Open Education Ecosystems, learning analytics and supportive software system framework
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Open Education Ecosystems, learning analytics and supportive software system framework



At present there is a clear absence of technical solutions that would allow for education design and provision across technologies. Even in the case of supportive licensing for underlying open ...

At present there is a clear absence of technical solutions that would allow for education design and provision across technologies. Even in the case of supportive licensing for underlying open educational resources, and the access opportunity to educational communities, the disconnection of the respective technical solutions and environments has turned out so far to be a serious challenge. As a matter of fact current technological solutions are typically not designed or intended to allow for education across higher education institutions, nor to allow all type of learners to learn at any institution of their choice, nor to engage with students from such institutions, nor to obtain support from such institutions. Commercial approaches like Amazon for the retail sector or Sourceforge for developer community do provide some insights on how Open Education Ecosystems might be perceived. Amazon and Sourceforge both offer examples that bring together competing commercial enterprises within their environments, which in the traditional formal higher education domain does not exist. Thus there is the need to advance knowledge in such new forms of collaboration in the education sector and to contribute towards specifications that emerging Open Education Ecosystems would need to meet.



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Open Education Ecosystems, learning analytics and supportive software system framework Open Education Ecosystems, learning analytics and supportive software system framework Document Transcript

  • Open Education Ecosystems,learning analytics and supportive software system framework Andreas Meiszner, PhD United Nations University UNU-MERIT | The Netherlands | Pantelis Papadopoulos, PhD Aristotle University of Thessaloniki | Greece | January, 2012 …with contributions from David Jacovkis; Free Knowledge Institute, NL Elmar Husmann; European Learning Industry Group, EU Imed Hammouda; Tampere University of Technology, FI Ioannis Stamelos; Aristotle University of Thessaloniki, GR Itana Maria de Souza Gimenes; Universidade Estadual de Maringá, BR José Janssen; The Open University, NL Leonor Barroca; The Open University, UK Patrick McAndrew; The Open University, UK Peter B. Sloep; The Open University, NL Ruediger Glott; United Nations University UNU-MERIT, NL Veerendra Deverashetty; Tampere University of Technology, FI Wouter Tebbens; Free Knowledge Institute, NL
  • Note:This conceptualized software system framework for Open Education Ecosystems and learninganalytics as presented within this document has been initially prepared within the wider contextof a research project funding proposal. For this reason perhaps not all information providedwithin this document are self-explanatory or fully comprehensive; though an effort has beenmade to leverage the initially developed key information into this document and to present themwithin a coherent narrative. Further information on the initially developed research projectconcept and supplement information are available upon request.Copyright Notice:This work is published under a Creative Commons License Attribution-Noncommercial-ShareAlike 3.0 Unported. • Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). • Noncommercial — You may not use this work for commercial purposes. • Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one.Version Information: January 17th 2012 ii
  • AbstractOpen Education (OE) potentially allows for the systematic bringing together of traditional formalhigher education offers from across higher education institutions with practice and authentic learningopportunities within real-life context environments that Web 2.0 provides. OE thus in principle allowsfor the scalable collections of large sets of learning pathways, the outcomes of learning, and thecontexts in which learning takes place from across Higher Education Institutions, the underlyingacademic subjects, and associated authentic real-life context environments. This provides the potentialto truly enable personalization, discovery, collaboration, and intelligent ICT-based guidance.In the following an OE software system framework will be presented that is aimed to support learningwith components to maintain profiles, scrape data, assess performance and offer tools and pathways tothe learner. As such, the framework has been conceptualized by the following three main objectives:1. Allow learners to understand the underlying theoretical foundations of subjects by providing them with opportunities to experiment within real-life context environments that are authentic, live, often real-time, and complex. Such real-life contexts could be for example (1) ‘Open Source Software Development’, (2) ‘Agile Software Development’, or (3) ‘Open Data Initiatives’.2. Identify the ways that alternative pathways can support learning, in particular within ill-structured domains. These pathways will recognize the different cognitive demands on learners required to understand theoretical foundations of subjects, but apply them in areas where approaches are less specified and knowledge may even be contested. The objective is to identify how technology can efficiently support learning pathways that enable learners to engage with authentic real-life context learning opportunities with support from social and content-based sources to bridge the performance gap between apprentices and practitioners.3. Provide learners with effective, personalized, ICT-based guidance by combining the theoretical foundations of subjects within such real-life contexts through the application of Open Education approaches.At present there is a clear absence of technical solutions that would allow for education design andprovision across technologies. Even in the case of supportive licensing for underlying openeducational resources, and the access opportunity to educational communities, the disconnection of therespective technical solutions and environments has turned out so far to be a serious challenge. As amatter of fact current technological solutions are typically not designed or intended to allow foreducation across higher education institutions, nor to allow all type of learners to learn at anyinstitution of their choice, nor to engage with students from such institutions, nor to obtain supportfrom such institutions. Commercial approaches like Amazon for the retail sector or Sourceforge fordeveloper community do provide some insights on how Open Education Ecosystems might beperceived. Amazon and Sourceforge both offer examples that bring together competing commercialenterprises within their environments, which in the traditional formal higher education domain doesnot exist. Thus there is the need to advance knowledge in such new forms of collaboration in theeducation sector and to contribute towards specifications that emerging Open Education Ecosystemswould need to meet. a
  • Table of ContentGlossary....................................................................................................................................................1  1.   Introduction.......................................................................................................................................2  2.   The Open Education context and potential for learning analytics ....................................................3  3.   Conceptualized supportive Open Education software system framework........................................5  4.   OE software system framework modules and functions...................................................................6  4.1.   Profiler module depiction...............................................................................................................6  4.2.   Scraper module depiction...............................................................................................................8  4.3.   Scraper Widgets depiction ...........................................................................................................10  4.3.1.   System Based Scraper Widgets.................................................................................................12  4.3.2.   Browser Based Scraper Widgets...............................................................................................12  4.4.   Assessor module depiction...........................................................................................................12  4.5.   Pathway Viewer & Scout module depiction ................................................................................13  4.5.1.   The Pathway Viewer.................................................................................................................13  4.5.2.   The Scout part ...........................................................................................................................13  4.6.   Tutor module depiction................................................................................................................15  4.7.   Ontology depiction.......................................................................................................................17  5.   Advances and Innovations (A&I) of the proposed system .............................................................18  5.1.   A&I in the Open Education Domain............................................................................................18  5.2.   A&I on Learner guidance through complex Open Education Ecosystems .................................19  5.3.   A&I on Learner Modelling ..........................................................................................................19  5.4.   A&I on Instruction and Assessment ............................................................................................20  5.5.   A&I in the field of Personal Learning Environments ..................................................................20  5.6.   A&I in Automated Planning for Curricula Synthesis ..................................................................21  5.7.   A&I in Ontologies and TEL.........................................................................................................22  6.   References.......................................................................................................................................23   b
  • Glossary  Open Courses [OC] – in contrast to traditional formal education courses, which will typically limit accessto registered students, OC allow for participation of third parties, such as fellow students and educators;free learners outside of formal education; practitioners and enterprises as producers, consumers orcollaborators; or established virtual communities of practice. The types of participation opportunitiesprovided to such third parties might vary and could consist for example of: ‘open to read’, ‘open toparticipate’, ‘open to change’, or ‘open to add’, ‘open to re-use’, etc.Open Education [OE] – the free and open access to, the usage of and the right to modify and re-usedigital open educational resources and digital educational tools, and the free and open access to the relatedvirtual educational communities and environments, in order to learn, teach, exchange or advanceknowledge in a collaborative and interactive way.Open Education Ecosystem [OEE] – the wider socio-technological system that might consist of a numberof OEFs and the various resources of such OEFs, including the stakeholders that are populating thisecosystem. OEE can be understood as the practical response to theoretic concepts that have been putforward by the work of Brown and Adler (2008) on ‘Open Participatory Learning Ecosystems’, whichemphasizes the emergent interconnections of educational resources and lightweight, bottom-up, emergentsocio-technical structures.Open Education Framework [OEF] – an organizational framework, which is embedded within atechnological system (such as OEE), that allows for the design and delivery of Open Education. The OEFincludes and considers the various OE module parts, such as ‘Open Content’, ‘Open Degrees’, ‘OpenAssessment’, ‘Open Learning’, ‘Open Tutoring’, ‘Open Technology’ and ‘Open Communities’. The OEFalso tangles organizational aspects with regards to the interplay of formal traditional higher educationacross institutions and real-life context environments. An example of OEF is the EU funded openSEframework that brings together courses from across traditional formal higher education institutions andfrom real-life context environments.Open Educational Resources [OER] – “are digital materials that can be re-used for teaching, learning,research and more, made available for free through open licenses, which allow uses of the materials thatwould not be easily permitted under copyright alone” (Source of definition: context environments – Real-life context environments are existing online environments thatallow students to experiment and to apply their knowledge within a real-life context. Those environmentsare real world, live and often real-time, and they are complex. Real-life context environments could be forexample (1) ‘Open Source Software development’, (2) ‘Agile Software Development’, and (3) ‘Open DataInitiatives’. 1
  • 1. Introduction  Over the past years the traditional formal education domain has been subject to a process of opening upresulting in an ever-blurring border between the formal and the informal and allowing traditional formaleducation to take advantage of the opportunities that participatory Web 2.0 provides (Meiszner, 2010,Weller & Meiszner 2008). Such blurring of borders can be seen both in the use of informal approacheswithin formal education and release of formal content for less formal use. On the one hand the traditionalformal education domain has been taking advantage of the practicing and authentic learning opportunitiesthat Web 2.0 based real-life context environments provide (Meiszner, 2010). For example using OpenSource Software development communities as real-life context environments to support traditional formalhigher education offers (Stamelos, 2008). Mozilla Education, OpenOffice Education, or the ApacheMentored Internship program1 are just three of such practitioner driven attempts, with openSE andict@innovation2 the academic driven counterparts, and all of them aim to provide learners with real-lifepracticing or internship opportunities alongside their academic subjects. On the other hand also the formaleducation sector has been advancing and further ‘opening up’ itself. The past years have been marked bythe emergence of ‘Open Courses’ (Meiszner, 2010), such as ‘OpenEd Syllabus’ (US, 2007), ‘CCKConnectivism Course’ (CA, 2008), ‘openED – Business and Management in a Web 2.0 World’ (EU,2009), or Stanford’s ‘Introduction to Artificial Intelligence’ (US, 2011)3. All of these such ‘Open Courses’seem to experiment with a range of different educational approaches, tend to promote different levels ofopenness, incorporate different sets of free and open tools and learning resources, and – to a varyingdegree – mix the formal with the informal; bringing together the different stakeholders to be found on theweb (Meiszner, 2010). These aspects taken together offer the potential to systematically bring togethertraditional formal higher education offers and theoretic subjects, and from across higher educationinstitutions, with practicing and authentic learning opportunities within real-life context environments thatWeb 2.0 provides. The recent developments considered above indicate an immense potential to bettersupport learners, but they also change the context of what is to be understood as traditional formal highereducation, and what current or future technologies might need to support. This changed context brings upa number of questions that must be reflected upon once again, and as will be detailed within the followingsection.1 For all of the foregoing please see: See and See:,,, 2
  • 2. The Open Education context and potential for learning analyticsICT based global and collaborative Open Education (OE) approaches in traditional formal highereducation in general, and through Open Education Frameworks in particular, are a new and emergingdomain that hold the potential to better support students to understand and construct their personalconceptual knowledge and meaning of scientific subjects, to take learners through the complexity oftraditional formal higher education subjects, activating and feeding at the same time the learners’ curiosityand reasoning, and allowing the creative applications of their theoretical knowledge in practical or real lifesituations. This approach has potential in particular to support learners within the Science, Technologyand Mathematics subjects. Open Education (OE) allows for systematic integration of traditional formalhigher education offers from across higher education institutions and subjects with practicing andauthentic learning opportunities within real-life context environments. This can facilitate guiding learnersthrough the complexity of subjects and allow for linking the theory of a subject to practice and authenticlearning opportunities. Moreover, such OE approaches support the creative application of the theory ofsubjects within real-life context environments. Earlier works, such as the EU funded openSE project, haveshown that OE approaches enabled Computer Science (CS) Software Engineering students to engage andlearn within real life and authentic learning activities within Open Source Software projects. OEapproaches are however not limited to the Open Source Software case and could be generally appliedwithin subjects and whenever real-life context environments exist. In the CS case further examples such asagile software development (e.g. or large-scale open data initiatives (e.g.Rotterdam City Open Data Initiative - are seen to be equally suitable real-life context environments. These have the characteristics to support learning that is situated in a real-lifecontext – and under real life conditions. They are all real world, live and with real-time dependencies, andthey are complex, therefore providing authentic learning opportunities. Just as importantly from aneducational perspective; allowing learners to engage within such learning opportunities, either by takingpart in analyzing activities or to create concrete and less abstract applications of concepts, provides theneed and opportunity for learners to make sense of theoretic subjects. OE and the use of real-life contextenvironments thus encourages students to explore, to find out, to apply their theoretical knowledge inpractice, or to gain key and soft skills that are difficult to impart within a traditional formal educationalcontext (Wilson, McAndrew & Meiszner, 2011; Meiszner 2011; Meiszner, 2010; Meiszner, Moustaka &Stamelos, 2009).The learning pathways of the students and what they have learned and created both in traditional formalhigher education and in real-life context environments however remains largely invisible and untraced.This means that supporting learners from the academic subject perspective within such real-life contextenvironments currently requires close personal monitoring through the educator and therefore is not a 3
  • scalable approach, in particular not within an OE context. Therefore it often remains unknown what arethe learning pathways and cognitive processes through traditional formal higher education subjects andreal-life context environments. Access to a diverse range of educational resources, and the availability oflarge sets of traced learning pathways of learners, and what they have learned and created acrosstraditional formal higher education and real-life context environments, nonetheless offers the potential fora high degree of ICT-based, automated and personalized guidance, as well as it potentially allows forconnecting, matching or scouting individuals at a scale. The following presented OE software frameworkhas thus been conceptualised with the following guiding questions in mind:• How could technologies efficiently support learning pathways and cognitive processes?• How can we take advantage of the potential availability of very large numbers of learning pathways and outcomes to support the individual learner as well as other learners, or to scout and provide them with better guidance?• How do we manage the complexity of the educational opportunities within systematically combined traditional formal higher education offers from across higher education institutions with practicing and authentic learning opportunities within real-life context environments?• What would be the meaning of ‘My Institution’, ‘My Community’ or ‘My Home’ within an Open Education Ecosystem context? What would be the ‘community’, where would it be ‘situated’, and what would be the learners’ ‘home’?• Where would guiding technologies and the OE software system framework itself be located within an Open Education Ecosystem that spans across higher education institutions and real-life context environments?• How are the common understandings of ‘ours and theirs’ and ‘internal and external’ challenged in an Open Education Ecosystem?• How do we allow for the education provision and guidance across a diverse number of technological solutions from a potentially large number of traditional formal higher education and real-context environments?• What would be the balance between fully automated OE software system frameworks that rely on Artificial Intelligence (AI) techniques (e.g. expert rules, planning, managing knowledge through ontology, matching learner profiles with courses, personalized syllabi, etc) and the role of the human instructor who will make refinements and final decisions?• What can be understood to be the human instructor within an Open Education Ecosystem at which human instruction might be provided in a number of different contexts; such as ‘educator to learner’ context, ‘master to apprentice’ context, ‘scout to novice’ context, or a ‘peer to peer’ context? 4
  • 3. Conceptualized supportive Open Education software system frameworkIn accordance to the information and questions of the foregoing section an OE software system frameworkhas been conceptualized and envisaged consisting of the modules that are presented in the following.Key characteristics that the OE software system framework aims to enable are 1. Bringing systematically together traditional formal higher education offers from across higher education institutions with practicing and authentic learning opportunities within real-life context environments 2. Allow for the creative applications of theoretical knowledge in practical or real-life contexts that have become available through newly emerging Open Education Ecosystems. 3. Provide scalable ICT based guidance that is enabled by the large number of educational resources and personalized sets of learning pathways and outcomes that become available through Open Education approaches and that can lead to a significantly higher level of effective, personalized, ICT-based guidance and engagement for all types of learners (formally enrolled students, practitioners, or free learners outside of formal higher education).The conceptualized the OE software system framework consists of the following modules:Module 1: ‘Profiler’ that allows gathering information on learner characteristics and to create a learnerprofile.Module 2: ‘Scraper’ that would allow fetching and brokering all relevant information from acrosstraditional formal higher education subjects and real-life context environments.Module 3: System and browser based ‘Scraper Widgets’ that would allow for personalization as they cantrace individual learning pathways and contexts of learning that have taken place across traditional formalhigher education institutions and real-life context environments.Module 4: ‘Assessor’ that would be capable of monitoring and assessing learner progress and learningoutcomes across traditional formal higher education subjects and real-life context environments.Module 5: ‘Pathway Viewer & Scout’ that would be capable of tracing and brokering the learningpathways and the context in which learning has been taking place across traditional formal highereducation subjects and real-life context environments. The ‘Pathway Viewer & Scout’ module shouldfurther serve as a means to find other learners with whom to collaborate.Module 6: ICT-based personalized ‘Tutor’ that would be responsible for bridging the theory of a subjectand the real-life context and thus provide the necessary guidance to learners.The overall OE software system framework and its modules are depicted within Figure 2.1 below. 5
  • Figure 2.1 OE software system framework modules4. OE software system framework modules and functionsThis section will detail the objectives of the modules that the OE software system framework consists ofand their principal functions. 4.1. Profiler module depictionThe objective of the Profiler module is to allow the creation and keeping up-to-date of learner profiles inthe OE software system framework. A profile is initially created when the learner first enters the systemsand is constantly updated following the learner’s progress. There are four layers in a learner’s profile:• Personal characteristics. Typical information such as age, gender, and occupation (student, professional, etc.).• Learning style and/or experience. Information regarding how a person experiences a learning activity. Similar information such as general skills and competencies also belong here.• Portfolio. The portfolio contains information on the background history of the learner. More specifically (A) from a theoretic subject perspective this might include: material studied, courses taken, collaborations with others, scores achieved, certificates, and (B) from a real-life context it might include: types of real-life context environments engaged in, activities carried out, artifacts created, associated dialogues and collaborations, or how any of the foregoing has been evaluated.• Goals and objectives. Learner themselves or the context of learning (e.g. affiliation to a subject, or the real-life context environments engaged in) declare the set of desirable goals and objectives to be 6
  • achieved through the learning activity along with the level of delivery (introductory, emphasized, reinforced, or applied).The objective is to gather data regarding the learners’ characteristics derived from multiple sources, suchas:• From a traditional formal higher education perspective this might include: (A) Forms. The learner completes forms regarding personal information related to the learning activity (e.g., formal education certificates, goals and objectives, etc.); (B) Questionnaires. There are numerous questionnaires available that can be used to evaluate a person’s characteristics (i.e., learning style, domain-general skills, cognitive profile, etc.); (C) Tests. Prior knowledge tests can be used in the beginning of the learning activity to define better learning paths and goals inside the learner’s zone of proximal development (Vygotsky, 1978); (D) Exams. When an advanced complex topic is on focus or when the desirable level of delivery is high, an exam session can precede the learning activity to provide more information about the learner’s knowledge, in a better way than a simple test would.• From real-life context perspective this might include: (A) Types of activities carried out and completed, for example a piece of software code written and that demonstrated to function; (B) Artifacts created by the learners, or supplemental information provided to accompany them, such as documentation of own learning activities and outcomes; (C) Associated dialogues and collaborations that clearly show a learning progression; (D) How the respective real-life context community participants have evaluated all of the foregoing.The goal is to have a clear image of the learners at all times to be able to provide learning experiences thatbetter fit their needs. Equally important is the ability to present the learning profile back to the learnersand support in that way their meta-cognition and understanding of their own capabilities.The Profiler will thus be responsible for creating and maintaining the user profile information, which isessential for the other components to perform their services. Part of the profile information will beprovided – directly or indirectly – by the users itself (e.g. directly by filling forms, or indirectly byproviding existing OpenID accounts). Also essential is the information that will be provided by theAssessor module, which will be important for judging the learners’ progression towards their learninggoals and objectives and the Tutor, which is responsible for building personalized long-term plans. Inorder for the Profiler to be self-adaptive and proactive towards achieving the goals of the learnersintelligent agent technologies (Wooldridge & Jennings, 1995) likely would need to be deployed. Inparticular some agent characteristics such as the autonomy, reactivity and pro-activity are very appropriatefor successfully representing the learner in the system. The autonomy characteristic is important formaintaining control over internal state and actions (e.g. independently request new interesting educational 7
  • material from the Scrapper). The reactivity characteristic is important for maintaining and updating thelearner’s objectives in reaction to external events (e.g. an assessment result may cause a reaction to changethe internal state of the learner from novice to expert). The proactive characteristic is important inexhibiting goal-directed behaviour towards achieving their objectives (e.g. the agent may follow the plansuggested by the Tutor but request alternative plans in case that the goals are not met).The internal architecture of the Profiler will be based on a combination of standard object-orientedtechnology and agent-based implementation (e.g. with the Open-Source Java Agent DevelopmentFramework – JADE (Bellifemine et al., 2007). For the purposes of integration with the other componentsthe Profiler will provide a standard SOA-based interface that will allow integration in the system and areuse in other learning platforms. 4.2. Scraper module depictionThe objective of the Scraper is to fetch the different type of educational resources and provide them to the‘Tutor’ module for the development of personalized syllabi. The Scraper gathers material from two mainsources: (A) Traditional formal higher education offers from across higher education institutions, and (B)Real-life context environments. To allow for scraping personalized data and to be able to providepersonalized syllabi the Scraper will take into account information provided by the ‘Profiler’, ‘Assessor’,‘Scraper Widgets’ and the ‘Pathway Viewer & Scout’ modules. The fetching process is based on thelearner’s profile (e.g., set of learning goals). The educational resources might be organized alongside thefollowing three categories:• Instructional material. This includes multiple types of learning resources, such as open courses, documents, papers, presentations, multimedia files, etc. The focus of the instructional material is on conceptual knowledge (i.e., ideas, principles, theories of the domain).• Assessment items from both: (A) traditional formal higher education subjects and (B) real-life context environments. The Assessor module will manage the collection of gathered assessment items and also be responsible for monitoring the learner’s progress.• Practicing opportunities. A practicing opportunity is a long and complex learning activity, where the learner is expected to transfer and apply acquired knowledge. Practicing opportunities include participation in communities of practice, open projects, traineeships, etc.The goal is to have a selection of educational resources that (A) will follow the latest trends of highlyevolving domains, (B) is tailored to the learner’s needs, (C) supports multiple ways of knowledgeassessment, and (D) provide opportunities for transferring and applying the acquired knowledge in real- 8
  • life settings. Especially, when multiple representations or perspectives are required (e.g., ill-structureddomains), the role of the Scraper is enhanced by fetching resources that address issues from multipleviewpoints exemplifying the impact of context on knowledge application.The basic responsibility of the Scraper module is thus to gather educational resources that are appropriatefor the learner. The appropriateness is based on profile information available from the user Profile. TheScrapper module therefore collaborates with the Profile module to get the relevant profile information.Having this information the Scrapper can then use the system or browser based Scrapper Widgets toaccumulate relevant educational resources or to provide recommendations to the learner, which could befor example realised through the Tutor module, but also be supported by the system or browser basedScrapper Widgets. The ultimate design of the Scraper module will however depend on the answers tosome of the questions posed within section 2, such as “what would be the meaning of ‘My Institution’,‘My Community’ or ‘My Home’ within an Open Education Ecosystem context?”, or “what would be the‘community’, where would it be ‘situated’, and what would be the learners’ ‘home’?”. The answers tosuch questions will ultimately impact on how and where the system will interact with the learner.To give an example of how the contribution of this module to the overall system architecture isenvisioned, it is assumed that a learner is interested in learning the Java programming language. TheScrapper could use known system-based Scrapper Widgets to collect resources from java courses providedin known LMS (i.e. from traditional formal higher education offers) and to recommend through the systemor browser based Scrapper Widgets suitable Open Source Software (OSS) Java projects from open sourcesoftware repositories such as the Sourceforge repository (i.e. from real-life context environments). Inaddition the education material should be strongly related to the learners’ current interests and levels ofcompetence. For example, there is little value in recommending an advanced course on Java EnterpriseEdition or an OSS project implemented using this technology to a learner that has not completed yet morebasic courses on the Java programming language and/or is currently interested in something else. The roleof the Scrapper module from the above description rather strategic (in the short-term) whereas the role ofthe different Scrapper Widgets is more technical and focused on how to get the different types ofunstructured information and present them to the rest of the system in a uniform and exploitable format. Inother words; Scrapper Widgets are more concerned on how to obtain information whereas the Scrapper ismore concerned on what information to get, what to do with this information once it has been obtained,and how to re-distribute it (e.g. via the Widgets back into such external systems). The role of the Tutormodule on the other hand is strategic in the long-term providing planning capabilities for the learners’progression. It must be explored however how to ultimately allow for the education provision andguidance across a diverse number of technological solutions from a potentially large number of traditionalformal higher education and real-context environments – this goes back to the question of where the 9
  • system would ultimately be situated. 4.3. Scraper Widgets depictionThe objective of the Scraper Widgets is to allow for gathering personalised information on learningpathways and outcomes from (A) traditional formal higher education offers from across higher educationinstitutions and (B) from real-life context environments. Scraper Widgets also allow tracing of the contextin which learning has been taking place, the resources used by the learner, the communities and individualthat the learner engaged at, etc. The Scraper Widgets will further allow feeding information to the Profilerto support a more comprehensive image of the learner’s profile. As such the Scraper Widgets will allowtracing, understanding, and preserving the cognitive processes related to learning. The Scraper Widgetsmay gather information on the progress the learners make from across higher education institutions andfrom real-life context environments and take this into account when suggesting a learning activity.Within the EU funded openSE project the Tampere University of Technology (TUT), Finland, has beendeveloping an experimental application aimed at allowing learners to participate in different open learningspaces and that would keep record, aggregate, organize and integrate all learning activities. Furthermore,such work also considered that learning spaces may issue proofs of educational activities for learners, forexample in terms of certificates, badges or user ratings. In order to keep track of all those authenticrecords, a centralized registry is needed. Such registry system thus should offer data containers to storeand retrieve aggregated data and adequate filtering techniques to extract selected data chunks. Theexperience from TUT suggests that transferring and working with data from heterogeneous learningspaces requires the use of standardized interfaces and well-defined data models. Therefore,implementation embeds well-defined ontologies that reflect learners’ objectives and activities. TUT alsocame across security issues like trust and authenticity that must be taken into account. Properauthentication mechanisms are needed for users to access learning spaces without the need to strugglewith the authentication details of each learning space separately. Authentication related problems couldfor example be addressed by introducing an OpenID based authentication scheme. OpenID is an URL,user-centred, open and decentralized standard for authenticating users. The advantage of OpenID is thatusers do not have to remember the multiple access credentials of different platforms. Instead, a commonaccess point is available for every learning space that supports the OpenID technology. This is illustratedin Figure 4.3.1 and Figure 4.3.2. 10
  • LS GUI: Learning space Graphical user interface Figure 4.3.1 OpenID mechanisam in learningspace Figure 4.3.2 OpenID Authentication MechanismThe TUT concept that had been empowered by the OpenID authentication mechanism offered a modelsolution for accessing, organizing, and retrieving educational activities across different learning spaces.The Scrapper widget development thus could draw on such initial developments, to be subsequentlyleveraged into the development of the System and Browser based Widgets, as well as feeding – via theScraper – into the Assessor module. Similar current attempts, such as Mozilla’s OpenBadges Project(, might be equally suitable to support establishing common standards.One question would be to which extend the Widgets could support a two-way information flow, whichagain goes back questions such as where the ‘system’ would be ultimately situated in a changed context atwhich once agreed concepts such as “My Home”, “My Institutions”, or “My Community” are challengedand need to be reflected on once again. This also links in directly into the need to understand how such asystem can assure that learners are provided with just the right learning opportunity within such widerOpen Education Ecosystem that spans across traditional formal higher education institutions and real-lifecontext environments. 11
  • 4.3.1. System Based Scraper Widgets‘System Based Scraper Widgets’; The term ‘system’ in this context is not limited to the OE softwaresystem framework detailed in this document, but also includes the respective systems of the differenteducational environments; namely (A) traditional formal higher education institutions, and (B) real-lifecontext environments. System Based Scraper Widgets are perhaps the most convenient and efficientsolution, but likely would require: (1) the willingness of the respective institution or environment toimplement the Widgets; and (2) adaption to real-life context environments that are in nature very differentfrom the Learning Management systems used by higher education institutions. Therefore System BasedScraper Widgets might need to provide a more generic ‘base Widget’ that can be adapted to the structureof each of the educational environments. 4.3.2. Browser Based Scraper Widgets‘Browser Based Scraper Widgets’; this is technically viable, but it might be a less convenient solution tothe learner since the effort associated with the Widget installation process, or perhaps also withmaintaining data, is moved into the learners’ responsibility. The opportunity to gain recognition forlearning outcomes through the ‘Assessor’ module, the possibilities to identify others to collaborate withvia the ‘Pathway Viewer & Scout’ module, or more accurate and personalized ‘Tutoring’, might howeverprovide the necessary incentive and motivation for learners to accept such additional efforts. 4.4. Assessor module depictionThe objective of the Assessor module is to monitor the activity of learners and evaluate their progress. TheAssessor keeps track of the learning pathways and outcomes of each learner and informs the Profiler tokeep an updated profile. The Assessor is also connected with the Scraper Widgets to take into accountlearners’ activity outside the system (e.g., in formal or informal learning environments). Through themonitoring process, the Assessor serves two goals. First it keeps the learners informed of their learningpaths so far, making the activity transparent and hence lowering the perceived complexity. Transparencyrefers to the fact that the learner is able to see the material covered, goals reached, and information onstudy patterns. This is another way of supporting learners’ meta-cognition, as the level of transparencyprovided will help learners to self-monitor, self-organize, and self-regulate their activity. High levels ofmeta-cognition enhance the learning outcome (Flavell, 1979, 1987; Metcalfe & Shimamura, 1994;Azevedo & Hadwin, 2005; Dimitracopoulou & Petrou, 2005). Second, the Assessor feeds information onthe current pathways to the Pathway Viewer and Scout (that maintains a depository of all the pathwaysfollowed), so that the latter will be able to compare the current pathway with pathways followed by other 12
  • learners and suggest (A) next steps and (B) peers that would be appropriate for the roles of collaborators,mentors, or mentees. The goal is to allow learners to demonstrate what they have learnt and how they haveapplied their theoretical knowledge in practice and therefore achieve recognition for the learning path thatlearners have followed. In the long run, this would allow education provider (e.g. higher educationinstitutions) to develop backend services such as formal assessment and certification for open learningoutcomes. 4.5. Pathway Viewer & Scout module depictionThe Pathway Viewer & Scout are concerned with both: learning within (A) traditional formal highereducation offers from across higher education institutions and (B) real-life context environments. 4.5.1. The Pathway ViewerThe Pathway Viewer part of this module is a depository holding information on the learning paths learnersfollowed in the past, along with their learning profiles (provided by the Profiler), and their learningoutcomes and achievements and the feedback that they might have received on all of those. In otherwords, the Pathway Viewer is a knowledge database containing the past experiences as recorded by thesystem (through its modules) and the learners (self-reported). This allows new learners to benefit from theactions of others. It also allows applying a Web 2.0 approach, as the comments of past learners on learningobjects become content for new learners. The Pathway Viewer depository will need to be capable ofhandling large sets of data over time. For example, each current learning path that is monitored by theAssessor is moved to the depository, along with comments and learners’ profiles, after the completion ofan activity. This potentially could lead to a large number of data sets that need to be stored and processedin close to real-time potentially for large numbers of learners. 4.5.2. The Scout partThe Scout part of the module is responsible for comparing the profile and the current learning path of alearner with those in the Pathway Viewer and suggesting appropriate next steps, or potentially availablescouts with whom to connect. The question that the Scout tries to answer is: what did other learners withsimilar profiles do while studying to reach similar learning objectives? The Scout can also comparecurrent paths of learners (monitored by the Assessor) and propose appropriate groupings. Even learnersstudying towards different goals may follow overlapping paths. The Scout can point learners to each otherand suggest collaboration in learning activities (i.e., a practicing opportunity). However, the groupingsmay not only refer to peer-collaboration. Peer-mentoring might also be helpful for both parties. From this 13
  • perspective the project will also examine how the availability of large sets of learner profiles, learningpathways and learning outcomes, and the context in which all of this has taken place, might be harnessedwithin a apprentice-mentor context, and how to bring both together in a meaningful way. To this end, theapprentice-mentor context might also serve as a means to foster sustainability and uptake of the systemand concepts, as the apprentice-mentor context could stimulate economic opportunities and benefits forapprentices and mentors in the communities and networks involved.The development activities for the Module 5 ‘Pathway Viewer & Scout’, and also for the Module 6‘Tutor’, could for example draw on earlier works that have been carried out by OUNL, such as the ATL‘ASA Tutor Locator’ that reduces tutor load by using transient ad-hoc peer communities that are seededwith document fragments from the learning network. ATL has been tested for example within two OpenSource Software systems: the Moodle LMS system and the Liferay Portal system. The Moodle LMSsystem is targeted at education institutions and used for example by the ‘Free Technology Academy’(FTA), meanwhile the Liferay Portal system is used across sectors, like for example by the Cisco SystemsCisco Developer Network (CDN - CDN is actually a real-life context environmentat which developers can easily locate resources for their solutions, assist each other in developingsolutions, and reach out to Cisco resources for assistance. FTA and CDN thus might be suitable test-beds.ATL makes use of language technology (Latent Semantic Analysis / LSA) to match questions asked withpeers who on the basis of the documentation that the system has should be able to answer that question.ATL analyses student questions with LSA to find suitable peers as depicted within Figure 4.5.1. 14
  • Fig. 4.5.1 ASL ‘ASA Tutor Locator’ depictionSuitable peers could be selected based on content competence (completed unit in question?), availability(e.g. workload), eligibility (similar peer group), etc. Related work that OUNL has been carrying out withinthe EU FP7 TENCompetence project (Janssen, 2010) has been also looking at ways to support learners infinding their way through multitudes of educational options and selecting a learning path that best fit theirneeds. It aimed at providing recommendations based on indirect social interaction: analysing the pathsfollowed by other learners and feeding this information back as advice to learners facing navigationaldecisions, or to use a learning path specification to describe both the contents and the structure of anylearning path in a formal and uniform way. Results (Janssen, 2010) showed use of the system significantlyenhanced effectiveness of learning and the approach that had been adopted for the Learning PathSpecification and the reference implementation were well received by end-users. 4.6. Tutor module depictionThe Tutor module is at the heart of the system and acts as a controller, defining the learning activity. TheTutor is the only module that interacts continually with every other module of the system and provides themain user interface. The Tutor is responsible for compiling personalized syllabi and conducting the 15
  • learning activity on the system side. The information presented from the Tutor to the learner refers to:• Instructional material on domain conceptual knowledge. Based on the learner’s profile, the Tutor organizes the material gathered by the Scraper in a meaningful way towards the set of learning goals described in the profile. Alternative syllabi or learning paths may also be suggested, especially in domains where multiple perspectives are needed. The main building block of a path is instructional material gathered by the Scraper.• Assessment method. According to the profile and the subject, the Tutor includes in the suggested paths assessment items. The type and the source of these items typically vary to get a better view of learners’ knowledge.• Practicing opportunities. A path also needs to include opportunities where learners are able to transfer their knowledge and apply it to a different context.• Learner groups. The Tutor presents information on neighbouring learners. The groups of learners can be formed based on (A) a common set of goals, (B) same profile characteristics, or both. This supports the creation of smaller learning communities inside the system and increases peer-interaction. Additionally, mentoring opportunities can be identified. In a peer-mentoring process both parties benefit. The mentors reinforce their own study skills and knowledge of the subject, while they assume more responsibility and learn how to manage others. Mentees on the other hand get valuable advice and increased feedback from learners that may have been in their position.• Past experiences. The learner is able to see the pathways followed by others and get valuable information on (A) the effectiveness/difficulty/appropriateness of learning material, (B) the issues raised, (C) external learning resources, and (D) available practicing opportunities.The paths presented by the Tutor are not mandatory. A learner can opt to follow a different path based onpersonal beliefs or input from other learners. The role of the Tutor is to present a complete learningactivity to the learner, containing all the necessary information that would help someone reach the setlearning goals.The Tutor module thus aims to provide the right balance in between fully automated systems that rely onArtificial Intelligence (AI) techniques (e.g. expert rules, planning, managing knowledge through ontology,matching learner profiles with courses, personalized syllabi, etc) and the role of the human instructor whowill make refinements and final decisions. This also includes to explore what can be understood to be thehuman instructor within an Open Education Ecosystem at which human instruction might be provided in anumber of different contexts; such as ‘educator to learner’ context, ‘master to apprentice’ context, ‘scoutto novice’ context, or a ‘peer to peer’ context? The conceptualized system will be intelligent curriculavalidation software based on automated planning techniques and algorithms. The system which will be a 16
  • web service exchanging SOAP messages with the rest of the tutoring software and will be accompaniedby semantic metadata (expressed in OWL-S or SAWSDL). The inputs to the validator will be a (partially)completed curriculum, created by a human expert, the learner’s profile (LIP) and his educational goals.The validator will use automated planning techniques in order to validate the curriculum in terms ofeducational, technical and user profiling aspects. More specifically, the planning component will simulatethe execution of the learning path (curriculum) in order to identify flaws between the learner’s expectedknowledge state, at each step of the process, with the prerequisites of each learning object in the learningpath. Apart from the educational validation, the software will also ensure that each piece of learningmaterial used in the learning path matches the learner’s preferences (e.g. language, multimedia format,pace of learning, etc.) and finally, it will also check the availability of each learning object in order toensure the soundness of the returned curriculum. In case of any flaw discovered in the learning path, thevalidation software will search the state of available learning objects, using their metadata (e.g., LOM), inorder to propose missing paths or alternatives. 4.7. Ontology depictionThe notions regarding the educational domain will be represented as an ontology. An ontology formallyrepresents knowledge as a set of concepts within a domain and their relationships. It has some advantagesover traditional data modelling, such as: • Interoperability: An ontology promotes a significantly higher level of interoperability among distinct, heterogeneous applications, a factor that greatly increases the utility of a system in a drastically diverse environment like the Web. Moreover, ontologies are not task-oriented and implementation-dependent, being relatively independent of particular applications, consisting of rather generic knowledge that can be reused by different kinds of applications/tasks. • Reusability: Ontologies are usually built upon other existing ontologies by extending them with additional concepts. Consequently, one can easily make an ontology available for further reuse. A database schema, on the other hand, is a more inflexible component, explicitly designed for a given application, thus offering limited extensibility capabilities. • Shared understanding: As a consequence of the previous item, an ontology forms a shared understanding of a given domain, where semantics are intertwined with the data. This feature, namely, the ability to define concepts and make conceptual alignment possible, comprises a fundamental strength of ontologies compared to traditional database schemas, where semantics are typically hard-wired, and, therefore, difficult to maintain and – often – out of date. 17
  • • Interference: In addition to offering a practical means for describing a domain, ontologies are also used for reasoning about the entities within that domain. More specifically, mechanisms are offered for inferring implicit knowledge, from the ontology concepts.Therefore, the role of ontologies in Technology Enhanced Learning (TEL) is vital though oftenunderestimated. Ontologies facilitate enhanced interoperability (i.e. interaction between heterogeneoussystems) and assist in the development process itself, by increasing the levels of reusability and reliability.Via ontologies, one can (a) describe the semantics of the learning process, (2) structure activities andcommunication facilities, and (3) define the TEL context and environment.5. Advances and Innovations (A&I) of the proposed systemIt is believed that the proposed OE software system framework pushes the innovation envelope withintraditional, informal and Open Educational settings in a number of ways and as will be detailed in thefollowing discussion. In brief; advances and innovations might be summarized by the way that theconceptualized system is addressing the demands of a context that is in the very beginning of its change.For example, current major EU FP7 funded research projects, such as ROLE, MATURE, NEXT-TELL,DynaLearn, or SCY, all provide significant advances to open, participatory, responsive, or blended formal/ informal educational provision that are supported through technologies. However, all of those projectshave been conceptualized at a point in time where the traditional formal higher education context hadchanged relatively little. 5.1. A&I in the Open Education DomainThe system will support education by providing new and innovative ways through the systematiccombination of traditional formal higher education offers from across higher education institutions withpracticing and authentic learning opportunities within real-life context environments that the Web 2.0provides. This will foster the creative applications of theoretical knowledge in practical or real-lifecontexts, and allow for effectively combining technology, transparency and educational approaches withinacademic, practical or real life situations. The system will provide means that allow for drawing on largenumbers of educational resources and personalized sets of learning pathways and outcomes that becomeavailable through Open Education approaches and that can lead to a significantly higher level of effective,personalized, ICT-based guidance and engagement of all types of learners (formally enrolled students,practitioners, or free learners outside of formal higher education). 18
  • 5.2. A&I on Learner guidance through complex Open Education EcosystemsAt present there is a clear absence of technical solutions that would allow for education design andprovision across technologies, for example this was identified as a major hurdle for the implementation ofthe openSE Open Education Framework ( Even in the case of supportive licensing forunderlying open educational resources, and the access opportunity to educational communities, thedisconnection of the respective technical solutions and environments has turned out so far to be a seriouschallenge. As a matter of fact current technological solutions are typically not designed or intended toallow for education across higher education institutions nor to allow all type of learners to learn at anyinstitution of their choice, nor to engage with students from such institutions, nor to obtain support fromsuch institutions. Commercial approaches like Amazon for the retail sector or Sourceforge for developercommunity do provide some insights on how Open Education Ecosystems might be perceived. Amazonand Sourceforge both offer examples that bring together competing commercial enterprises within theirenvironments, which in the traditional formal higher education domain does not exist. Thus there is theneed to advance knowledge in such new forms of collaboration in the education sector and to contributetowards specifications that emerging Open Education Ecosystems would need to meet. 5.3. A&I on Learner ModellingLearner modelling is an intriguing topic that is currently gaining a significant amount of attention. Themain reason behind this attention is researchers’ interest on adaptation, interoperability, and reusability(e.g., Brusilovsky & Millán, 2007; Brusilovsky & Tasso, 2004; Tseng et al., 2008; Chang et al., 2009;Klašnja-Milićević et al., 2011). By developing a context-aware, ontology-based system that will applycurrent methods of learner modelling, we will be able to achieve higher levels of interoperability andreusability. This is important, since the system must be designed to interact with pre-existing systems anduse freely available learning material. Regarding adaptivity, the question is what to model and how to usethis model to better adapt the learning experience to the individual learning needs. Current EU FP7 fundedprojects such as NEXT-TELL, DynaLearn, and SCY projects each apply a learner modelling approachusing ontology and semantic web. In the case of the conceptualized OE software system frameworkpresented in this document, learner modelling will be used for adaptation in three levels: (a) content, (b)instruction, and (c) scaffolding. The educational material gathered and managed by the system will beadapted to the learners’ profile. In this sense, the presentation of the domain will be adapted to betteraddress the individual’s needs. Second, the instructional method will also be adapted, meaning thesuggested learning pathways and the practicing opportunities proposed to the learners. Finally, adaptationwill be applied to the scaffolding method towards the learner, meaning the supporting content (e.g.,tutorials), transparency, peer interaction, etc. All these can be adapted according to a learner’s profile. 19
  • 5.4. A&I on Instruction and AssessmentIn formal learning environments, learners typically follow a predefined path towards a predefined set oflearning objectives. Even when the learners have a degree of freedom (e.g. course enrolment), it is theinstructor or the institution that defines the learning experience. In order to deal with complex topics andill-structured domains, formal education tends to simplify matters. Researchers agree that thisoversimplification has eventually a detrimental effect on learning (Feltovich et al., 1989, 1997, 2001;Spiro et al., 1988, 1989). Integrating technology in formal education is an effort to address this issue byproviding learners the opportunity to have richer learning experiences. In some cases, learners are able toapply a trial and error, hands on, or simulation techniques to get an idea of how things work in the realworld. Although this is in the right direction, it does not fully reduce the context distance the learnersexperience when they are asked, eventually as professionals, to transfer and apply knowledge that wasacquired in an educational context to a real-life situation. The overall pedagogy we seek to apply in thesystem is based on the constructivism theory of learning and draws on: (a) active learning (Ward, 1998),(b) situated learning (Lave & Wenger, 1991; Korthagen, 2010; Kimble & Hildreth, 2008; Hung, 2002),and (c) case-, problem-, and project-based learning (Demetriadis et al., 2008; Papadopoulos et al., 2006,2007; Jonassen & Hernadez-Serrano, 2002; Chin & Chia, 2005; Hmelo-Silver, 2004). The educationalgoal is to support learning by shortening this context distance and immersing students in real-lifeenvironments. The first step is to address complexity very early in the activity. One reason why learnersoften fail to effectively transfer knowledge is because they do not have a clear image of the complexityand irregularity of a real-life situation. In the conceptualized OE software system framework presented inthis document, learners will be supported by an increased level of transparency. The goal is to have thelearner always aware of their individual characteristics, the content they covered, the goals they reached,and the available learning paths towards remaining goals. Second, instead of presenting a single solutionor a single learning path, learners must understand that very rarely will there be only one solution to a realproblem. The learner will be provided with alternative pathways and multiple views of a knowledgedomain in order to assist learners in understanding and recognizing domain themes and the way they areconnected to each other. 5.5. A&I in the field of Personal Learning EnvironmentsThe main idea behind personal learning environments is to provide learners with a set of tools and servicesthat they can freely use to form their own learning spaces (Wild et al., 2008; Liber & Johnson, 2008).Learning goals and activities can be set by the learners themselves, while the learning material, along with 20
  • the available services, can be outsourced and compiled from various resources. This description fitsperfectly well in the system. In addition, though, the knowledge domain itself will also be defined by thelearners. When learners login into the system they are able to define their learning goals including thetopics they are interested in. The system then presents personalized syllabi that include multipleeducational resources (instructional material, assessment items, and practicing opportunities). The learnersare able to follow alternative learning paths and select the material that better fits their needs creating atruly personal learning space. 5.6. A&I in Automated Planning for Curricula SynthesisAutomated Planning is the area of Artificial Intelligence that deals with search problems (called planningproblems) of finding specific sequences of actions that, if applied, drive the system in hand from itscurrent state to a desired one. Automated Planning is an active research area for approximately fivedecades and offers a number of algorithms and systems that automatically or semi-automatically constructsequences of actions along with formalizations and languages for efficiently representing planningproblems. Automated Planning has been effectively applied to solve curricula synthesis problems. Thelearning material is structured in concepts and prerequisite knowledge is defined, which states the causalrelationships between different concepts. Then, planning techniques are used in order to find plans thatachieve the learning goals. There are also a number of systems (Morales et al., 2009; Ullrich, 2005) thatserve as course generators that automatically assemble learning objects retrieved from one or severalrepositories. These systems adopt the Hierarchical Task Network (HTN) planning framework. Morerecent approaches have been based on the use of domain independent planning systems enhanced withsemantic capabilities (e.g., ontologies) for matching learning objects with the learner’s profile (e.g.,Kontopoulos et al., 2008; Garrido et al., 2011).Drawing on the above, the proposed system will perform curricula validation based on automatic planningtechniques. The system, through its Tutor module, will be able to suggest appropriate learning paths andvalidate paths suggested by human instructors. Validation will simultaneously take into account thelearner’s profile, the available educational material and activities, and past learners’ input. This is a hardtask for a human instructor as it involves going through a massive volume of data (feedback, forum posts,reports, etc.) containing past learners’ opinions and behaviour. Automation will improve the feasibility ofthis process and enable a greater range of factors to be taken into account. 21
  • 5.7. A&I in Ontologies and TELOntologies are now an established approach to describing the respective educational domains of theapplications, namely, the various educational fields and learning objectives, as well as theirinterrelationships. For example, Chen et al. apply a mobile phone ontology-based knowledge base toassess the competence of mobile phone salespersons’ professional knowledge (Chen et al., 2011).Similarly, Muthulakshmi & Uma propose an ontology-based e-learning system for the sports domain(Muthulakshmi & Uma, 2011). Similar paradigms are described in (Hunyadi & Pah, 2008; Snae &Brueckner, 2007). Other approaches extend the utility of ontologies, by encompassing informationregarding the learner’s profile (Ivanova & Chatti, 2010), or for monitoring and evaluating the learner’sbehaviour, learning styles and performance (Hadj et al., 2007; Pramitasari et al., 2009). Further examplesintegrate a range of learning ontology types (e.g., user modeling ontology, domain ontology and learningdesign ontology), to capture the information about the real usage of a learning object inside a learningdesign (Jovanović et al., 2007). An alternative direction is the joint application of more ontologies (insteadof a single, “enhanced” ontology) in a single framework, for describing the various differentiated features(e.g. domains, users, observations, competencies etc.) (Abel et al., 2004; Henze et al., 2004; Draganidis etal., 2006). Furthermore, a limited number of approaches propose a more extensible architecture, where thee-learning system is not limited to using a static ontology, but integrates automated ontology mapping andmerging procedures, via which the existing knowledge base is dynamically updated with new knowledge(e.g. Castano et al., 2004; Busse, 2005; Kiu & Lee, 2006). The ontology-driven approach is rapidlybecoming mainstream, taking advantage of the fact that information is organized systematically and thesemantically-enriched knowledge is both sharable and reusable. 22
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