338 IEEE TRANSACTIONS ON EDUCATION, VOL. 50, NO. 4, NOVEMBER 2007

Using Web-Based Teaching Interventions inComputer Science Courses

Dimitris Kanellopoulos, Evangelos Sakkopoulos, Miltiadis Lytras, and Athanasios Tsakalidis

Abstract—An open-source management system of Web-basedteaching interventions is presented that can be used in general forscience curriculum courses and especially for computer sciencecourses. The proposed solution is called System of TeachingIntelligent Interventions (STII) and facilitates the authoring,deployment, and evaluation of Web-based metaphors. Thispaper discusses the use of metaphors for assisting students tocomprehend the substance of cognitive concepts and their basicbackground. STII has been applied in a particular educationalscenario during the learning process of 228 students attending thecourse “computer applications.” The presentation of results froma pilot evaluation of two metaphors demonstrates the usefulnessof the system. The evaluation focuses on the impact of the twometaphors on student learning, and on detecting potential corre-lations between metaphors and students’ groups. STII evaluationconclusions strongly support that multimedia metaphors canbe used as an alternative instructional tool to assist students inconfronting their cognitive constraints and developing functionalunderstanding of curriculum courses.

Index Terms—Internet technologies, multimedia metaphors,open learning system, teaching intervention.

I. INTRODUCTION

SCIENCE learning is often considered by teachers and stu-dents to be a difficult pursuit. When students have to cope

with a difficult cognitive concept, they often get confused tryingto visualize its operation, and to understand the interrelations ofits conceptual units. Such confusion happens because studentsoften have difficulties in understanding a complicated cognitiveconcept. In scientific thinking, metaphors and terms constituteuseful tools in teaching interventions [1]. Presently, successfulmetaphors are frequently used in teaching to enable students tocreate analogies with their prior knowledge and construct theirconceptual forms. Metaphors can be used also in the develop-ment of theories in the social sciences [2] since they provide aconvenient means by which to create a taxonomy; the first step istowards description, then prediction, and finally understanding.

In science learning, cognitive concepts that are complex instructure and operation can be understood by using successfulmetaphors. For example, when describing a complicated ma-chine, one often identifies a central part, and refers to it as theheart of the machine. When an analogy from one area (e.g., from

Manuscript received March 24, 2006; revised August 1, 2007.D. Kannellopoulos is with the Department of Mathematics, University of Pa-

tras, GR-26500 Patras, Greece.E. Sakkopoulos and A. Tsakalidis are with the Department of Computer Engi-

neering and Informatics, University of Patras, GR-26500 Patras, Greece (e-mail:sakkopul@cti.gr).

M. Lytras is with the Research Academic Computer Technology Institute,GR-26500 Patras, Greece.

Digital Object Identifier 10.1109/TE.2007.906906

biology) is used in a different area (e.g., for mechanical ma-chines) to describe some concept/term, it is called a metaphor.

Even though several open-source e-learning solutions areavailable, currently, to the authors’ best knowledge, no previoussolution serves as a hosting platform to both implement andevaluate metaphors on the Web. In the following, the Systemof Teaching Intelligent Interventions (STII) is introducedto manage Web-based metaphors for cognitive concepts ofcurriculum courses. The STII is a novel, Web-based learningsystem implemented via the open-source software (OSS)model. A proposed methodology for the development and eval-uation of multimedia-based metaphors is also presented. TheSTII has already been used in higher education science coursesand has undergone evaluation with the participation of thestudents who attended the course “computer applications” atthe Technological Educational Institute (TEI), Patras, Greece.

The rest of the paper is organized as follows. Section IIpresents related work concerning metaphor-based instructionsystems and open-source e-learning solutions. Section III dis-cusses how metaphors are modeled on the Web and introduces

, , and to quantify theperformance of a metaphor. Section IV presents STII imple-mentation. Section V analyzes the experimental findings, whileSection VI concludes the paper and discusses future work.

II. RELATED WORK

A. Metaphor-Based Instruction Models

Teaching intervention is defined as the point at which astudent has struggled so that the teacher must assist the studentindividually, usually with one-to-one tutoring. In particular,teaching interventions based on metaphors are very useful fortwo reasons: 1) they offer to students opportunities to be ex-pressive and 2) they enable tutors to create links between whatstudents understand and the new conceptual units of a cognitiveconcept that tutors want students to learn. Well-organizedteaching interventions should be tried under real conditionswith the practical problems and the possible solutions beingrecorded.

In learning and teaching, various issues must be considered:1) the investigation of students’ thoughts; 2) the interpretation oftheir alternative perceptions for a cognitive concept; and 3) thedevelopment of conceptual tools and successful teaching inter-ventions for students. A crucial challenge remains to learn howthe students comprehend complicated concepts and how theycapture scientific knowledge forms.

A conceptual metaphor is defined as the mechanism by whichabstract concepts are understood and comprehended about interms of physically-based concepts [3]. A metaphor is used tocommunicate the characteristics of a difficult-to-conceptualize

0018-9359/$25.00 © 2007 IEEE

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concept, i.e., the target, in terms of another, more accessibleand familiar concept in the addressee’s prior knowledge, i.e.,the source. Frequently, conventional instruction is ineffective inworking with misconceptions. For example, students’ alterna-tive conceptions for a complex cognitive concept are consid-ered not as easily affected by traditional instructional methods.In curriculum courses, transforming ideas and correcting defectsof students’ knowledge is sometimes beyond the reach of the tra-ditional teaching approaches because the students’ perception,often ignored, is possibly different from that of the teacher [4].

Tobin and Tippins [5] have mentioned “metaphors appeal asways of beginning conversations about teaching and learningscience and making it easier to be reflective in and in practice”and “teaching and learning described in terms of metaphorswhich can then be foci for analyses.” Most metaphors (usedin teaching interventions) are not Web-based and their manage-ment is a time-overloaded process. In addition, management en-vironments for Web-based teaching interventions or metaphorshave not been developed yet. However, a variety of computerapplications have been developed and used in teaching, such asmultimedia [6].

B. Open-Source E-Learning Systems

Instruction tools can be based on the OSS model, which offerspotential benefits. Open source refers to software’s source codethat is freely available to anyone who wishes to extend, modify,and improve the code. Indicative open-source organizations thatare involved in helping tutors build their e-learning courses areClaroline, EduZope, and Moodle. The OSS model encompassesa set of principles and values that ensure the integrity of OSS.

E-learning applications’ initiatives are adopting the OSSmodel for delivering education online. The benefits of the OSSmodel are increased quality, greater stability, superior perfor-mance, and improved functionality. Reduced vendor reliance,reusability, reduced costs, validation of security, reliability, andquick solutions to problems are among other benefits that theOSS model can offer. An evaluation of open-source e-learningsolutions is presented in [7], focusing on personalization.

III. A PERFORMANCE MODEL FOR METAPHORS ON THE WEB

Students’ access to the Web poses tremendous challengesfor teaching and learning. The Web offers employment of newteaching methods in curriculum courses. Multimedia offer in-creased modularity, a very important aspect in the developmentand maintenance of Web-based metaphors. In the proposedframework, a teaching intervention is based on a conceptualmetaphor between a cognitive concept (term) and its analo-gies. Every term and its analogies contain multimedia data.Following, novel metrics for the performance of metaphors inWeb-based e-learning platforms are introduced.

Hereafter, the notions of and for metaphorson the Web are introduced in (1) and (2).

(1)

(2)

In (1) and (2), term_id is a cognitive concept (term); concep-tual_unit is a conceptual unit corresponding to the former term;and analogy_id is a metaphor of the former term.

The performance of a metaphor is the novel

metric vector given by (3)

(3)

The definition and analysis of the right hand parameters of(3) follow. During the investigation of a student’s thought, thestudent relates the conceptual units of the withthe analogy units of the metaphor. In (4), is the numberof analogy or conceptual units, and is the coefficient weightfor the . If all coefficients areequals, then and is the total numberof successful relations made by student .

(4)

If the student relates successfully the ,then (succeed), otherwise (failed). The first

element of the vector is the investigation of all students’thoughts given by (5), where is the number of all students.

(5)

The second element of the vector is given in (6), whereis the number of similarities existing in with

and being the total number

of presented similarities. The third element of the vector isshown in (7), where is the number of differences existingin with andbeing the total number of differences.

(6)

(7)

In the proposed framework, a course consists of a set of terms.A term consists of a set of conceptual units; and an analogyconsists of a set of analogy units. Concepts and metaphors helpstudents to comprehend completely the substance of a cognitiveconcept.

IV. THE SYSTEM OF TEACHING INTELLIGENT

INTERVENTIONS

Section IV presents the STII architectural design providingtechnological information about the system. Moreover, func-tional specifications and their respective modules are discussed.

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Fig. 1. The STII architecture.

A. System Presentation and Architecture

The STII provides a hosting and evaluation platform forWeb-based metaphors for curriculum courses. The STII com-prises a general Web-authoring environment of metaphors forcurriculum courses. The proposed system is aspiring to pro-vide a functional and self-evaluated authoring environment tosciences’ authors, who might then write their own metaphors.Every teaching intervention is based on a proper metaphor fora concept/term, while the term and its analogous metaphorsare expressed via multimedia data. The STII is based on theWeb to avoid the implementations of different user interfaces(UIs) for each new platform or operating system. Each autho-rized student can be connected to the STII Web server and beinvolved in a Web-based teaching intervention. Moreover, theSTII constitutes a delivery platform of multimedia educationalcontent. STII is designed to provide high levels of portabilityand reusability of the educational content and independencyfrom terms of pedagogy, content, culture, and infrastructure.

Fig. 1 depicts the STII architectural components:1) the educational model and logic, which includes naviga-

tion, student investigation, metaphor presentation, exami-nation, and evaluation processes;

2) the multimedia data repository, which includes the reusableeducational objects;

3) users roles and user profiles, which allow personalization,based on registration input and implicit observation fromuser logs;

4) the data storage system that concludes the data layer ofSTII.

Finally, additional operations are provided for managementand statistics.

B. Technical and Functional Specifications

The STII environment is an OSS environment, quite light-weight, which can be hosted on a typical PC Server (during eval-

uation, STII was implemented on a single Pentium IV, 2.8 GHz,1-GB RAM). STII has been based on different categories ofopen-source tools (Apache, PHP, MySQL) that are freely avail-able online. As a consequence, software licensing costs are di-minished, and future enhancements are possible.

The functional specifications included three levels of dif-ferent roles: 1) the student; 2) the instructor; and 3) theadministrator. For each role player, a certain set of operationsand sections are available. The operational specifications im-plement the functional requirements imposed by an applicationmanagement environment. In particular the STII includes thefollowing.

• Navigation: STII provides navigation among the avail-able courses, concepts/terms, and metaphors. After thestudent’s authentication, the STII introduces him/herinto a panel to select an educational topic and one of theavailable metaphors.

• Investigation of student’s initial perspective: STIIpresents the conceptual units of the selected term in arandomized manner. The STII asks the student to relatethe conceptual units of the term (e.g., computer) to theunits of the analogy (e.g., factory or confectionary), to de-termine resemblances and differences between them. Forexample, selecting the term “computer,” conceptual unitscould constitute CPU, memory, bus, storage devices, I/Odevices, etc. Units are presented as sets of educational ob-jects using a multimedia content repository infrastructure.Each unit may comprise a series of multimedia objects,such as videos, images, sounds, and educational passages.

• Adaptive presentation: The STII presents the resem-blances and differences between the conceptual andanalogous units. Each conceptual unit of the term isrelating to an analogous unit (e.g., the conceptual unit,“storage devices,” corresponds to the analogous unit,“storerooms-warehouses”). The presentation is based on

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the student’s profile, which is based on his/her directchoices upon registration, and by implicit observations ofthe multimedia objects that attract the student’s attention.The profile is based on the multilayered user profile intro-duced in [8].

• Postcourse examination: The student is asked to answer aquestionnaire that belongs to the analogous context. In par-ticular, the questions concerning the analogous-metaphorcorrespond to univocally questions in the term context. Forexample, in the term context, a question might be, “Howwould the computer operate without its storage devices?”A potential answer might be, “The data in the computercould not be stored permanently.”

• Metaphor evaluation: All users can evaluate eachmetaphor in the scale, [ 2, 2], according to the fol-lowing criteria: 1) the organization time of metaphor;2) the display content of the metaphor; 3) the effectivenessof exploratory activity; 4) the teaching role; 5) the levelof personal pastime in the evaluation process; 6) the totalview and operation of the selected metaphor; and 7) thesentimental and cognitive results. The use of evaluationservices for Web-based metaphors will involve betterintegration of evaluation, improved reliability, and savingtime from the side of instructors/evaluators.

Management functions for the instructor and administratorfacilitate the overall environment. STII includes: 1) user pro-files’ management; 2) dynamic management of courses, terms,metaphors, and multimedia units; 3) dynamic management ofa postcourse examination process; 4) statistical analysis of thestudent thought investigation, examination, and evaluation re-sults per term-analogous pair; 5) registration procedure; and6) basic data management facilities (i.e., multimedia resourcemanipulation).

V. STII USAGE AND METAPHORS EVALUATION

A. Case Study and Research Method

Section IV presents the STII evaluation which took place atthe TEI of Patras. The human subjects who participated in theresearch were 228 students, 20–24 years old, enrolled in thesecond year of the Tourism Management Department of TEI ofPatras. All human subjects were attending the course ComputerApplications that forms part of the computer science syllabus.The target set was the conceptualization of the concept/term“computer” by using two metaphors, “factory” and “confec-tionary.” The concept/term and metaphors are modeled usingconceptual and analogous units, respectively. Each unit is com-posed of multimedia data.

STII educational scenario included the following steps.1) Each student registers to STII and logins.2) The student selects a course (e.g., computer applications),

a concept/term (e.g., computer), and a metaphor (e.g., fac-tory or confectionary).

3) Next, the student matches between conceptual andanalogous units, which represent the concept/term andmetaphors, respectively.

Fig. 2. Students’ responses for both metaphors related to gender.

4) Following, the STII presents similarities and differencesbetween each conceptual and analogous unit pair.

5) STII presents the “correct” matching relationships amongthe conceptual and analogous units.

6) Finally, each student evaluates the selected metaphor.Human subjects represented a wide range of achievement

levels and were coming from a variety of socioeconomic back-grounds from 23 different regions of the country (Greece). Manymore females (183) were enrolled in the research than males(45). Most of the students (63%) had computer skills and workexperience (64%). The following foreign languages skills distri-bution was found: 2% (no foreign languages), 29% (one foreignlanguage), 61% (two foreign languages), 6% (three foreign lan-guages), and 2% (more than three foreign languages).

The case study investigates the effects of two metaphorson students’ learning a basic computer science concept (com-puter). Initial user registration procedure consists of creatingstudents’ profiles by recording a number of characteristics(e.g., sex, locality, computer skills, and foreign languages).Furthermore, user activity is recorded to assist the STII per-sonalization based on each student’s learning curve. Implicituser logins assist in two directions: 1) to evaluate the impactof the available metaphors on student learning and 2) to detectpotential correlations between metaphors and student usergroups (e.g., students from industrialized areas may preferthe metaphor “factory”). The STII offers mechanisms thatfacilitate evaluation of the metaphors using the proposed vector

model and the provided personalization of the system forspecific user groups. The proposed system includes a statisticalmodule that allows correspondence analysis (CA) [9] of theavailable characteristics and metaphors. Final interpretation ofthe analysis is performed by the instructor.

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Fig. 3. The symmetric plot of the correspondence analysis.

The analysis of the research data included two distinct phasesor levels of statistical analysis. The first phase is based on thestatistical description of the data. The second one involves theuse of CA.

B. Descriptive Analysis Findings

The goal of the descriptive analysis was to investigate stu-dents’ thoughts. During descriptive analysis, the efficient andinefficient students’ responses for both metaphors, and

, were recorded. Efficient responses are the cor-rect answers (matches between conceptual and analogous units)given during the investigative stage of STII. Fig. 2 shows ef-ficient and inefficient students’ responses for both metaphors( and ) with respect to gender. In Fig. 2,the conceptual units of the term computer are located in axis(x).During the students’ thoughts investigation, male students gavemore efficient responses concerning the conceptual units “CPU”and “IO devices” and “bus” than the female students. However,female students gave more efficient responses for the “storagedevices” and “memory” conceptual units.

Applying the proposed vector model (Section IV—Systempresentation and architecture) for the two metaphors, the cor-

responding vectors are: and

. Both vectors have ap-proximately the same and ( 14).However, the metaphor has a highervalue (0.737) than the confectionary value(0.719). Therefore, according to the proposed model using thevector , the metaphor can be characterized as mosteffective.

The analysis is automatically generated by STII and allowsthe administrator to identify the effectiveness of all metaphorsand to compare metaphors by conclusions regarding theirperformance.

C. Correspondence Analysis Findings

The STII administrator can use the evaluation results todetermine which metaphors are best suited for different usergroups and to further customize the metaphors based on eachuser group’s specific needs. Particularly, for the two metaphors,CA can be performed including the static characteristics inthe user profiles (e.g., sex, user geographical area, level ofcomputer skills, and knowledge level of foreign languages).As a result, the STII administrator/tutor determines which ofthe two metaphors is best suited for each user group. Further-more, the STII tutor can conclude which users’ groups haveprovided similar responses during the student thought investi-gation. CA output plots (Fig. 3) represent the correlations ofuser characteristics (included in the user profile) to the userresponses given during the matching of the conceptual units(e.g., computer units) with the analogous units of a metaphor(e.g., factory/confectionary units). The interpretation of the CAplots cannot be performed automatically. The STII tutor caninterpret manually each symmetric plot of every CA.

Using a variance method, students’ efficient and inefficientresponses and their correlations can be derived. Next, how tu-tors can interpret such a plot and utilize it to support teaching ispresented. To identify correlations between the responses givenin the two interventions (factory and confectionary) by studentsin TEI of Patras, a CA has been performed. The used contin-gency matrix includes the efficiency of the responses in eachmetaphor according to the students’ region.

As a result from the CA, the first two dimensions (F1 andF2) are kept summarizing cumulatively the 90.7% of the totalinertia (Table I), that is, the relative frequency values that canbe reconstructed from two dimensions can reproduce 90.7% ofthe inertia for the used contingency matrix. Consequently, theanalysis is run with two dimensions only, and the results containonly two axes. The results of the CA may be seen in Fig. 3. The

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TABLE IEIGENVALUES AND VARIANCE PERCENTAGES

representations of row-points and column-points are drawn oneabove the other on the same graph, at the same scale.

To interpret the graph, the tutor should consider only the po-sitions relative to an axis of the points belonging to the samedimension. One tutor may interpret only the position of a re-gion relative to that of another region. Two such points closeon the graph will have a similar profile. For example, studentscoming from regions “Messinia,” “Lakonia,” and “Other” (non-nationals) have answered to the intervention questions in a sim-ilar proportion. The proximity of two points from two differentclouds in a CA cannot be safely interpreted. However, one mayinterpret the angle between a row point and a column point(taking the origin as the summit). The following are examples:

1) When the angle between the points is acute , thetwo characteristics for which the points stand are corre-lated. In particular, (Fig. 3), the student region points thatform acute angles with the FactoryR point (i.e., efficient-right responses in the “factory” metaphor) suggest that the“factory” metaphor is more successful for students comingfrom the respective regions. This observation also appliesto the “confectionary” metaphor or any metaphor underanalysis accordingly. For example, the acute angle betweenthe points FactoryR and regions “Kefallonia” and “Athens”is about 15 and 65 , respectively, because 94% and 76%of the corresponding students provided correct responses.The respective students answered correctly against approx-imately 72% in the whole population. Therefore, in regions“Kefallonia” and “Athens” the over-representation of “ef-ficient responses in the factory metaphor” appears in thegraph.

2) When the angle is obtuse, the points are negatively corre-lated. In particular, the student region points that form anobtuse angle with ConfectionaryR (i.e., correct responsesin the “confectionary” metaphor) suggest that the “confec-tionary” metaphor is not appropriate for students comingfrom the respective regions. For example, an obtuse angleexists between ConfectionaryR and “Zakynthos” island(almost 180 ). No inefficient responses about the “con-fectionary” metaphor from “Zakynthos” island studentsexist. Against 72% of the total population, the conclusionis that the students are underrepresented.

3) Finally, with a right angle, the points do not interact, asbetween “Achaia” and ConfectionaryR (i.e., correct re-sponses in the “confectionary” metaphor). Actually, 72%answered efficiently in “Achaia,” about the same propor-tion as in the total population ( 72%).

VI. CONCLUSION AND FUTURE WORK

An open-source system (STII) for modeling metaphors inWeb-based instruction is proposed. The development and evalu-ation methodology of two Web-based metaphors (“factory” and“confectionary”) is also presented for a particular educationalscenario (teaching computer science for the TEI of Patras).

The STII provides a powerful framework that integrates cre-ation, management, and evaluation of Web-based metaphors,implemented according to the OSS model. STII is a metaphorlearning management system and may host different interven-tions for teaching different subjects. STII supports hosting mul-tiple courses and metaphors.

The evaluation results from STII’s usage in the computerscience course at the TEI of Patras indicate that multimediametaphors can be used as an alternative instructional tool to as-sist students in confronting their cognitive constraints and de-veloping functional understanding of curriculum courses. STIIcan support multiple courses and metaphors.

To know more about Web-based metaphors’ usage patternsand the potential of computer log analysis, one may includethese future research goals: 1) investigation of the usage rate ofthe content system, a general variable for all types of informa-tion uploaded into STII; 2) examination of the individual differ-ences among students regarding content consumption, utilizingcluster analysis techniques; 3) investigation of the way in whichcontent items affect the presentation and consumption of con-tent in the STII; and 4) the use of ontologies to model terms andmetaphors will be considered.

REFERENCES

[1] J. Ogborn, C. Mariani, and I. Martins, “Metaphorical understandingsof scientific ideas,” Int. J. Sci. Educ., vol. 18, no. 6, pp. 631–652, 1996.

[2] G. Hartzell, “The metaphor is the message,” School Libr. J., vol. 48,no. 6, p. 33, 2004.

[3] G. Lakoff and M. Johnson, Philosophy in the Flesh: The EmbodiedMind and Its Challenge to Western Thought. New York: Basic Books,1999.

[4] L. C. McDermott, “How we teach and how students learn—A mis-match?,” Amer. J. Phys., vol. 61, pp. 295–298, 1993.

[5] K. Tobin and D. Tippins, “Metaphors as seeds for conceptual changeand the improvement of science teaching,” Sci. Teach. Educ., vol. 80,no. 6, pp. 711–730, 1996.

[6] M. E. Crosby and M. K. Iding, “The influence of a multimedia physicstutor and user differences on the development of scientific knowledge,”Comput. Educ., vol. 29, pp. 127–136, 1997.

[7] S. Graf and B. List, “An evaluation of open source e-learning plat-forms stressing adaptation issues,” in Proc. IEEE Int. Conf. AdvancedLearning Technologies, Kaohsiung, Taiwan, Jul. 2005, pp. 163–165.

[8] J. Garofalakis, E. Sakkopoulos, S. Sirmakessis, and A. Tsakalidis,“Integrating adaptive techniques into virtual university learning envi-ronment,” in Proc. IEEE Int. Conf. Advanced Learning Technologies,Kazan, Russia, Sep. 2002, pp. 28–33.

[9] M. J. Greenacre, Correspondence Analysis in Practice. London,U.K.: Academic, 1993.

Dimitris Kanellopoulos received the Ph.D. degree in electrical and computerengineering from the University of Patras, Patras, Greece, in 1998.

He is currently a Member of the Educational Software Development Labo-ratory, Department of Mathematics, University of Patras. His research interestsinclude multimedia communications, Web engineering, e-learning, and knowl-edge representation. He has authored more than 40 papers in international jour-nals and conferences.

Dr. Kanellopoulos serves as an Editor for some academic journals.

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Evangelos Sakkopoulos received the Computer Engineering and Informaticsdiploma degree, the M.Sc. degree in computer science and technology, and thePh.D. degree from the Department of Computer Engineering and Informatics,University of Patras, Patras, Greece, in 2001, 2003, and 2005, respectively.

He is currently a Research Associate of Research Unit 5 Internet and Multi-media Technologies at the Research Academic Computer Technology Institute(RACTI), Patras, Greece. His research interests include Web engineering, Webservices, e-learning, and Internet technologies. He has authored more than 40papers in international journals and conferences.

Miltiadis Lytras received the Informatics diploma, M.B.A., and Ph.D. degreesfrom the Department of Management Science and Technology of the AthensUniversity of Economics and Business (AUEB), Athens, Greece, in 1995, 1998,and 2004, respectively.

He is currently a Research Associate of Research Unit 5 Internet and Multi-media Technologies at the Research Academic Computer Technology Institute(RACTI), Patras, Greece. His research focuses on e-learning and knowledgemanagement.

Dr. Lytras is the Founder of the Semantic Web and Information Systems Spe-cial Interest Group in the Association for Information Systems.

Athanasios Tsakalidis received the Ph.D. degree from the University of Saar-land, Saarbuecken, Germany, in 1983.

He is currently a Full Professor in the Department of Computer Engineeringand Informatics, University of Patras, Patras, Greece. He is also Vice-Directorand R&D Coordinator of Research Academic Computer Technology Institute(RACTI), Patras, Greece. His research interests include data structures, compu-tational geometry, GIS, medical informatics, multimedia, information retrieval,and bioinformatics.

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