Int. J. Knowledge and Learning, Vol. 3, Nos. 2/3, 2007 209

Copyright © 2007 Inderscience Enterprises Ltd.

IWT: an innovative solution for AGS e-learning model

Giovannina Albano* DIIMA – Centre of Excellence in Methods and Systems for Learning and Knowledge, Via Ponte don Melillo, Fisciano 84084, SA, Italy Fax: +39-089-964-191 E-mail: albano@diima.unisa.it *Corresponding author

Matteo Gaeta DIIMA – Dipartimento di Ingegneria dell’Informazione e Matematica Applicata, University of Salerno, Via Ponte don Melillo, Fisciano 84084, SA, Italy Fax: +39-089-964-191 E-mail: gaeta@inisa.it

Pierluigi Ritrovato CRMPA – Centro di Ricerca in Matematica Pura ed Applicata, c/o DIIMA – University of Salerno, Via Ponte don Melillo, Fisciano 84084, SA, Italy Fax: +39-089-964-191 E-mail: ritrovato@crmpa.unisa.it

Abstract: This paper is a technological development and realisation of the e-learning model presented by Albano G., Gaeta M. and Salerno S. (AGS) which appeared in the previous issue of IJKL, focused on the advanced features provided by Intelligent Web Teacher (IWT) an innovative learning platform available in the market. In this paper, the approaches used in IWT for knowledge representation and management through ontologies are examined in detail. Similarly, the way the special component Learner Intelligent Advisor (LIA) uses these and other information available on the system for creating personalised learning paths taking into account the single user preferences including present knowledge state and preferred didactic approach, etc. is carefully studied. IWT is in continuing evolution. In particular, we are improving LIA structure and algorithm and integrating IMS learning design for the description of our unit of learning. We paid particular attention to the issue of giving an insight into the main ideas and challenges underlying the solutions we chose.

Keywords: e-learning; learning model; technology enhanced learning; knowledge representation and ontology; intelligent tutoring systems.

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Reference to this paper should be made as follows: Albano, G., Gaeta, M. and Ritrovato, P. (2007) ‘IWT: an innovative solution for AGS e-learning model’, Int. J. Knowledge and Learning, Vol. 3, Nos. 2/3, pp.209–224.

Biographical notes: Giovannina Albano is an Assistant Professor at the Department of Information Engineering and Applied Mathematics, University of Salerno (Italy), since 1997 and for a long time she is a teacher for several courses at the Faculty of Engineering. Her research interests are in didactic models for e-learning environment. She is jointly responsible of the innovative series MoMAMath for the mathematics learning. She is an IEEE member. She is an Italian representative at the Working Party for Learning Policies within IST Programme and V Framework Programme. She is a Member of the project Centre of Excellence ‘Methods and Systems for the learning and knowledge’, approved and financed by the MURST, singular in Italy on such theme. Within the Centre of Excellence she is responsible of the research line ‘Virtual Scientific Experiments’. Besides she is the Vice-Project Coordinator of the Centre of Excellence project.

Matteo Gaeta is an Associate Professor for Information Processing Systems at the Engineering Faculty of the University of Salerno. His research interests include technologies and architectures for designing advanced innovative e-learning environment. He is the General Manager of the Centre of Excellence on Learning and Knowledge of the University of Salerno and is the Member of the Technical Committee for the Minister of Productive Activities. He is also the Member of the Steering Committee of the Ministry of Agricultural and Forest Policies for the consultancy and the scientific, organisational support to research, development and innovations.

Pierluigi Ritrovato received a BS in Computer Science at the University of Salerno in 1992. At present he is an Assistant Professor in Engineering and Computer Science at University of Salerno and Responsible for International Research Cooperation and Technology Innovations for the CRMPA. In the last five years, he has been focusing his scientific research on Distributed Learning Management Systems and Grid technologies. He is the Chair of the Service Orchestration, SLA usage and Workflow – Technical Group for the EU Software Services and Infrastructure Unit. He is the Editor of the book Towards the Learning Grid: Advances in Human Learning Services.

1 Introduction

In a recent work (Albano et al., 2006), an original e-learning model and a related conceptual solution are presented and described in depth, pointing out the most innovative features with respect to the dominating learning paradigm based on the information transfer and to widespread learning management system available on the market. Also, Intelligent Web Teacher (IWT) platform is mentioned as able to put in practice the main ideas and distinctive features of the conceptual solution considered, listing briefly the competitive advantages from a technical point of view.

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In this paper, building on the work quoted before, we aim to focus our attention on IWT platform giving a more detailed and technical description, including also issues under development.

This paper is organised as follows:

• Section 2 is devoted to knowledge, contents and operational models issues, with particular attention to ontologies, authoring tools, learner and didactic models.

• Section 3 concerns the process of intelligent dynamic construction of learning units, based on Learning intelligent Advisor (LIA) algorithm and tool. Related aspects concerning knowledge domain representation, like macro-ontology, generic contextualised ontology, specific personalised ontology are also discussed.

• Section 4 describes architecture and deployment model of IWT, essentially on a conceptual level, but also with some more technological details.

• Section 5 briefly presents the most relevant aspects under development, like multimedia personalised Learning Objects (LO) design, the introduction of learning experience as a more structured complete learning path with respect to previous unit of learning, according to general indications of didactic model (namely, on the macrolevel), high level access methods and functionalities, problem-oriented learning path in addition to concept oriented one.

• Finally in Section 6, we discuss on some conclusion.

We take the pleasure to acknowledge the contribution and generous help offered to us by Prof. Saverio Salerno and Dr. Francesco Orciuoli.

2 Tools for unit of learning creation

The current version of the IWT platform provides functionalities for knowledge model representation as illustrated in Figure 1. Specifically, the IWT platform’s users (namely experts and teachers) may interact with macro-ontologies, LO and related metadata, student’s models and a simplified interpretation of the didactic models. Each user platform has its own repository for storing resources (i.e. LO, ontologies, documents, etc.) that can be shared with other users.

Before going into details about the capability provided by IWT, it is worth to mention that all the functionalities described in the following are available both online (though the Portal) or off-line using a specific application named Knowledge Representation Tool (KRT). KRT also allows to load the resources and related metadata into an internal repository and upload/synchronise it with the IWT online repository at any time.

2.1 Knowledge representation tool

IWT allows the experts to define and structure disciplinary domains, by constructing domain dictionaries, composed by a list of terms representing the relevant concepts

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of the disciplinary domain that we are modelling and constructing some macro-ontologies on such dictionaries that are modelled using graphs structure. The graph nodes are concepts (taken from the chosen dictionary) and the arches are the relations between the concepts (Figure 1). The relations can be of different types but refer, essentially, to two classes that are decomposition and order. As for the representation of macro-ontologies, IWT makes uses of standard description languages such as SHOE (DAML+Oil, 2001,next version of ontology management tool will support OWL). The domain experts will not be forced to learn such languages (that are only used to keep an internal representation of the data structures and to facilitate their export to/import from other systems) but will have the opportunity to use user-friendly visual tools for the design of macro-ontologies.

Figure 1 Macro-ontology in IWT

2.2 LO creation and annotation

IWT provides a set of authoring tools to create LO or to import them from both third party environments (like Microsoft Office suite – power point, Word and Producer) and from an already existing SCORM package using IMS-Content Packaging v1.1.3 (IMS-CP, 2003). In particular, it is possible to create multimedia LO (through the insertion of images, audio and video files) and multiple choice tests compliant with the IMS Question and Test Interoperability standard (IMS-QTI, 2004).

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The environment supports the creation of contents that link further material and that could be classified either in terms of difficulties or purposes (i.e. extending materials) (Figure 2).

Figure 2 Creation of a didactic resource in IWT

All the learning resources (including the tests) have to be annotated using IMS Learning Resource Metadata standard version 1.2.1 (IMS-LRM, 2004). Also for the input of the metadata IWT provides specific tools that facilitate the input operations. Through IMS-LRM are provided information such as the interactivity level, the semantic density, the difficulty and typology of the LO as described in Figure 3. Moreover, IWT requires the user who is dealing with building the metadata for a specific LO, to specify a concept (or more than one) inside a domain which the content of the LO is referred in Figure 5. In this way, it is possible to link the LO to the concepts of the macro-ontologies, indeed, by associating a LO with one or more concepts, we can assume that the content of such LO ‘explains’ the correlated concepts. After constructing and describing through metadata a LO, the teacher can store it in a private repository provided by IWT. Using the resource sharing service, he/she can use the LOs and the ontologies built by other teachers who will have to give the suitable permissions.

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Figure 3 Metadata for resources in IWT

2.3 The student modelling

IWT guarantees the possibility to model the student storing, what knowledge acquired during the learning process and retrieve, automatically, the learner’s preferences concerning the typologies of contents, the didactic approach, the interaction level, the semantic density, etc. (Figure 4). Also for the student’s model, IWT adopts the IMS-Learner Information Package (IMS-LIP, 2005) standard that, besides keeping the usual information on the acquired knowledge and on the preferences, also allows to store demographic and biographic information. The use of IMS-LIP allows, also, the users–learners of the IWT platform to export their own profile and let them recognise by other software platforms adopting the same standard or, vice versa, to allow importing into IWT the learner’s profile already registered on other e-learning systems.

2.4 Didactic model representation

Actually, IWT uses a simplified representation of the didactic models that are associated to specific typologies of LO. A LO whose content is a simulation, is associated with an inductive didactic model, while a LO of textual type is typically associated with a deductive didactic model. The information on the didactic model (as we meant it) are stored using once again the metadata associated with the LO and it will be also possible to indicate, inside the student’s model, the preferred didactic model, making use of some attributes within the preferences section of IMS-LIP. This vision will be extended in the next IWT versions as illustrated in Section 5.

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Figure 4 Learner profile in IWT

3 Unit of learning intelligent building

Now, starting from what is said above, we will see how IWT manages the creation of a personalised Unit of Learning. The process of construction of the personalised Unit of Learning is activated by a teacher who decides to create a didactic course.

3.1 Fix the learning objective: target concept selection

The teacher will select a macro-ontology among those available and will select, through the visual tool, the target concepts for his/her course (Figure 5 – the bold nodes indicates the target concepts). Eventually, he/she will choose to add some milestones during the course, for instance half-way or end course milestones, an entry test on the subjects of the course itself. The information provided by the teacher, in the framework of the course construction, will be named with the course specifications.

3.2 The course publishing

After the target concept selection, the course will be published and the students will be given the possibility to access after an enrolment request and its approval by the didactic secretariat or the teacher himself/herself. The course is composed by specifications only and not of the real contents. Indeed, it includes the target concepts and where only the milestones will be placed. The contents will be assembled at runtime when the student will ask to access to the course the first time according to his/her preferences.

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Figure 5 Selection of target concepts in IWT

3.3 The personalised UoL creation

When student access to the course for the first time, IWT will invoke a software module named Learner Intelligent Advisor (LIA) (Capuano et al., 2002, 2003) able to automatically generate for each student the best possible learning path according to the information available in the student model, to the course specifications and to the LO available in the repository. In particular, the first operation executed by LIA is to use the macro-ontology and the target concepts, chosen by the teacher, to create a learning path, namely an ordered sequence of elementary concepts (leaf of the ontology graph). The list is organised according to the order and decomposition relations indicated in the macro-ontology. In particular, the selected target concepts will be preceded by all the concepts linked to them with a IsRequredBy relation (order). The second operation which LIA executes will be to eliminate from the learning path those concepts already known to that given student. The previous knowledge of a student is inferred by LIA through the student’s model. The new path obtained is a personalised learning path. The last operation executed by LIA is the selection and composition of a sequence of suitable LO (that will be the personalised Unit of Learning) able to teach all the concepts present in the personalised learning path. LIA has to provide, to each concept of the path, a LO chosen inside the repository, thanks to the association of concepts and contents given by the metadata fields. In general, within a repository more LO are able to explain

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the same concept will exist, LIA will choose the one which better meet the preferences of a given learner and will also take into account the chosen didactic model. We want to point out that the last two operations executed by LIA implicitly make use of a Personalised Specific Ontology (PSO). IWT will play the LO sequencing assembled by LIA, supplying the students with an environment (Figure 6) where they can study the proposed content and in which to collaborate with other students and with the tutor.

Figure 6 IWT delivery environment

3.4 An elementary example in the area of calculus

Figure 7 provide an example, summarising all the steps for the construction of a personalised Unit of Learning, starting from a macro-ontology on the elementary concepts of Mathematics (addition, subtraction, multiplication and division). The illustrated macro-ontology has two order relations called ‘Is Required By’ (prerequisite) and ‘Suggested Order’, and a decomposition relation called ‘Has Part’. It is supposed that the repository of the IWT platform already contains some LO that can ‘explain’the concepts of the macro-ontology. In particular the mul concept is ‘explained’ by two LO of difference typologies such as ‘textual explanation’ and ‘simulation’. The teacher has chosen to construct a course having as its objective the target concept

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‘div’. At the first fruition by any student, LIA create a learning path and then refines it using the ‘acquired knowledge’ of the student himself/herself. In our example, the student already knows the sum concept that is eliminated by the learning path and obtain a personalised learning path. The last step is the construction of the learning path sequencing. LIA searches the right LO for each concept. As for the mul concept, we have the possibility to choose between the LO 3 and 4. LIA will choose the LO 4, being constituted by a simulation that we have associated with the inductive didactic model which results to be the preferred didactic model by the student (information indicated in the student’s model).

Figure 7 Unit of learning building in IWT

4 Architecture and technological features of IWT

Every disciplinary domain is characterised by a set of applications supporting and facilitating the formative process. There exist, moreover, some e-learning applications that, putting aside the disciplinary domain, are able to be used transversally in more didactic/formative contexts and some applications that are characterised by the contents typologies used (for instance the simulations, the virtual scientific experiments, etc.). IWT takes into account these considerations and offers a Base Platform that put at disposal a set of domain-independent resources and services. Furthermore, for each domain will be arranged a set of domain-specific resources and services that will construct what we call Domain Platform and that is architecturally collocated ‘over’ the Base Platform.

From the technological point of view IWT originates from the consideration that each didactic/formative context needs its own specific e-learning solution. It is not thinkable to use the same application to teach, for instance, foreign languages at the primary

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schools and mathematical analysis at the universities. The first thing varying would be the content, but, together with it, the didactic model will change (which could be of inductive-experiential type as for the first case and deductive type in the second case), the typology of the formative modules to use, the application layout and the set of related tools (e.g. a text-to-speech engine in the first case and an engine for the mathematical-symbolic calculation in the second one). From a technical point of view and in the light of the previous considerations, the necessity of introducing an e-learning system into a new didactic/formative context is translated in work for analysts, designers and programmers that must create every time a new complementary application. To avoid all this, the IWT architecture has been designed to support, in a native way, feature of extensibility. The IWT architecture supports the extensibility, either at the level of typologies of contents to manage or considering the functionalities to offer, using a paradigm based on variances of some design patterns. From the contents viewpoint, it is possible to add a support to a new LO typology by implementing and installing, inside the IWT system, a Driver that manages its creation, delivery, modification, deletion and possible feedback by the users. From the point of view of the functionalities, once again thanks to the adoption of variation of some known design patterns, IWT allows to add new services written ad hoc after the implementation of suitable modules named Plug-Ins that are compliant with the specifications of the IWT system. Finally, through the implementation of particular typologies of Drivers called Plug-In Drivers, it is possible to realise the wrapping of external engines (as it can be for instance the Mathematica kernel), realised by third parties, all necessary for the correct functioning of advanced contents. The management and usage of all the aforesaid extension modules is realised in a transparent way by some integrated services within the IWT platform.

5 Further developments in progress

Concerning the features of the knowledge model, as illustrated in (Albano et al., 2006) and do not yet implemented in the IWT platform, we are developing solutions and components for the context representation (which describes a student classroom addressee of the Unit of Learning we have to build) and Contextualised Generic Ontology (CGO) which represent ontologies aimed at a particular context. Besides, we are working at the formalisation of the transformation rules of a macro-ontology in a CGO. Finally, we are studying the Didactic Models software implementation and, in particular, how the algorithms for the selection of LO (present in LIA) and for their sequencing are influenced by the chosen didactic model. Some details about the work under development are presented.

5.1 Context and CGO modelling

The context will be represented with data which inform about the student’s characteristics (average cognitive state and average quality level); the objective of the educational process (theoretical or applied knowledge and know-how) and student’s needs (typology and duration of the course, fruition modes, devices used for the

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fruition, etc.). The contexts could be prepared by the teachers thanks to a tool, accessible inside the IWT platform and will be stored as resources within IWT so to allow their reuse.

The CGO will keep the same base structure of the macro-ontology but will bring with itself some metadata (difficulty level, content typology, didactic method, etc.) that will describe one or more families of concepts. The choice to store the additional information derives from the need to differentiate, in some cases, some families of concept from other existing in the same ontology. The following example helps to clarify this aspect. The CGO is obtained by a semi-automatic process that has as an input a macro-ontology. The first step of the process is automatic and allows, starting from a chosen context, to create a set of values to associate with metadata fields and valid for all the concepts of the CGO (e.g. difficulty degree, didactic model and method, etc.). The second step is instead realised by the teacher (thanks to the help of a visual tool) who selects the families of concepts and modifies the metadata for such families of concepts. The metadata, differentiated according to the families of concept, will be used by LIA when we need to realise the association of real LO with concepts extracted from the CGO and that have become now part of the personalised learning path.

In the figures below, we show an example of different CGO w.r.t. different contexts starting from a macro-ontology for Linear Algebra (Figure 8). According to the context, some nodes and relations can be skipped or substituted with others and some metadata values (elementary concept with metadata) can be changed for one or more concepts. The GCO represented in Figures 9–11 can be automatically built by the system taking into account the context (in order to fix the Generic Metadata used by all the concepts) and Cognitive State of the learner to delete some nodes and relations that can be assumed already learned.

Figure 8 Macro-ontology for Linear Algebra

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Figure 9 Example of GCO

Figure 10 Example of GCO

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Figure 11 Example of GCO

5.2 LIA improvements

A Didactic Model is implemented by one or more didactic methods. The didactic methods can be described using the IMS-Learning Design specifications (IMS-LD, 2003) which offers a language for the description of activities workflow used to define learning processes suitable to specific didactic methods. These workflows include a set of acts in which every student personifies a specific role. The language allows defining the acts inside particular environments composed by sets of LO and services (forum, chat, calendar, virtual classroom, access to maths engines, etc.). Our idea is to associate the didactic methods (classified in didactic models) with prearranged learning design scenarios. In the phase of the Unit of Learning building and particularly when we have to associate the LO with a family of concepts within the GCO, according to a specific didactic method, LIA will have to recover the learning design scenario associated to the didactic method indicated by that family of concepts (thanks to the metadata of GCO) and fill it with the right LO. The Unit of Learning will not be anymore an ordered list of LO, but a sequence of learning activities orchestrated within a whole learning design scenario. Unfortunately, in a scenario described according to the IMS-LD specifications, the environment for the LO provision must be populated at the design-time and not at the run-time as our model would require. This led us in investigating how to extend IMS-LD in order to allow the binding with the LO or services at the run-time, starting from a formal specification of the expected output (behaviour) provided at design-time. This means that at run-time the information available are ‘interpreted’ by a specific engine that using the information about the context, the learner’s knowledge state, the didactic method, etc. will select the more suitable resources available (contents, services, applications, etc.) as presented by Orciuoli (2005) and Capuano et al. (2005).

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5.3 Learning path personalisation: new access methods to contents

We are also working on the development of new functionalities for facilitating the access to learning resources to the learners hiding and avoiding the use of ontologies (of concepts) and providing them the possibility to create a course in a declarative form also for supporting problem-based learning. The idea is to use a Case-Based Reasoning (CBR) engine that will provide an interface for course creation where the student simply provide, in natural language a statement of the problem (case) that he wants to learn to solve using IWT. At each case it has been associated a set of concepts to be learned. The CBR will search in the case base for the problem, in order to find a suitable one according to the value of a similarity function, also taking advantage of a suitable decomposition based on a hierarchical structure for the problems representation. Alternatively, it will provide the list of target concepts to a specific engine in order to generate a suitable learning path (course).

6 Conclusions

In this paper, we presented some technical and technological solutions used in a commercial learning platform (IWT) in order to allow the adoption of a learning model in the e-learning processes moving a substantial step ahead with respect to the information transfer paradigm commonly used in the e-learning practice. We paid particular attention to the issue of giving an insight of the main ideas and challenges underlying the solutions we chose. Further developments from both the methodological and technological point of view are already in place and some of these efforts (especially the methodological one) are carried out in the frame of relevant national and European Research projects such ELeGI – European Learning Grid infrastructure FP6 Integrated Project (www.elegi.org), Sim-Learning and Learning and Knowledge national projects.

References

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Capuano, N., Gaeta, M., Micarelli, A. and Sangineto, E. (2002) ‘An integrated architecture for automatic course generation’, Proceedings of the IEEE International Conference on Advanced Learning Technologies ICALT 2002, Kazan, Russia.

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IMS Learning Resource Meta-data Specification (2004) Available at: http://www.imsglobal.org/ metadata/index.cfm.

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Orciuoli, F. (2005) ‘Building personalised and ontology-based UoLs with IMS Learning Design’, Presentation at the UNFOLD Communities of Practice Meeting, Braga Campus of the University of Minho Portugal, 15–17 June 2005, Available at: http://www.unfold-project.net/about_folder/events/cops/portugal/report2.