 0.150 0.850

0.850 0.850 0.8500.850 0.850 0.1500.850 0.850 0.8500.850 0.500 0.8500.850 0.150 0.8500.850 0.850 0.8500.150 0.850 0.850

0.850 0.150 0.8500.850 0.150 0.1500.850 0.150 0.1500.150 0.150 0.8500.850 0.500 0.8500.850 0.850 0.850



Table 3Selected learning activities and Web 2.0 tools for Moodle v2.2.

Learning activities Web 2.0 tools in Moodle v2.2

Video conferencing tools:A1 – View photo T1 – 2 Way Video ChatA2 – View video T2 – AMVONETRoomA3 – Create video T3 – BigBlueButonBNA4 – Storing puzzle T4 – OpenMeetingA5 – Make presentations T5 – SookoorooA6 – View demonstration of some procedure T6 – Video ConsultationA7 – Working at the whiteboard T7 – WebExA8 – Discuss in pairs Video viewing tools:A9 – Discuss in groups T8 – YouTubeA10 – Record and listen lectures T9 – LiveStreamingA11 – Read written feedback Picture repositories:

T10 – Flickr (public)T11 – Picasa web album

Audio recorders:


refers to ontology related issues found in the tools, such as theamount of help on ontology building and the high-level primitivesprovided (7 criteria). The last dimension is that of cooperation,which is used to evaluate the tool’s support for constructingontology by several people at different locations (6 criteria). Thisevaluation is based on Duineveld, Stoter, Weiden, Kenepa, andBenjamins (2000).

We consider all the weight equally important for the currentstudy. Therefore, according to normalisation requirement(Kurilovas & Dagiene, 2010), we have the equal weights 0.0476.After application of the experts’ additive utility function, we havegot the following results: Ontolingua: 14.35 ⁄ 0.0476 = 68.3%,WebOnto: 13.65 ⁄ 0.0476 = 64.97%, OntoSaurus: 14.00 ⁄ 0.0476 =66.64%, ODE: 10.50 ⁄ 0.0476 = 49.98%, and Protégé: 15.40 ⁄ 0.0476= 73.30%. Therefore, Protégé tool was selected for our domainontology development during this evaluation procedure.

T12 – BabeliumT13 – PoodLL Online

Game maker:T14 – Exabis Games

Questionnaire makers:T15 – WIRIS

Virtual classroom:T16 – WizIQ

3.2. Establishing interconnections between the sets of learning styles,preferred learning activities, and related Web 2.0 tools

Several learning activities were selected for Web 2.0 tools exist-ing in Moodle v2.2 (see Table 3). Sets of VARK learning styles andpreferred learning activities are interconnected according toFleming (2001):

Visual learners prefer maps, charts, graphs, diagrams, bro-chures, flow charts, highlighters, different colours, pictures, wordpictures, different spatial arrangements etc.

Aural learners like to explain new ideas to others, discusstopics with other students and their teachers, use a taperecorder, attend lectures and discussion groups, use stories andjokes etc.

Read/Write learners prefer lists, essays, reports, textbooks,definitions, printed hand-outs, readings, manuals, Web pages,feedback, taking notes etc.

Kinaesthetic learners like field trips, trial and error, doing thingsto understand them, laboratories, recipes and solutions toproblems, hands-on approaches, using their senses, collections ofsamples etc.

Interconnections between the sets of VARK learning styles, pre-ferred learning activities, and related Web 2.0 tools existing inMoodle v2.2 are presented in Fig. 1:

Fig. 1. Interconnections of VARK learning styl


Interconnections between the sets of VARK learning styles,preferred learning activities, and related Web 2.0 tools existing inMoodle v2.2 in Fig. 1 were established using sets portrait methodwhile analysing data in Table 3.

3.3. Creation of Web 2.0 tools ontology

There are few types of ontologies which have different roles, e.g.knowledge representation ontologies, generic ontologies, applica-tion ontologies etc. According to Mizoguchi (2003), ontology con-sists of (1) task ontology which characterises the computationalarchitecture of a knowledge-based system which performs a task,and (2) domain ontology which characterises knowledge of thedomain where the task is performed. Task means a problem solv-ing process like diagnosis, monitoring, scheduling, design etc. Theidea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems

es, learning activities and Web 2.0 tools.




might provide us with an effective methodology and vocabularyfor both analysing and synthesizing knowledge-based systems.Task ontology specifies the roles which are played by the domainobjects.

In the current study, we use ontology to interconnect preferredlearning activities with relevant Web 2.0 tools.

An ontology describes the concepts in the domain and also therelationships between those concepts. Different ontology lan-guages provide different facilities. The most recent developmentin standard ontology languages is OWL from the World WideWeb Consortium (W3C). Web Ontology Language (OWL) is a lan-guage developed by W3C. OWL is designed to make it a commonlanguage for ontology representation and is based on DAML + OIL.OWL is an extension of RDF Schema and also employs the triplemodel. Its design principle includes developing a standard lan-guage for ontology representation to enable Semantic Web, andhence extensibility, modifiability and interoperability are giventhe highest priority. At the same time, it tries to achieve a goodtrade-off between scalability and expressive power (Mizoguchi,2004). OWL ontologies have similar components to Protégé framebased ontologies. OWL classes are interpreted as sets that containindividuals. They are described using formal (mathematical)descriptions that state precisely the requirements for membershipof the class. Properties are binary relations on individuals, i.e. prop-erties link two individuals together, e.g. the property hasFunctionmight link the individual Tool to the individual ToolFunction. Prop-erties can have inverses, e.g. the inverse of hasName is isNameOf.

We have defined the following components on the domainontology: Concepts (Main Classes) (Fig. 2) and Relationshipsbetween Concepts (Properties) (Fig. 3):

We have used a standard Protégé plug-in – the DL Query tabwhich provides a powerful and easy-to-use feature for searching

Fig. 2. Concepts (main classes).

Fig. 3. Relationship between


a classified ontology. The query language (class expression) sup-ported by the plug-in is based on the Manchester OWL syntax, auser-friendly syntax for OWL DL that is fundamentally based oncollecting all information about a particular class, property, orindividual into a single construct, called a frame. Queries can beexecuted only on a classified ontology by reasoner built in Protégé.Some Query examples are presented in Figs. 4 and 5.

4. Discussion

The ontology presented is necessary for learners to semanti-cally search for suitable Web 2.0 tools while learning in VLEMoodle. Thus a user can learn effectively in the Web 2.0 tools-rich environment. Suitability of Web 2.0 tools depends on pre-ferred types of learning activities which in its turn depend onpreferred learning styles. The ontology is created using Protégétool that provides a graphical and interactive ontology-designand knowledge-base-development environment. The presentedontology is a very convenient tool for learners to quickly findsuitable Web 2.0 tools for their preferred learning activities inVLE by using semantic search engine. This possibility is neces-sary for particular learners to implement their preferred learningactivities using suitable Web 2.0 tools in VLEs and thus to opti-mise their learning paths.

Also, domain ontology enables knowledge reusability suitableboth for human beings and program modules and thus is suitablefor recommender systems that recommend items to users accord-ing to their preferences and characteristics of the required item.Therefore, the developed domain ontology can be used for recom-mender system that recommends Web 2.0 tools to learners basedon the preferred types of learning activities which in its turndepend on preferred learning styles.

However, Web 2.0 tools used in learning do not mean animprovement of the learning process. In literature, the need fordeeper conceptualisation of the relationships between Web 2.0tools and learning processes, clarification of how and by whatmeans these tools support learning is highlighted. In this paper,it is assumed that the enrichment of learning activities providedby Web 2.0 tools leads to learning process improvement, anddomain ontology provides the clear interconnections betweenthese tools and learning activities.

With the view to evaluate the proposed learning activities andWeb 2.0 tools interconnection approach, it must be at least imple-mented in the prototype of the recommender system or semanticsearch engine and then evaluated by learners or experts. This pro-totype can be integrated in VLE and then evaluated based on VLE

Concepts (properties).



Fig. 5. Entered quering for finding tool that has function show.

Fig. 4. Entered querying for finding tool that can show pictures.


quality in use model. It is the user’s view of the quality of a systemthat is measured in terms of the results of using the system (i.e.how people behave and whether they are successful in their tasks),rather than the properties of the system itself and the output canbe measured as effectiveness of the users. This evaluation isplanned in near future.

The research results are under implementation in iTEC (2014) –a four-year, pan-European research and development projectfocused on the design of the future classroom funded by EU 7FP.Both Web 2.0 tools (widgets) and VLEs (shells) incl. Moodle areused in iTEC to technologically improve innovative learning activ-ities. During iTEC, those learning activities are validated in over1000 European classes.

5. Conclusion

Systematic review performed in the presented study andapplication of triangular fuzzy numbers method show that the bestrelevant tool for creating Web 2.0 ontology is Protégé.

Sets of VARK learning styles, preferred learning activities andrelevant Web 2.0 tools existing in VLE Moodle were interconnectedusing sets portrait method.


Created ontology interconnects VARK learning styles, preferredlearning activities and relevant Web 2.0 tools in VLE Moodle.

The research results are used in iTEC – a four-year, pan-European R&D project focused on the design of the future class-room funded by EU 7FP. Learning activities proposed by iTECexperts will be interconnected with relevant Web 2.0 tools in Moo-dle, and the semantic search engine will be used by students toquickly find relevant collaborative technologies according to theirpreferences. The presented approach will be used in the next twoyears’ project cycles, and large scale validation will be performedto analyse scientific evidence on the impact of practical applicationof the proposed approach.

The ontology presented in the paper is absolutely novel, andthis new element makes the given work distinct from all the otherearlier works in the area.

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