Bridging the Gap between ITS and eLearning: towards Learning Knowledge Objects Amal Zouaq1, Roger Nkambou2, and Claude Frasson1 2
1 University of Montreal, CP 6128, succ Centre-Ville, Montreal, QC, H3C3J7 University of Quebec at Montreal, CP 8888, succ Centre-Ville, Montreal, QC, H3C3P8 {zouaq, frasson}@iro.umontreal.ca
[email protected]
Abstract. This paper presents an ontology-based approach for the dynamic generation of learning knowledge objects (LKO). These objects have the particularity of implementing AIED techniques and they exhibit characteristics hitherto reserved for intelligent tutoring systems (ITS): they are knowledgebased, adapted to a learner model and they are composed dynamically according to a learning need and a particular instructional theory. Additionally, the paper tackles the issue of using eLearning resources by ITS and shows how this could be realized. Finally, the paper also demonstrates how LKOs are deployed and evaluates briefly the results of the proposed approach. Keywords: Learning Knowledge Objects, ontologies, AIED, eLearning.
1 Introduction In the context of eLearning, educational material provides access to many knowledge sources. However, there are several shortcomings with this kind of webbased education. First, the huge number of learning resources makes it difficult to find the appropriate resource to fulfill a learning need. The main burden of indexing, storing, extracting and organizing the learning material remains on human’s shoulders [6]. The creation and standardization of metadata offers structuring and indexing, but again this task has to be performed by a human expert. Moreover there are several standardized metadata formats and they require a matching process to be used in a uniform way. Finally, an important shortcoming of educational material is that it cannot be exploited by ITS due to its lack of model. To alleviate the user’s tasks and provide access to better formalized knowledge structures, there is a need to set up (semi)automatic mechanisms to mine the learning object content. There is also an urgent need of rethinking the notion of learning object. From one side, learning object has to evolve to become more appropriate for the current needs of eLearning: adaptation, on-the-fly composition, knowledge modeling, instructional theory modeling and learning objective statement. On the other side, ITS should no longer neglect learning objects as potential knowledge sources. This paper presents the results of “The Knowledge Puzzle Project” (KPP) in its effort to offer an alternative to static learning objects by generating dynamically
learning resources called “Learning Knowledge Object” (LKO). These LKOs have several characteristics: they are knowledge-based, theory-aware [18], dynamically generated and they provide a tutoring service interface. Therefore, they draw several characteristics from both AIED and eLearning communities, hence contributing to bridging the gap between them [3]. The first part of this paper (section 2) discusses the current landscape of eLearning and AIED communities. It emphasizes on the current issues facing web-based education and initiates a reflection over the impact of ITS modules on the learning object concept and vice-versa. Section 3 describes a general semantic Web architecture for the on-the-fly generation of LKOs that benefit from the AIED techniques to model the domain knowledge, adapt their content to a given learner and encode instructional theories in a declarative way. Finally, section 4 presents an evaluation of the approach before a conclusion.
2 Computer-based Instruction Landscape Computer-based education is mainly divided into two communities: eLearning and AIED. ELearning has been widely adopted by organizations whereas AIED techniques are still generally confined to research projects in the universities. 2.1. ELearning Systems Landscape Since eLearning relies heavily on web-based resources and on the web as the training platform, we have seen, over the past few years, the proliferation of learning objects in repositories and their wide adoption in the industrial world. This success is also the result of the failure of more sophisticated techniques such as intelligent systems and artificial intelligence in education in general to take place on a wide scale. However, there are many problems in the eLearning vision [4, 17]: • The metadata problem: Metadata is used for the description and research of learning objects. The first problem is that metadata are designed with the idea that they are intended to humans and produced by humans [3]. The second problem is that these metadata, while necessary, are not sufficient. In fact, they describe the world around the learning object (the language used, the context of use, the author, etc.) but they remain very vague about its content. • The black-box problem: A learning object is a black box: it is presented as an integrated content package containing all the necessary resources but its real content is inaccessible to a software agent. • The lack of explicit instructional theory problem: Learning objects implement an instructional theory provided by their designer. However, this theory remains implicit and learning objects are therefore dependent on this theory and cannot modify it dynamically. • The lack of adaptability problem: Finally, learning objects are suffering from their static model and the lack of adaptation of their content to a learner model. This is why various researches focused on this issue and has led to the adoption of various
standards for modeling the learner, such as IMS LIP (Learner Information Package) [12], or IMS ePortfolio [11]. The creation of teaching scenarios standards such as IMS-LD (IMS Learning Design) [13] is also another effort to formalize the pedagogical aspect and adapt it to a given learner. 2.2. Intelligent Tutoring Systems Landscape ITS rely on artificial intelligence techniques to improve the processes of computerbased teaching and learning. Typically, a traditional ITS architecture uses various knowledge representation techniques. One of the main problems of ITS is the lack of an agreed standard among the AIED community regarding domain modeling, learner modeling, and instructional scenario modeling. This lack of standardization is felt in the different modules of an ITS which must be rebuilt from scratch in every project. It is also clearly felt in the knowledge representations themselves: various representations, techniques and languages are used without clear distinction about when any of these formalisms should be used instead of another. The domain model is specially a heavy burden over the community shoulders when built manually, which is often the case. Finally, with the growing number of learning objects and their availability on a large scale, ITS should benefit from these learning resources and should find a way to integrate them into their knowledge base.
3 Implementing the Semantic Web Architecture: The Knowledge Puzzle Experience To overcome the limitations mentioned above, the research community has gradually refocused on the need to create more complex representations than simple content aggregations, the need to adapt training to individual learners and the need of knowledge representation and reasoning mechanisms able to exploit the knowledge base. The educational Semantic Web [1, 5, 17] represents a way to integrate all these efforts in a common architecture. In the Knowledge Puzzle Project (KPP), we propose a Semantic Web Architecture which aims at formalizing the different models of the traditional ITS architecture using ontologies and at offering a set of services that exploit the ontological model. Another goal of the project is to provide tools to support the dynamic composition of learning resources that act themselves as small tutoring systems: they possess a domain model, a learner model, and a tutor model. They also exhibit different characteristics: they are active, domain-knowledgeable, independent, reusable and theory-aware [18]. We believe that this architecture makes the synergy of the strengths in the eLearning and the ITS field. First, it uses OWL as a standard language for knowledge representation and reasoning. Second it advocates the use of tutoring services to dynamically create learning objects implementing the traditional ITS architecture.
Contrary to classical eLearning approaches where learning objects repositories are considered as suppliers of learning objects, KPP exploits these repositories (or any kind of documents about the domain of interest) as raw materials for creating learning content. A number of tools and services are implemented to make use of these resources to produce domain knowledge and to annotate relevant items according to different contexts (domain, instructional roles, competences, instructional theories). These different contexts are explained below and organized around the three models of an ITS: the domain model, the learner model and the tutor model. The following figure shows the KPP semantic web architecture. On the left, the different tools that feed the knowledge base are represented (Onto-Author, TEXCOMON). On the right, the exploitation of the knowledge base is shown through the deployment of LKOs in eLearning environments and ITS. ONTO-AUTHOR DEPLOYMENT
Knowledge Anotator
Competence Editor
SWRL Rule Editor
Theory Editor
IMS-LD Player
SCORM RTE
LKO RTE
OWL
OWL JAVA API
i STANDARDIZATION
Knowledge Base
i n d e x
CMPNeeds
DOMCMAP
Assets
Competences
Skills
ILT-Theories
IMS EPorfolio
Rules
LKO 2IMS
LKO2 SCORM
COMPOSITION
Data State CGA
IPG
Functions
Instruc tional Theory
DOMONTO DOCONTO
IROONTO
ITS integrates the LKO RTE (Load the LKO applet into HTML)
CMPONTO ILT-ONTO
ITS uses the knowledge base directly
Intelligent Tutoring System
Legend: OWL JAVA API TEXCOMON
OWL PROTÉGÉ
Documents ONTO-ENGINE
Fig.1. Overview of the KPP Semantic Web Architecture
DOC-ONTO: Document ontology DOM-CMAP: Domain Concept Maps DOM-ONTO: Domain Ontology IRO-ONTO: Instructional Role Ontology CMP-ONTO: Competence Ontology ILT-Theories: Instructional Learning Theory Ontology CGA: Competence Gap Analyzer IPG: Instructional Plan Generator
3.1. Setting up the knowledge base The knowledge base is represented as a organized around the domain ontology. ontology, the instructional role ontology, Ontology instances are created through two ONTO-AUTHOR.
set of intertwined ontologies that are These ontologies are the competence and the instructional theory ontology. authoring suite tools: TEXCOMON and
3.1.1. Setting up the Domain Model In KPP, the domain model is represented by a domain ontology and an ontology of instructional roles to model the educational content. A domain ontology is used to formally describe a domain. Most of the works [8, 13] have considered first building a domain ontology from scratch and then annotating the learning objects with relevant ontological concepts. However, due to the difficulty and cost of such a manual operation, the adoption of automatic methods for extracting the domain knowledge is now acknowledged within ontology engineering communities. The domain ontology is generated semi-automatically through an ontology learning tool (The TEXCOMON Tool) that formalizes the domain knowledge as a set of concepts (most of them are primitive classes), individuals, concept attributes and relationships between concepts. The process of learning this domain ontology has been described elsewhere [23]. Without repeating its description, it is important at this stage to underline the benefit gained from the employed methodology. In fact, one of the main advantages of our approach is the resulting three-layer model that allows for the representation of text content through concept maps and the refining of these concepts maps into more formal domain ontology. Here, refining means the identification of the most important domain concepts according to the input documents. This importance is measured by the connection degree of a given concept with the others (the out-degree parameter). The domain model is thus represented by three structures in ascending order of abstraction: the texts, the concept maps and the ontology. It is then easy to navigate from one level to the other by searching a concept map around a given ontological concept and then retrieving text portions relevant to this map and vice versa (starting from a text to know the relevant domain concepts and relationships). In the last case, the domain ontology “automatically” reflects the domain knowledge covered by the input documents (or at least a part of it) and automatically references this knowledge. This is an interesting feature that helps the search of concepts or particular relationships between concepts by providing finegrained resources (at the sentence-level, paragraph level, or whole text level). Course designers (human, software agents) as well as learners benefit both from these structures respectively to build a course or to understand a domain in an exploratory way. The instructional role ontology represents instructional roles widely used in education such as definitions, examples, descriptions, exercises, and so on. These roles are related to the domain ontology through an OWLObjectProperty “concept” that identifies the referred topic. Instructional roles allow for the identification of
reusable pedagogical knowledge fragments in learning objects or documents. These fragments are needed for the establishment of an automatic generation process of learning resources. Hence, they sustain the reusability of fine-grained learning resources, which is one of the main aims of eLearning.
3.1.2. Setting up the Learner Model In ITS, the learner model is the key aspect that allows for the adaptation of instruction. We believe that the learner model should be expressed in an ontology in order to offer a better sharing of the model between training environments. For example, Dolog and Nejdl [7] have proposed a user model ontology based on IEEE Personal and Private Information (PAPI) and IMS Learner Information Package (LIP). Another ontology based on an RDF Schema is presented in TANGRAM [14]. An additional specification is emerging as a way to model learner knowledge over a long time period: the IMS ePortfolio specification [11]. This specification has been used in [9] as a way to initialize the learner model and to conserve the trace of mastered competencies. Since the adoption of this specification is rapidly growing in higher education and is also suitable for organizations, we propose to build an ePortfolio ontology to represent the learner model. In KPP, the learner model is expressed as a set of skills (where one or more skills describe a competence) to acquire over domain concepts through an OWLObjectProperty “concept”. The terminology adopted (the Bloom taxonomy [2]) to describe the skills is independent from the domain model (example of skill: define, analyze …) to enable its reusability and an easier sharing. This ontology stores the mastered skills of a given learner over domain knowledge.
3.1.3. Setting up the Tutor Model One of the other drawbacks that face ITS is their inability to model instructional theories and strategies in a standard and reusable manner. The instructional strategies are generally encoded in the ITS program and are then very difficult to update and reuse. Some efforts and reflections have been made to remedy the situation. Hayashi et al. [10] present a framework that gives theoretical justification to standardcompliant learning/instructional scenarios. There were also some efforts to encode instructional strategies in the form of rules by extending the SWRL standard [21]. In KPP, the tutor model has an ontology of instructional theories which allows it to present the same content with different teaching strategies. Each instructional theory is represented as a set of instructional steps. Each instructional step is fulfilled either through the presentation of an instructional role or through the execution of actions that are meant to represent basic tutor operations such as computing the learner score or generating or loading exercises. For expressing this link between a particular step and a given instructional role or a given instructional action, an instructional step is linked to a set of SWRL rules. Given a particular theory, these rules express the kind of processing the tutor should make at a particular step of the instruction (present a definition, generate an exercise, etc.). It is then relatively easy
for a designer to edit or add new rules or create ad-hoc rules. For the moment, we still use the SWRL editor provided by the Protégé Environment but easier ways to edit instructional rules are currently undertook. Other initiatives regarding ontology-based instructional theory modeling are established by the research community such as the OMNIBUS project [17]. Since KPP instructional theory model is very simple, the final aim would be to integrate a richer ontology such as OMNIBUS instead of the one we currently use. The ONTO-AUTHOR tool suite [22] is used to edit all the ontology instances. It comprises functionalities such as competence edition, instructional role annotation, instructional theory edition, etc. It allows establishing the pedagogical, structural, domain and competence relationships between the contents. The ONTO-AUTHOR tool suite uses the Protégé OWL API to communicate with the ontology layer. 3.2. Exploiting the knowledge base As previously mentioned, KPP advocates a service-oriented architecture to exploit the knowledge base. Once the different models are set up, it is possible to organize a set of services to effectively use the KB. Other projects have recognized the importance of the service-based vision in contrast with the resource-based vision such as the TELOS architecture [19] and the LUISA project [15]. In the same line of thought, KPP proposes the replacement of LORs by knowledge bases which should be exploitable by LCMS and ITS. One difference in the KPP vision is that we also consider a learning resource as a small knowledge base surrounded by services (the LKO). In fact, setting up the knowledge base as described above aims at providing the required resources to automatically generate LKOs. These LKOs can be usable by eLearning environment as well as by ITS. The LKO generation process is composed of three layers represented as services: the composition service, the deployment service and the standardization service (see fig.1, right side).
3.2.1. The Composition Service The composition service is in charge of aggregating learning Knowledge Objects according to a given instructional theory, a specific competence need (CMP-Needs) and a specific learner model (IMS e-Portfolio). Once learning objectives are specified, the competence definition is matched against the learner profile using a Competence Gap Analyzer (CGA). For each skill in the targeted competence, the learner profile is checked to find out if this skill is already mastered or if required prerequisites have to be added. If this is the case then adjustments are made to the competence definition leading to a set of skills that are new to the learner (Adjusted Competence). The Adjusted Competence is transferred to an Instructional Plan Generator (IPG) that is in charge of composing the actual Learning Knowledge Object according to the chosen theory. The IPG exploits the Instructional Learning Theory Ontology in order to find out the instructional events and conditions that will effectively guide the composition. Actually, KPP supports the execution of SWRL rules using the Jess rule
engine. As previously indicated, the execution of the rules indicates to the IPG the type of resources that should be used to fulfill each step or it indicates a type of action that should be performed. Each action described in the rules is implemented as a java function (for example: generate an exercise related to a concept X). Since an LKO is generated on-the-fly according to a particular training need, it requires, at generation time, an ontological data structure to store relevant instances and resources. This data structure is represented by the LKO ontology. The instructional Plan Generator fills the LKO ontology with the required resources.
3.2.2. An Ontology for representing Data States in Learning Knowledge Objects The execution of the IPG produces a Learning Knowledge Object that is composed of a data state and of a set of functions to manipulate it. An LKO defines learning scenarios through topics from the domain ontology based on the definition of competences as learning objectives and guided by an instructional theory. The data state is an OWL data structure composed of the various resources necessary for the LKO. An LKO is represented by a set of LKOActivities (CourseActivity or ExerciseActivity). Each activity is targeted towards a specific skill and is linked to the skill concept. Each concept is in turn related to a set of LKOSemanticRelations that define the concept map around the concept (its context). Each LKOActivity is composed of a set of LKOSteps that are conformant to a specific instructional theory. Each of the steps requires a resource to fulfill it. The LKO functions are assembled into a standard interface that enables any LKO to act as a small “Intelligent Tutoring System”. This standard interface offers a number of functions that are further described in the deployment service.
3.2.3. The Deployment Service The LKOs are targeted towards any kind of training environment. KPP provides an LKO Runtime Environment (LKO-RTE). The LKO-RTE makes it possible to run the LKO as a standalone resource. The user interface gives access to relevant functions that are implemented within the LKO to support the variety of learning services (functions) described below. Scenario control is set up by following the instructional theory that was used to generate the Learning Knowledge Object. Basically the LKO Runtime Environment is composed of a course view, a concept map view and concept map exploration view. One deficiency of current textual learning objects is that they only foster one kind of learning. KPP’s aim is to offer some kind of constructivist environment using the learning object content. Each concept related to the skills that compose the learning knowledge object is presented with its context. The concept map view shows the context of these different concepts and the relationships between them. The concept map exploration enables a deeper understanding of the concepts at hand. In fact, it enables to explore the concepts connected to the current competence and it also allows exploring the surrounding concepts. Another possibility is to explore the different instructional roles related to a concept as well as their source
documents. This can be helpful for a learner as well as for a course designer, because it gives a quick view of the available resources. Besides concept map exploration, the learner is offered with exercises to test his understanding regarding the presented concepts. An exercise can be any learning resource that is edited in the ONTO-AUTHOR Tool Suite. Single or multiple choice exercises can also be generated by exploiting the concept maps. Here an exercise is defined as a set of right and erroneous assertions where the learner should select the right ones. The right assertions (relationships between concepts) come from the domain model. The erroneous ones are automatically created by exchanging relationships labels or concepts labels with other ones from the domain model that are not appropriate to the current learning situation. Finally, at any time, the learner is offered with the possibility of asking for the generation of a new learning knowledge object related to some concept of the context. For example, the learner can ask for the generation of an LKO about the concept of LMS (Learning Management System).
3.2.4. The Standardization Service The standardization layer serves as an interface to different standard eLearning environments and to ITS. For each eLearning standard (SCORM and IMS-LD), the same methodology is employed: Once the LKO is generated according to a particular instructional theory, it is possible to export the generated LKO ontology as an OWL File “lko.owl”. This file indicates the scenario to follow, the different resources related to each step and the concepts and their context. An LKO applet takes this OWL file as input to display the generated LKO. This applet can be packaged in a SCORM content package with other required resources. The same procedure is used for IMS-LD where an LKO is considered as an activity executable in a Learning Design Player [20]. As far as ITS are concerned, a different approach may be necessary. If the ITS relies on web-based resources such as HTML pages, it is then possible to launch the LKO applet as a resource to fulfill the given competence need. Proprietary ITS should also follow this methodology to be able to launch an LKO. Otherwise, a standard ontology-based ITS (what we call the LKO Runtime Environment) is required. This environment uses the ontologies as its knowledge base and exploits also standard services such as the ones described above. However, one may have noticed that an essential component in the ITS architecture is missing: the expert module. The actual knowledge base focuses on declarative knowledge. The expert procedural knowledge should then be manually added with usual techniques in the form of rules that come on top of the domain model.
4 Evaluating the Semantic Web Architecture We performed evaluations of the proposed approach by mainly focusing on the semantic validity of the domain concept maps and the domain ontology. In fact, since
the domain model represents the basis of the proposed framework, it is really important to ascertain the interest of the ontology learning techniques to produce relevant results. We completed a three-level analysis: a structural, semantic and comparative analysis. The structural analysis considers the domain ontology as a graph and focuses on structural properties such as the out-degree of a concept, the centrality of a concept, etc. We found that the concepts were generally richly described and had sufficient amount of parents. The semantic analysis relies on human experts to judge the validity of the ontology. On average, the two experts rated the pertinence of the generated ontology as follows: 86.65% for primitive classes, 84.3 for hierarchical relationships and 80.08 for conceptual relationships. Finally, we completed a comparative evaluation (on the same corpus) with the Text-To-Onto Tool [16] where the analysis has shown a significant improvement of results with KPP ontologies especially in conceptual relation learning, often considered as one of the weakest points of similar text mining platforms. An empirical evaluation of the LKOs is needed to complete these experiments. Our goal is to compare, on a given subject, LKOs and traditional learning objects with a set of learners. Moreover, the main part of our efforts has been devoted, until now, to the evaluation of the automatic extraction techniques. However, another concern is to evaluate the workload for the teaching staff to set up the knowledge base. In fact, since annotation and edition tools are very simple to use, we do believe that this should not take more effort (at least) than “traditional” platforms. From the domain knowledge side, it is important to underline that an important part of the work is done automatically but the ontology should still be validated and enriched, which requires some effort from the expert.
5 Conclusion We presented a semantic web architecture that allows the incorporation of AIED techniques in learning objects. We also introduced a new definition of learning objects: these resources should not be static content packages but programs able to reason over a small knowledge base. This new definition is concretized in the Learning Knowledge Objects that exhibit various characteristics and implement the three main models of ITS’ architecture: the domain model, the tutor model and the learner model. The architecture proposed here has many advantages: it lessens the burden of the manual creation of the domain model, which is one of the main obstacles to AIED, it allows guiding learners towards the right content, it clarifies the links between learning objects and between learning objects and domain concepts, and finally, it offers the ability to switch from an instructional theory to another by regenerating a new LKO. From the ITS side, we proposed a new vision based on the definition of the ITS architecture through ontologies. We also showed how ITS could exploit LKOs.
References 1. 2. 3. 4. 5. 6. 7. 8.
9.
10.
11. 12. 13. 14. 15. 16. 17.
18. 19.
20. 21. 22. 23.
Aroyo, L., & Dicheva, D. (2004). The New Challenges for E-learning: The Educational Semantic Web. Educational Technology & Society, 7 (4), 59-69. Bloom, B. (1956). Taxonomy of educational objectives: The classification of educational goals: Handbook I, cognitive domain. New York: Longman. Brooks, C. A., Greer, J. E., Melis, E., & Ullrich, C. (2006). Combining ITS and eLearning Technologies: Opportunities and Challenges. Proceedings of ITS (pp. 278-287). Springer. Brooks, C., McCalla, G., & Winter, M. (2005). Flexible Learning Object Metadata. Proceedings of SWEL 05) held in conjunction with AIED 2005, (pp. 1-8). Amsterdam. Devedzic, V. (2006). Semantic Web and Education. Berlin: Springer US. Devedzic, V. (2003). Key Issues in Next-Generation Web-Based Education. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 339-348. Dolog, P., & Nejdl, W. (2003). Challenges and Benefits of the Semantic Web for User Modeling. Proceedings of AH2003 workshop at 12th WWW Conference. Budapest. Gasevic, D., Jovanovic, J., & Devedzic, V. (2004). Ontologies for Creating Learning Object Content. Proceedings of the 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (pp. 284-291). Wellington: Springer. Guo, Z., & Greer, J. (2006). Electronic Portfolios as a Means for Initializing Learner Models for Adaptive Tutorials. In Innovative Approaches for Learning and Knowledge Sharing (pp. 482-487). Berlin / Heidelberg: Springer. Hayashi, Y., Bourdeau, J., & Mizoguchi, R. (2007). Standard-compliant Scenario Building with Theoretical Justification in a Theory-aware Authoring Tool. Proc. of AIED 2007), (pp. 37-44). Marina Del Rey. IMS ePortfolio Specification. (2007). Retrieved from : www.imsglobal.org/ep/index.html IMS LIP. (2001, 03). Retrieved from http://www.imsglobal.org/profiles/ IMS-LD. (2007). Retrieved from : http://www.imsglobal.org/learningdesign/index.html Jovanovic, J., Gasevic, D., & Devedzic, V. (2006b). Ontology-based Automatic Annotation of Learning Content. International Journal on Semantic Web and Information Systems, 2 (2), 91-119. LUISA. (2007). Retrieved march 10, 2008, from http://luisa.atosorigin.es/www/ Maedche, A., & Staab, S. (2001). Ontology Learning for the Semantic Web. IEEE Intelligent Systems, 16 (2), 72-79. Mizoguchi, R., Hayashi, Y. and Bourdeau, J.: Inside Theory-Aware Authoring System, Proc. of The Fifth International Workshop on Ontologies and Semantic Web for E-Learning (SWEL’07) , pp. 118, Marina del Rey, CA, USA, July. 9, 2007. Mizoguchi, R., & Bourdeau, J. (2000). Using Ontological Engineering to Overcome Common AIED Problems. Journal of Artificial Intelligence and Education, 11, 107-121. Paquette, G., Rosca, I., Mihaila, S., Masmoudi, A.: Telos, a service-oriented framework to support learning and knowledge management. In Pierre, S., ed.: E-Learning Networked Environments and Architectures: a Knowledge Processing Perspective. Springer-Verlag. Reload Editor and Player. (2007). Retrieved May 2007, from http://www.reload.ac.uk/ldplayer.html Wang, E., & Kim, Y. S. (2007). Using SWRL for ITS through Keyword Extensions and Rewrite Meta-Rules. Proceedings of SWEL, (pp. 101-105). Marina Del Rey. Zouaq, A., Nkambou, R. & Frasson, C. (2007a). A Framework for the Capitalization of e-Learning Resources. Proceedings of ED-MEDIA (pp. 1241-1247). AACE. Zouaq, A., Nkambou, R., & Frasson, C. (2007b). Building Domain Ontologies from Text for Educational Purposes. Proceedings of the 2nd European Conference on Technology-enhanced Learning (pp. 393-407). Berlin: Springer-Verlag.
