Open Education Ecosystems,

learning analytics and supportive software system framework

Andreas Meiszner, PhD

United Nations University UNU-MERIT | The Netherlands | meiszner@merit.unu.edu

Pantelis Papadopoulos, PhD

Aristotle University of Thessaloniki | Greece | pmpapad@csd.auth.gr

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

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Note: This conceptualized software system framework for Open Education Ecosystems and learning analytics as presented within this document has been initially prepared within the wider context of a research project funding proposal. For this reason perhaps not all information provided within this document are self-explanatory or fully comprehensive; though an effort has been made to leverage the initially developed key information into this document and to present them within a coherent narrative. Further information on the initially developed research project concept and supplement information are available upon request.

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resulting work only under the same or similar license to this one. Version Information: January 17th 2012

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Abstract

Open Education (OE) potentially allows for the systematic bringing together of traditional formal higher education offers from across higher education institutions with practice and authentic learning opportunities within real-life context environments that Web 2.0 provides. OE thus in principle allows for the scalable collections of large sets of learning pathways, the outcomes of learning, and the contexts in which learning takes place from across Higher Education Institutions, the underlying academic subjects, and associated authentic real-life context environments. This provides the potential to 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 learning with components to maintain profiles, scrape data, assess performance and offer tools and pathways to the 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 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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Table of Content

Glossary....................................................................................................................................................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  

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Glossary  

Open Courses [OC] – in contrast to traditional formal education courses, which will typically limit access to 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 or collaborators; or established virtual communities of practice. The types of participation opportunities provided to such third parties might vary and could consist for example of: ‘open to read’, ‘open to participate’, ‘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-use digital open educational resources and digital educational tools, and the free and open access to the related virtual educational communities and environments, in order to learn, teach, exchange or advance knowledge in a collaborative and interactive way.

Open Education Ecosystem [OEE] – the wider socio-technological system that might consist of a number of OEFs and the various resources of such OEFs, including the stakeholders that are populating this ecosystem. OEE can be understood as the practical response to theoretic concepts that have been put forward by the work of Brown and Adler (2008) on ‘Open Participatory Learning Ecosystems’, which emphasizes the emergent interconnections of educational resources and lightweight, bottom-up, emergent socio-technical structures.

Open Education Framework [OEF] – an organizational framework, which is embedded within a technological system (such as OEE), that allows for the design and delivery of Open Education. The OEF includes and considers the various OE module parts, such as ‘Open Content’, ‘Open Degrees’, ‘Open Assessment’, ‘Open Learning’, ‘Open Tutoring’, ‘Open Technology’ and ‘Open Communities’. The OEF also tangles organizational aspects with regards to the interplay of formal traditional higher education across institutions and real-life context environments. An example of OEF is the EU funded openSE framework that brings together courses from across traditional formal higher education institutions and from 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 that would not be easily permitted under copyright alone” (Source of definition: http://en.wikipedia.org/wiki/Open_educational_resources).

Real-life context environments – Real-life context environments are existing online environments that allow students to experiment and to apply their knowledge within a real-life context. Those environments are real world, live and often real-time, and they are complex. Real-life context environments could be for example (1) ‘Open Source Software development’, (2) ‘Agile Software Development’, and (3) ‘Open Data Initiatives’.

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1. Introduction  

Over the past years the traditional formal education domain has been subject to a process of opening up

resulting in an ever-blurring border between the formal and the informal and allowing traditional formal

education 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 approaches

within formal education and release of formal content for less formal use. On the one hand the traditional

formal education domain has been taking advantage of the practicing and authentic learning opportunities

that Web 2.0 based real-life context environments provide (Meiszner, 2010). For example using Open

Source Software development communities as real-life context environments to support traditional formal

higher education offers (Stamelos, 2008). Mozilla Education, OpenOffice Education, or the Apache

Mentored Internship program1 are just three of such practitioner driven attempts, with openSE and

ict@innovation2 the academic driven counterparts, and all of them aim to provide learners with real-life

practicing or internship opportunities alongside their academic subjects. On the other hand also the formal

education sector has been advancing and further ‘opening up’ itself. The past years have been marked by

the emergence of ‘Open Courses’ (Meiszner, 2010), such as ‘OpenEd Syllabus’ (US, 2007), ‘CCK

Connectivism 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 of

openness, incorporate different sets of free and open tools and learning resources, and – to a varying

degree – mix the formal with the informal; bringing together the different stakeholders to be found on the

web (Meiszner, 2010). These aspects taken together offer the potential to systematically bring together

traditional formal higher education offers and theoretic subjects, and from across higher education

institutions, with practicing and authentic learning opportunities within real-life context environments that

Web 2.0 provides. The recent developments considered above indicate an immense potential to better

support learners, but they also change the context of what is to be understood as traditional formal higher

education, and what current or future technologies might need to support. This changed context brings up

a number of questions that must be reflected upon once again, and as will be detailed within the following

section.

1 For all of the foregoing please see: http://teachingopensource.org/index.php/Main_Page 2 See http://www.opense.net/ and http://www.ict-innovation.fossfa.net/clp 3 See: http://opencontent.org/wiki/index.php?title=Intro_Open_Ed_Syllabus, http://www.connectivism.ca/, http://www.open-ed.eu/, https://www.ai-class.com/

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2. The Open Education context and potential for learning analytics

ICT based global and collaborative Open Education (OE) approaches in traditional formal higher

education in general, and through Open Education Frameworks in particular, are a new and emerging

domain that hold the potential to better support students to understand and construct their personal

conceptual knowledge and meaning of scientific subjects, to take learners through the complexity of

traditional formal higher education subjects, activating and feeding at the same time the learners’ curiosity

and reasoning, and allowing the creative applications of their theoretical knowledge in practical or real life

situations. This approach has potential in particular to support learners within the Science, Technology

and Mathematics subjects. Open Education (OE) allows for systematic integration of traditional formal

higher education offers from across higher education institutions and subjects with practicing and

authentic learning opportunities within real-life context environments. This can facilitate guiding learners

through the complexity of subjects and allow for linking the theory of a subject to practice and authentic

learning opportunities. Moreover, such OE approaches support the creative application of the theory of

subjects within real-life context environments. Earlier works, such as the EU funded openSE project, have

shown that OE approaches enabled Computer Science (CS) Software Engineering students to engage and

learn within real life and authentic learning activities within Open Source Software projects. OE

approaches are however not limited to the Open Source Software case and could be generally applied

within subjects and whenever real-life context environments exist. In the CS case further examples such as

agile software development (e.g. http://pipes.yahoo.com/pipes) or large-scale open data initiatives (e.g.

Rotterdam City Open Data Initiative - www.rotterdamopendata.org) are seen to be equally suitable real-

life context environments. These have the characteristics to support learning that is situated in a real-life

context – and under real life conditions. They are all real world, live and with real-time dependencies, and

they are complex, therefore providing authentic learning opportunities. Just as importantly from an

educational perspective; allowing learners to engage within such learning opportunities, either by taking

part in analyzing activities or to create concrete and less abstract applications of concepts, provides the

need and opportunity for learners to make sense of theoretic subjects. OE and the use of real-life context

environments thus encourages students to explore, to find out, to apply their theoretical knowledge in

practice, or to gain key and soft skills that are difficult to impart within a traditional formal educational

context (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 formal

higher 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 context

environments currently requires close personal monitoring through the educator and therefore is not a

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scalable approach, in particular not within an OE context. Therefore it often remains unknown what are

the learning pathways and cognitive processes through traditional formal higher education subjects and

real-life context environments. Access to a diverse range of educational resources, and the availability of

large sets of traced learning pathways of learners, and what they have learned and created across

traditional formal higher education and real-life context environments, nonetheless offers the potential for

a high degree of ICT-based, automated and personalized guidance, as well as it potentially allows for

connecting, matching or scouting individuals at a scale. The following presented OE software framework

has 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?

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3. Conceptualized supportive Open Education software system framework

In accordance to the information and questions of the foregoing section an OE software system framework

has 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 learner

profile.

Module 2: ‘Scraper’ that would allow fetching and brokering all relevant information from across

traditional formal higher education subjects and real-life context environments.

Module 3: System and browser based ‘Scraper Widgets’ that would allow for personalization as they can

trace individual learning pathways and contexts of learning that have taken place across traditional formal

higher education institutions and real-life context environments.

Module 4: ‘Assessor’ that would be capable of monitoring and assessing learner progress and learning

outcomes 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 learning

pathways and the context in which learning has been taking place across traditional formal higher

education subjects and real-life context environments. The ‘Pathway Viewer & Scout’ module should

further 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 subject

and 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.

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Figure 2.1 OE software system framework modules

4. OE software system framework modules and functions

This section will detail the objectives of the modules that the OE software system framework consists of

and their principal functions.

4.1. Profiler module depiction

The objective of the Profiler module is to allow the creation and keeping up-to-date of learner profiles in

the OE software system framework. A profile is initially created when the learner first enters the systems

and 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

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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, such

as:

• 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 that

better fit their needs. Equally important is the ability to present the learning profile back to the learners

and 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 is

essential for the other components to perform their services. Part of the profile information will be

provided – directly or indirectly – by the users itself (e.g. directly by filling forms, or indirectly by

providing existing OpenID accounts). Also essential is the information that will be provided by the

Assessor module, which will be important for judging the learners’ progression towards their learning

goals and objectives and the Tutor, which is responsible for building personalized long-term plans. In

order for the Profiler to be self-adaptive and proactive towards achieving the goals of the learners

intelligent agent technologies (Wooldridge & Jennings, 1995) likely would need to be deployed. In

particular some agent characteristics such as the autonomy, reactivity and pro-activity are very appropriate

for successfully representing the learner in the system. The autonomy characteristic is important for

maintaining control over internal state and actions (e.g. independently request new interesting educational

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material from the Scrapper). The reactivity characteristic is important for maintaining and updating the

learner’s objectives in reaction to external events (e.g. an assessment result may cause a reaction to change

the internal state of the learner from novice to expert). The proactive characteristic is important in

exhibiting goal-directed behaviour towards achieving their objectives (e.g. the agent may follow the plan

suggested 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-oriented

technology and agent-based implementation (e.g. with the Open-Source Java Agent Development

Framework – JADE (Bellifemine et al., 2007). For the purposes of integration with the other components

the Profiler will provide a standard SOA-based interface that will allow integration in the system and a

reuse in other learning platforms.

4.2. Scraper module depiction

The 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 main

sources: (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 provide

personalized 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 the

learner’s profile (e.g., set of learning goals). The educational resources might be organized alongside the

following 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 highly

evolving domains, (B) is tailored to the learner’s needs, (C) supports multiple ways of knowledge

assessment, and (D) provide opportunities for transferring and applying the acquired knowledge in real-

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life settings. Especially, when multiple representations or perspectives are required (e.g., ill-structured

domains), the role of the Scraper is enhanced by fetching resources that address issues from multiple

viewpoints exemplifying the impact of context on knowledge application.

The basic responsibility of the Scraper module is thus to gather educational resources that are appropriate

for the learner. The appropriateness is based on profile information available from the user Profile. The

Scrapper 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 to

accumulate relevant educational resources or to provide recommendations to the learner, which could be

for example realised through the Tutor module, but also be supported by the system or browser based

Scrapper Widgets. The ultimate design of the Scraper module will however depend on the answers to

some 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 to

such 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 is

envisioned, it is assumed that a learner is interested in learning the Java programming language. The

Scrapper could use known system-based Scrapper Widgets to collect resources from java courses provided

in known LMS (i.e. from traditional formal higher education offers) and to recommend through the system

or browser based Scrapper Widgets suitable Open Source Software (OSS) Java projects from open source

software repositories such as the Sourceforge repository (i.e. from real-life context environments). In

addition the education material should be strongly related to the learners’ current interests and levels of

competence. For example, there is little value in recommending an advanced course on Java Enterprise

Edition or an OSS project implemented using this technology to a learner that has not completed yet more

basic courses on the Java programming language and/or is currently interested in something else. The role

of the Scrapper module from the above description rather strategic (in the short-term) whereas the role of

the different Scrapper Widgets is more technical and focused on how to get the different types of

unstructured information and present them to the rest of the system in a uniform and exploitable format. In

other words; Scrapper Widgets are more concerned on how to obtain information whereas the Scrapper is

more 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 Tutor

module 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 and

guidance across a diverse number of technological solutions from a potentially large number of traditional

formal higher education and real-context environments – this goes back to the question of where the

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system would ultimately be situated.

4.3. Scraper Widgets depiction

The objective of the Scraper Widgets is to allow for gathering personalised information on learning

pathways and outcomes from (A) traditional formal higher education offers from across higher education

institutions and (B) from real-life context environments. Scraper Widgets also allow tracing of the context

in which learning has been taking place, the resources used by the learner, the communities and individual

that the learner engaged at, etc. The Scraper Widgets will further allow feeding information to the Profiler

to support a more comprehensive image of the learner’s profile. As such the Scraper Widgets will allow

tracing, understanding, and preserving the cognitive processes related to learning. The Scraper Widgets

may gather information on the progress the learners make from across higher education institutions and

from 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 been

developing an experimental application aimed at allowing learners to participate in different open learning

spaces 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, for

example in terms of certificates, badges or user ratings. In order to keep track of all those authentic

records, a centralized registry is needed. Such registry system thus should offer data containers to store

and retrieve aggregated data and adequate filtering techniques to extract selected data chunks. The

experience from TUT suggests that transferring and working with data from heterogeneous learning

spaces requires the use of standardized interfaces and well-defined data models. Therefore,

implementation embeds well-defined ontologies that reflect learners’ objectives and activities. TUT also

came across security issues like trust and authenticity that must be taken into account. Proper

authentication mechanisms are needed for users to access learning spaces without the need to struggle

with the authentication details of each learning space separately. Authentication related problems could

for 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 that

users do not have to remember the multiple access credentials of different platforms. Instead, a common

access point is available for every learning space that supports the OpenID technology. This is illustrated

in Figure 4.3.1 and Figure 4.3.2.

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LS GUI: Learning space Graphical user interface

Figure 4.3.1 OpenID mechanisam in learningspace

Figure 4.3.2 OpenID Authentication Mechanism

The TUT concept that had been empowered by the OpenID authentication mechanism offered a model

solution for accessing, organizing, and retrieving educational activities across different learning spaces.

The Scrapper widget development thus could draw on such initial developments, to be subsequently

leveraged into the development of the System and Browser based Widgets, as well as feeding – via the

Scraper – into the Assessor module. Similar current attempts, such as Mozilla’s OpenBadges Project

(http://openbadges.org), 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, which

again goes back questions such as where the ‘system’ would be ultimately situated in a changed context at

which once agreed concepts such as “My Home”, “My Institutions”, or “My Community” are challenged

and need to be reflected on once again. This also links in directly into the need to understand how such a

system can assure that learners are provided with just the right learning opportunity within such wider

Open Education Ecosystem that spans across traditional formal higher education institutions and real-life

context environments.

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4.3.1. System Based Scraper Widgets

‘System Based Scraper Widgets’; The term ‘system’ in this context is not limited to the OE software

system framework detailed in this document, but also includes the respective systems of the different

educational environments; namely (A) traditional formal higher education institutions, and (B) real-life

context environments. System Based Scraper Widgets are perhaps the most convenient and efficient

solution, but likely would require: (1) the willingness of the respective institution or environment to

implement the Widgets; and (2) adaption to real-life context environments that are in nature very different

from the Learning Management systems used by higher education institutions. Therefore System Based

Scraper Widgets might need to provide a more generic ‘base Widget’ that can be adapted to the structure

of 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 to

the learner since the effort associated with the Widget installation process, or perhaps also with

maintaining data, is moved into the learners’ responsibility. The opportunity to gain recognition for

learning outcomes through the ‘Assessor’ module, the possibilities to identify others to collaborate with

via the ‘Pathway Viewer & Scout’ module, or more accurate and personalized ‘Tutoring’, might however

provide the necessary incentive and motivation for learners to accept such additional efforts.

4.4. Assessor module depiction

The objective of the Assessor module is to monitor the activity of learners and evaluate their progress. The

Assessor keeps track of the learning pathways and outcomes of each learner and informs the Profiler to

keep an updated profile. The Assessor is also connected with the Scraper Widgets to take into account

learners’ activity outside the system (e.g., in formal or informal learning environments). Through the

monitoring process, the Assessor serves two goals. First it keeps the learners informed of their learning

paths so far, making the activity transparent and hence lowering the perceived complexity. Transparency

refers to the fact that the learner is able to see the material covered, goals reached, and information on

study patterns. This is another way of supporting learners’ meta-cognition, as the level of transparency

provided will help learners to self-monitor, self-organize, and self-regulate their activity. High levels of

meta-cognition enhance the learning outcome (Flavell, 1979, 1987; Metcalfe & Shimamura, 1994;

Azevedo & Hadwin, 2005; Dimitracopoulou & Petrou, 2005). Second, the Assessor feeds information on

the current pathways to the Pathway Viewer and Scout (that maintains a depository of all the pathways

followed), so that the latter will be able to compare the current pathway with pathways followed by other

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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 have

applied their theoretical knowledge in practice and therefore achieve recognition for the learning path that

learners have followed. In the long run, this would allow education provider (e.g. higher education

institutions) to develop backend services such as formal assessment and certification for open learning

outcomes.

4.5. Pathway Viewer & Scout module depiction

The Pathway Viewer & Scout are concerned with both: learning within (A) traditional formal higher

education offers from across higher education institutions and (B) real-life context environments.

4.5.1. The Pathway Viewer

The Pathway Viewer part of this module is a depository holding information on the learning paths learners

followed in the past, along with their learning profiles (provided by the Profiler), and their learning

outcomes and achievements and the feedback that they might have received on all of those. In other

words, the Pathway Viewer is a knowledge database containing the past experiences as recorded by the

system (through its modules) and the learners (self-reported). This allows new learners to benefit from the

actions of others. It also allows applying a Web 2.0 approach, as the comments of past learners on learning

objects become content for new learners. The Pathway Viewer depository will need to be capable of

handling large sets of data over time. For example, each current learning path that is monitored by the

Assessor is moved to the depository, along with comments and learners’ profiles, after the completion of

an activity. This potentially could lead to a large number of data sets that need to be stored and processed

in close to real-time potentially for large numbers of learners.

4.5.2. The Scout part

The Scout part of the module is responsible for comparing the profile and the current learning path of a

learner with those in the Pathway Viewer and suggesting appropriate next steps, or potentially available

scouts with whom to connect. The question that the Scout tries to answer is: what did other learners with

similar profiles do while studying to reach similar learning objectives? The Scout can also compare

current paths of learners (monitored by the Assessor) and propose appropriate groupings. Even learners

studying towards different goals may follow overlapping paths. The Scout can point learners to each other

and suggest collaboration in learning activities (i.e., a practicing opportunity). However, the groupings

may not only refer to peer-collaboration. Peer-mentoring might also be helpful for both parties. From this

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perspective the project will also examine how the availability of large sets of learner profiles, learning

pathways and learning outcomes, and the context in which all of this has taken place, might be harnessed

within a apprentice-mentor context, and how to bring both together in a meaningful way. To this end, the

apprentice-mentor context might also serve as a means to foster sustainability and uptake of the system

and concepts, as the apprentice-mentor context could stimulate economic opportunities and benefits for

apprentices 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 seeded

with document fragments from the learning network. ATL has been tested for example within two Open

Source Software systems: the Moodle LMS system and the Liferay Portal system. The Moodle LMS

system 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 Systems'

Cisco Developer Network (CDN - developer.cisco.com). CDN is actually a real-life context environment

at which developers can easily locate resources for their solutions, assist each other in developing

solutions, 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 with

peers 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.

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Fig. 4.5.1 ASL ‘ASA Tutor Locator’ depiction

Suitable 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 within

the EU FP7 TENCompetence project (Janssen, 2010) has been also looking at ways to support learners in

finding their way through multitudes of educational options and selecting a learning path that best fit their

needs. It aimed at providing recommendations based on indirect social interaction: analysing the paths

followed by other learners and feeding this information back as advice to learners facing navigational

decisions, or to use a learning path specification to describe both the contents and the structure of any

learning path in a formal and uniform way. Results (Janssen, 2010) showed use of the system significantly

enhanced effectiveness of learning and the approach that had been adopted for the Learning Path

Specification and the reference implementation were well received by end-users.

4.6. Tutor module depiction

The Tutor module is at the heart of the system and acts as a controller, defining the learning activity. The

Tutor is the only module that interacts continually with every other module of the system and provides the

main user interface. The Tutor is responsible for compiling personalized syllabi and conducting the

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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 on

personal beliefs or input from other learners. The role of the Tutor is to present a complete learning

activity to the learner, containing all the necessary information that would help someone reach the set

learning goals.

The Tutor module thus aims to provide the right balance in between fully automated systems 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. This also includes to explore 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? The conceptualized system will be intelligent curricula

validation software based on automated planning techniques and algorithms. The system which will be a

17

web service exchanging SOAP messages with the rest of the tutoring software and will be accompanied

by 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 of

educational, technical and user profiling aspects. More specifically, the planning component will simulate

the execution of the learning path (curriculum) in order to identify flaws between the learner’s expected

knowledge state, at each step of the process, with the prerequisites of each learning object in the learning

path. Apart from the educational validation, the software will also ensure that each piece of learning

material 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 to

ensure the soundness of the returned curriculum. In case of any flaw discovered in the learning path, the

validation software will search the state of available learning objects, using their metadata (e.g., LOM), in

order to propose missing paths or alternatives.

4.7. Ontology depiction

The notions regarding the educational domain will be represented as an ontology. An ontology formally

represents knowledge as a set of concepts within a domain and their relationships. It has some advantages

over 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.

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• 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 often

underestimated. Ontologies facilitate enhanced interoperability (i.e. interaction between heterogeneous

systems) 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 and

communication facilities, and (3) define the TEL context and environment.

5. Advances and Innovations (A&I) of the proposed system

It is believed that the proposed OE software system framework pushes the innovation envelope within

traditional, informal and Open Educational settings in a number of ways and as will be detailed in the

following discussion. In brief; advances and innovations might be summarized by the way that the

conceptualized 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 projects

have been conceptualized at a point in time where the traditional formal higher education context had

changed relatively little.

5.1. A&I in the Open Education Domain

The system will support education by providing new and innovative ways through the systematic

combination of traditional formal higher education offers from across higher education institutions with

practicing and authentic learning opportunities within real-life context environments that the Web 2.0

provides. This will foster the creative applications of theoretical knowledge in practical or real-life

contexts, and allow for effectively combining technology, transparency and educational approaches within

academic, practical or real life situations. The system will provide means that allow for drawing on large

numbers 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 of all types of learners (formally enrolled students,

practitioners, or free learners outside of formal higher education).

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5.2. A&I on Learner guidance through complex Open Education Ecosystems

At present there is a clear absence of technical solutions that would allow for education design and

provision across technologies, for example this was identified as a major hurdle for the implementation of

the openSE Open Education Framework (www.opense.net). 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.

5.3. A&I on Learner Modelling

Learner modelling is an intriguing topic that is currently gaining a significant amount of attention. The

main 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 apply

current methods of learner modelling, we will be able to achieve higher levels of interoperability and

reusability. This is important, since the system must be designed to interact with pre-existing systems and

use freely available learning material. Regarding adaptivity, the question is what to model and how to use

this model to better adapt the learning experience to the individual learning needs. Current EU FP7 funded

projects such as NEXT-TELL, DynaLearn, and SCY projects each apply a learner modelling approach

using ontology and semantic web. In the case of the conceptualized OE software system framework

presented 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 be

adapted to the learners’ profile. In this sense, the presentation of the domain will be adapted to better

address the individual’s needs. Second, the instructional method will also be adapted, meaning the

suggested learning pathways and the practicing opportunities proposed to the learners. Finally, adaptation

will 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.

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5.4. A&I on Instruction and Assessment

In formal learning environments, learners typically follow a predefined path towards a predefined set of

learning objectives. Even when the learners have a degree of freedom (e.g. course enrolment), it is the

instructor or the institution that defines the learning experience. In order to deal with complex topics and

ill-structured domains, formal education tends to simplify matters. Researchers agree that this

oversimplification 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 by

providing learners the opportunity to have richer learning experiences. In some cases, learners are able to

apply a trial and error, hands on, or simulation techniques to get an idea of how things work in the real

world. Although this is in the right direction, it does not fully reduce the context distance the learners

experience when they are asked, eventually as professionals, to transfer and apply knowledge that was

acquired in an educational context to a real-life situation. The overall pedagogy we seek to apply in the

system 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 educational

goal is to support learning by shortening this context distance and immersing students in real-life

environments. The first step is to address complexity very early in the activity. One reason why learners

often fail to effectively transfer knowledge is because they do not have a clear image of the complexity

and irregularity of a real-life situation. In the conceptualized OE software system framework presented in

this document, learners will be supported by an increased level of transparency. The goal is to have the

learner 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 solution

or a single learning path, learners must understand that very rarely will there be only one solution to a real

problem. The learner will be provided with alternative pathways and multiple views of a knowledge

domain in order to assist learners in understanding and recognizing domain themes and the way they are

connected to each other.

5.5. A&I in the field of Personal Learning Environments

The main idea behind personal learning environments is to provide learners with a set of tools and services

that 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

21

the available services, can be outsourced and compiled from various resources. This description fits

perfectly well in the system. In addition, though, the knowledge domain itself will also be defined by the

learners. When learners login into the system they are able to define their learning goals including the

topics they are interested in. The system then presents personalized syllabi that include multiple

educational resources (instructional material, assessment items, and practicing opportunities). The learners

are able to follow alternative learning paths and select the material that better fits their needs creating a

truly personal learning space.

5.6. A&I in Automated Planning for Curricula Synthesis

Automated Planning is the area of Artificial Intelligence that deals with search problems (called planning

problems) of finding specific sequences of actions that, if applied, drive the system in hand from its

current state to a desired one. Automated Planning is an active research area for approximately five

decades and offers a number of algorithms and systems that automatically or semi-automatically construct

sequences of actions along with formalizations and languages for efficiently representing planning

problems. Automated Planning has been effectively applied to solve curricula synthesis problems. The

learning material is structured in concepts and prerequisite knowledge is defined, which states the causal

relationships between different concepts. Then, planning techniques are used in order to find plans that

achieve the learning goals. There are also a number of systems (Morales et al., 2009; Ullrich, 2005) that

serve as course generators that automatically assemble learning objects retrieved from one or several

repositories. These systems adopt the Hierarchical Task Network (HTN) planning framework. More

recent approaches have been based on the use of domain independent planning systems enhanced with

semantic 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 planning

techniques. The system, through its Tutor module, will be able to suggest appropriate learning paths and

validate paths suggested by human instructors. Validation will simultaneously take into account the

learner’s profile, the available educational material and activities, and past learners’ input. This is a hard

task 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 of

this process and enable a greater range of factors to be taken into account.

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5.7. A&I in Ontologies and TEL

Ontologies are now an established approach to describing the respective educational domains of the

applications, namely, the various educational fields and learning objectives, as well as their

interrelationships. For example, Chen et al. apply a mobile phone ontology-based knowledge base to

assess 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 information

regarding the learner’s profile (Ivanova & Chatti, 2010), or for monitoring and evaluating the learner’s

behaviour, learning styles and performance (Hadj et al., 2007; Pramitasari et al., 2009). Further examples

integrate a range of learning ontology types (e.g., user modeling ontology, domain ontology and learning

design ontology), to capture the information about the real usage of a learning object inside a learning

design (Jovanović et al., 2007). An alternative direction is the joint application of more ontologies (instead

of 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 et

al., 2006). Furthermore, a limited number of approaches propose a more extensible architecture, where the

e-learning system is not limited to using a static ontology, but integrates automated ontology mapping and

merging 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 rapidly

becoming mainstream, taking advantage of the fact that information is organized systematically and the

semantically-enriched knowledge is both sharable and reusable.

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