Metadata and Ontologies in Learning Resources Design - Springer

Metadata and Ontologies in Learning Resources Design Christian Vidal C.1, Alejandra Segura Navarrete1, Víctor Menéndez D.2, Alfredo Zapata Gonzalez2, and Manuel Prieto M.3 1

2

Universidad del Bio-Bio, Avda. Collao 1202. Concepción, Chile Univ. Autónoma de Yucatán. FMAT Periférico Norte. 13615, 97110 Mérida, Yuc, Mexico 3 Univ. de Castilla-La Mancha. ESI. Po. de la Universidad. 4, 13071 Ciudad Real, Spain {cvidal,asegura}@ubiobio.cl, {mdoming,zgonzal}@uady.mx, [email protected]

Abstract. Resource design and development requires knowledge about educational goals, instructional context and information about learner’s characteristics among other. An important information source about this knowledge are metadata. However, metadata by themselves do not foresee all necessary information related to resource design. Here we argue the need to use different data and knowledge models to improve understanding the complex processes related to e-learning resources and their management. This paper presents the use of semantic web technologies, as ontologies, supporting the search and selection of resources used in design. Classification is done, based on instructional criteria derived from a knowledge acquisition process, using information provided by IEEE-LOM metadata standard. The knowledge obtained is represented in an ontology using OWL and SWRL. In this work we give evidence of the implementation of a Learning Object Classifier based on ontology. We demonstrate that the use of ontologies can support the design activities in e-learning. Keywords: Ontology, Instructional Design, knowledge acquisition, web semantic.

1 Introduction In the words of Tim Berners-Lee [1] "The Semantic Web is an extension of the current web in which information has a well-defined meaning, more understandable by computers, and where people work cooperatively". In such sense, including technologies used in recent times, ontologies enable better define the meaning of things on the web. They are designed so that this meaning could be processed by machines and humans, due to its precise semantic [2]. Languages based on XML as OWL or RDF allows specifying ontological models. OWL is the ontology language recommended by the World Wide Web Consortium [3]. OWL exploits many of the capabilities of Description Logic, including a well-defined semantics and some techniques for practical reasoning. Another important language is SWRL (Semantic Web Rules Language) that allows as to define rules in ontologies. This language is used as a complement to the ontological structure described by OWL. M.D. Lytras et al. (Eds.): WSKS 2010, Part I, CCIS 111, pp. 105–114, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Educational Ontologies are those that can be used in Web based teaching [4]. According to [5] in e-learning, ontologies have been largely used to systematically describe resources; to enable semantic search and to provide users with a benchmark for shared concepts and terminology. WWW offers teacher-designers a huge amount of resources that can be used to support learning. Although many of these resources are stored in Repositories, Learning Objects do not always have enough information to use them correctly from an educational perspective. In e-learning environments, the design of learning resources becomes an essential activity. The ultimate goal of learning resources is to enable and enhance learning, but according to [6], a large amount of these resources lack a defined instructional strategy, which causes many of them to fail in their goals. The use of Instructional Design theories can guide the construction of learning resources and help to achieve learning objectives. According to Reigeluth, an Instructional Design theory is a theory that offers explicit guidance on how to help people to learn [7]. For this reason, it is believed to be useful for designers of e-learning resources the knowledge modelling of these theories in precise semantic structures. For example, ontologies can be used to develop processes that support design [8] [9]. In the terminology of Instructional Design, sequencing meant taking individual Learning Objects (LO) and combining them in a way that made instructional sense [10]. To achieve this, there must be enough information in LO from the educational perspective. Some of this information about LO instructional use, can be found in metadata. IEEE-LOM standard [11] in its educational category provides important information for this purpose. Other categories that help to describe some educational aspects of the LO’s are: relation, annotation and classification. However, this information is insufficient for many purposes. For example, for sequencing, to it, would be desirable to classify LO’s from a pedagogical perspective in order to meet those objects that encourage learning as active or passive, or with a high level of interaction. Thus, course designers might use LO’s for their instructional requirements. But this requires more complex analysis of the instructional information provided by metadata individually and collectively. Machine learning techniques can be used to automatically extract characterizations of LO collections. Indeed, the application of Data Mining (DM) techniques in the e-learning domain, have become more frequent in recent years [12],[13]. DM in elearning has been mainly oriented to analyze student’s behaviour, outcomes and interests in their interaction with learning technology and learning resources. This article presents the process of obtaining useful knowledge for Instructional Design by applying DM techniques to LO metadata contained in a repository. The resulting knowledge is represented using semantic web technology, particularly with ontological languages, to support Learning Object Classification according to instructional needs. The structure of the paper is as follows: Section 2 presents the general approach used to obtain knowledge. Section 3 describes the ontology model which represents the resulting knowledge. Section 4 presents the way to use the ontology in the practice. Finally conclusions are presented and the direction of future work of this research.

Metadata and Ontologies in Learning Resources Design

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Ontology Clustering and Classification

IF … THEN

Data Mining in LO metadata

Knowledge Representation

OWL + SWRL rules LO

LO LO

LO

LO

Learning Object Repository

LO metadata xml

Pedagogical Clasiffication Application (java)

Grouping LO In XML Fig. 1. Process of generation and representation of knowledge

2 Knowledge Acquisition Here we focus on analyzing metadata stored in Agora[14]. This system has several modules that interact with each other to provide metadata assisted generation, LO management and resource recovery in its own repository as well as a management and knowledge representation generated by the user activity. The knowledge gained through data mining techniques, is represented in the ontology. Figure 1 shows the process of generation and representation of knowledge. The following sections detail the relevant aspects of the approach used. 2.1 Knowledge Discovering The process of knowledge discovery applied on data provided by the Agora Repository considered the information pre-processing, the application of DM algorithms and the post-processing. Metadata were extracted for 200 objects stored in Agora. The study used those objects that are of greater completeness in its metadata. Subsequently, clustering and classification techniques were applied to metadata. Results were interpreted and transformed into a rules set. This process is documented in [15] and is summarized in Figure 2.

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LO

LO metadata

LO

LO

LO

Data Pre-processing

LO

LO Repository Clustering and Classification

Data Mining

Results Analysis rules

IF … THEN

Fig. 2. Data mining process applied to Agora’s metadata

2.2 Knowledge Extraction Applying Simple K-means algorithm allows to establish 3 clusters as shown in Table 1. Table 1. Clustering results obtained form LOs in Agora Attribute

Full Data (200)

aggregation_level structure cat_format cat_context difficulty interactivity_level interactivity_type cat_learn_res_type semantic_density

one atomic flash high medium very_low expositive sld medium

Cluster# 0 (99) 50% one atomic flash high medium high active exe high

1 (58) 29% two atomic ppt high medium low expositive sld medium

2 (43) 22% One Atomic Pdf High Easy very_low Expositive Rea Medium

Clusters can be described in terms of objects grouped as follows: • Cluster 0 (actives): Objects more active and highly interactive for the learner. These are mainly resources of type exercise. They have a high semantic density and high complexity level also. • Cluster 1 (pasives): Objects with low interactivity level and expositive. These resources are mainly slides with a medium level of both complexity and semantic density.


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• Cluster 2 (very_pasives): Expositive objects with very low interactivity. They are mainly type resources reading that are easy to use with medium semantic density. Clustering resulting provides important evidences for Instructional Design. A system for instructional design assistance may use information resulting from clustering for classify LO into the three discovered groups and thus to recommend these resources in different instructional contexts.

3 Knowledge Representation The knowledge gained in DM process must be represented for later use. This section shows how the knowledge is represented in LOSO ontology. 3.1 LOSO Ontology Model The rules obtained through the application of DM techniques provided rules that are stored in an ontology to support the sequencing of LOs [16]. This ontology, called LOSO (Learning Object Sequencing Ontology), was created with the intent to support the generation of sequencing strategies for LO. The generation of the Instructional Design Strategy (IDS) is performed according to an Instructional Requirement (IR), which indicates for example, if one require active or passive learning; or some specific student’s cognitive style as well as the instructional context, among other information.

Fig. 3. Fragment of classes and relations of LOSO

All knowledge associated with a IR and other items, are stored in the ontology. A view of the classes and relations that make up the ontology, can be seen in Figure 3. A central concept of the model is ID_Strategy that represents the IDS responding to an IR, related to a specific Instructional_Context. The ID_Strategy relates Learning_Resource, Learning_Activity and Psicopedagogical_resource.

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3.2 Representation of Rules in SWRL OWL was used as representation language with Semantic Web Rules Language (SWRL). Code generation OWL-DL was performed using [17]. OWL allows building hierarchies of concepts and defining them through an axioms language for reasoning and interpretation. SWRL adds an additional layer of expressiveness allowing the definition of inference rules in these models [18]. Rules were implemented using the Protegé SWRLTab plugin, with the Jess rules machine [9]. A view of the execution environment of the rule is displayed in Figure 4.

Fig. 4. Environment of definition SWRL's rules with Protégé

For example, a rule that defines the group of very passive LO as follows: “expositive objects and very little interactive for the learner. They have a medium level semantic density and low level of complexity”. This rule is expressed in SWRL as follows: LOM:learningObject(?lo) ^ LOM:hasAggregationLevel(?lo,"1") ^ LOM:hasStructure(?lo,"atomic") ^ LOM:hasEducationalInformation(?lo,?x) ^ LOM:isIntendedForContext(?x,"higher education") ^ LOM:hasDifficulty(?x,"easy") ^ LOM:hasInteractivityLevel(?x,"verylow") ^ LOM:hasInteractivityType(?x, "expositive") ^ LOM:hasSemanticDensity(?x, "medium") Æ LOSO:LOGroup(?lo,LOSO:very-pasive )

Table 2 shows other rules defined in the ontology. First and the second rules allow classifying LO as active and passive respectively. Rule 3 supports the creation of the IDS.


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Table 2. Other rules defined in LOSO No 1

Rule Active objects

2

Pasive objects

3

IDS Generation

Expression SWRL LOM:learningObject(?lo) ^ LOM:hasAggregationLevel(?lo, "1") ^ LOM:hasStructure(?lo, "atomic") ^ LOM:hasEducationalInformation(?lo, ?x) ^ LOM:isIntendedForContext(?x, "higher education") ^ LOM:hasDifficulty(?x, "medium") ^ LOM:hasInteractivityLevel(?x, "high") ^ LOM:hasInteractivityType(?x, "active") ^ LOM:hasSemanticDensity(?x, "high") Æ LOSO:LOGroup(?lo, LOSO:active) LOM:learningObject(?lo) ^ LOM:hasAggregationLevel(?lo, "2") ^ LOM:hasStructure(?lo, "atomic") ^ LOM:hasEducationalInformation(?lo, ?x) ^ LOM:isIntendedForContext(?x, "higher education") ^ LOM:hasDifficulty(?x, "medium") ^ LOM:hasInteractivityLevel(?x, "low") ^ LOM:hasInteractivityType(?x, "expositive") ^ LOM:hasSemanticDensity(?x, "medium") Æ LOSO:LOGroup (?lo,LOSO:pasive ) LOSO:ID_Strategy (?i) ^ LOSO:Instructional_Requirement (?r) ^ LOSO:rIDSTrate_Req (?i,?r) ^ LOSO:Instructional_Context (?ic) ^ LOSO:rReq_ICont (?r,?ic) ^ LOSO:Audience (LOSO:high-education ) ^ LOSO:rICont_Audience (?ic, LOSO:high-education) ^ LOSO:rICont_Audience (?ic, LOSO:programming) ^ LOSO:Instructional_Objective (?io) ^ LOSO:Learning_type (LOSO:cognitive-strategies ) ^ LOSO:rIObj_LearnType (?io, LOSO:cognitivestrategies) ^ LOSO:Knowledge_type (LOSO:procedural ) ^ LOM:learningObject (?lo) Æ LOSO:rIDStrate_LR_use (?i,?lo)

Rules presented in the ontology can mainly support the classification of LO used in sequencing activities. This provides some degree of automation in this process. Our group is now working in the representation of instructional design theories that may support the sequencing.

4 Knowledge Use: The Pedagogical Resource Classifier In this section, we present an application that uses LOSO ontology. This application is designed to support the resource design task. It implements a Pedagogical Resources Classifier (PRC) in order to help designer to find the most appropriate resources according to the instructional context. Classifier has two main components: the Instantiation component and the Inference component. The first, performs the instantiation of XML documents into the ontology. This XML document contains metadata for LOs in the IEEE-LOM standard. The second process the inference based on SWRL

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rules. This application was built in Java and it use Jena Framework to manipulate the ontology, SWRLtab to make inference and JDOM for processing and generation of XML documents. Using PRC a teacher-designer, who works in a LO management environment, repository or LMS, can request items from any of the groups founded: active, passive or very passive. These groups are related to the instructional context where objects are used. As an example, we show the possible context of use for objects of cluster 0 (active): it can be used in constructivist learning environments and based on experience (interactivity_level= high, interactivity_type= active, resource_type= exe), for students with active and intuitive learning styles [19]. These objects can be used in courses with intermediate or advanced students (difficulty= medium, semantic_density= high). Figure 5 show the interaction between PRC and the Agora repository. The use of XML as data exchange technology, allows communication with other systems that generate the metadata schema of the objects in this format. Pedagogical Classification Based on ontologies LOM:learningObject(?lo) ^ LOM:hasAggregationLevel(?lo, "1") ^

→OSLO:LOGroup(?lo, OSLO:active)

Instantiation

Inference SWRL

LOs metadata (XML)

LO Repository teacher LO

LO

LO

LO

which objects to use?

LO

LOM

LOs Classified (XML)

Fig. 5. Interaction between the Classifier and Agora Repository

5 Initial Testing Some tests have been carried out in order to analyse the tool performance. First, a collection of 49 LO’s from the Agora system has been obtained. Next, this collection has been processed by PCR. In this test we achieve: 7 LO’s classified as active, 3 LO’s as passive, 2 LO’s as very_passive and 28 LO’s as unknown or not classified. Second, tests was made witch LO’s obtained from Ariadne repository [20]. The classifier processed correctly 300 LO’s, but its results were poor. This is because LO from Ariadne have not completed their educational metadata (category 5 IEEE-LOM). Other test was executed using LO from Mace repository [21]. 115 LO’s were processed by PCR with the following results: 13 LO’s classified as active, 12 LO’s as passive, 14 LO’s as very_passive and 76 as unknown. This results show that PCR works correctly but rules for classification must be improved, because they depend on the completeness of metadata.


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6 Conclusions and Future Works This paper presented an approach to incorporate the generation and representation of knowledge as an important input for learning resource development. We demonstrate that the use of ontologies can support design activities in e-learning environments. Results also evidence the importance of using metadata in the Instructional Design process and the possibilities of supporting this process with Semantic Web technologies. We present also an application that supports the resources design task. The use of ontologies enables that represented knowledge can be used by other applications that provide automated support to this process. We show evidences of use with Agora system and other LO Repository as Ariadne and Mace. Currently we are also trying to use other models and techniques to improve the knowledge structure and the details provided by the ontology. Additionally, we are working in the use of the obtained results into Learning Object’s searching and in metadata assisted generation.

Acknowledgments This work is partially supported by MECESUP UBB 0305 project, Chile; the National Council of Science and Technology (CONACYT, México); the Council of Science and Technology of Yucatán State (CONCyTEY, México); the TIN2007-67494 project of the Science and Innovation Ministry; The PEIC09-0196-3018 project of the Autonomous Government of Castilla-La Mancha.

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