We introduce an ontology-based semantic modelling framework that addresses subject domain modelling, instruction modelling, and interoperability aspects in the development of complex reusable learning objects. Ontologies are knowledge representation frameworks, ideally suited to support knowledge-based modelling of these learning objects. We illustrate the benefits of semantic modelling for learning object assemblies within the context of standards such as SCORM Sequencing and Navigation and Learning Object Metadata

C. Pahl

H.-E. Eriksson

M.C. Daconta

S.-C. Hu

English

Crossref

Semantic Modelling of Learning Objects and Instruction

Pahl, Claus

Melia, Mark

DCU Online Research Access Service

Semantic Modelling ofLearning Objects and InstructionClaus Pahl and Mark MeliaDublin City University, School of ComputingDublin 9, Ireland[cpahl|mmelia]@computing.dcu.ieAbstract. We introduce an ontology-based semantic modelling frame-work that addresses subject domain modelling, instruction modelling,and interoperability aspects in the development of complex reusablelearning objects. Ontologies are knowledge representation frameworks,ideally suited to support knowledge-based modelling of these learning ob-jects. We illustrate the benefits of semantic modelling for learning objectassemblies within the context of standards such as SCORM Sequencingand Navigation and Learning Object Metadata.1 IntroductionCost-effective and educationally successful learning object development increas-ingly relies on reusable learning objects and their integration into a sound in-structional design. Description and discovery mechanism are needed to allowlearning objects to be published by providers and retrieved by potential users.Instruction needs to be designed separated from the learning objects themselves.Two developments from software engineering and Web technologies, address-ing composition and description, can provide solutions for the development ofcomplex units of study from reusable learning objects. Firstly, model-driven de-velopment uses diagram-based models that are abstract, easy to comprehendand communicate. Modelling has been recognised as an important aspect in thedevelopment of software systems. Model-driven architecture (MDA) is a pro-posed standard that reflects this view [1]. MDA supports development throughdiagram-based modelling [2]. Secondly, semantic modelling is based on ontologiesas sharable, extensible knowledge representation frameworks [4]. Ontologies andontology-based modelling have been proposed to enhance classical UML-basedmodelling. The essential benefits of ontologies are, firstly, interoperability andsharing of descriptions and, secondly, advanced analysis and reasoning.We present an ontology-based modelling framework that addresses the re-quirements of learning object and instruction development. The three differentlayers of the MDA framework – business modelling, abstract system design, andplatform-specific modelling – shall be adapted to the learning technology con-text and shall be supported by combining different ontology-based modellingtechniques that address the central concerns of these layers. These concernsare, firstly, subject domain modelling, secondly, instruction design, and, finally,platform-specific implementation. Instructional design through sequencing ofreusable learning objects shall here be the central activity.– Subject domain modelling is a concern that is independent of a concretecomputational paradigm. Capturing the domain context of a unit of studyis essential for learning content development.– Instructional design on a platform-independent level is important since soundeducational composition and sequencing are essential design issues.– Explicit modelling of learning objects and instruction within the given plat-form technology supports the model-based discovery of learning objects andthe deployment of sequenced instruction.We also address the abstract description of learning objects through metadatain order to enable reusability. Our platform consists of SCORM Sequencing andNavigation [5] and the Learning Object Metadata standards [6].2 Subject Domain ModellingThe focus of the first MDA modelling layer is to capture and conceptualise theobjects and processes that form the central concepts of the subject domain. On-tologies consist of two basic entities – concepts of a domain and relationshipsbetween these concepts that express properties of one concept in terms of an-other concept. Classical ontologies relate concepts in a subclass hierarchy, whichcreates a taxonomy for a particular domain. Two kinds of concepts – objectsrepresenting static entities and information and processes representing activitiesand behaviour – can be distinguished. The set of relationships shall comprise asubclass relationship for concept hierarchies, a dependency relationship, and acomposition relationship. The choice of relationship types here is critical to ad-dress the needs of activity-oriented domain modelling [7]. Dependency relation-ships express how information objects are processed by activities. Compositionis important for both objects and processes. Our concern is domain modellingfor course subjects. Domain activities and processes are important as they oftenform the starting point for detailed models including instructional aspects.In Fig. 1, objects are elliptic entities such as learning objects. Processes arerectangular entities such as learning activities. These entities – concepts in anontology – are represented from three viewpoints. This three-part ontology inFig. 1 is a generic model template that can be refined for a particular subject:– Classification: For a programming course, learning objects which addressloops or specific data structures could be defined as refinements of the givenlearning object elements in the template.– Behaviour: Subject-specific learning activities such as study units on dif-ferent forms of iteration and loops in programming can be sequenced, i.e.instructionally designed, using diagrammatic process expressions.– Structure: The hierarchy of composite learning objects can be presented.Classification(using is_a relationships)learningassetstatic learningobjectdynamic learningobjectassessmentobjectlearningobjectlearnerprofileBehaviour and Processes(using dependency relationships)assessmentobjectlearnerprofilelearnerdetailsfeedbacklearningactivityregistrationlearnerevaluationlearnerinputStructure(using composition relationships) learnerprofilemasteredactivitiespersonalinformationlearningactivitylabactivitylectureparticipationFig. 1. Subject-level Excerpts from an E-Learning Domain Ontology.Sequencing constraints on activities and processes can also be expressed in on-tological form in addition to concepts and relationships. Iteration, choice, con-currency, or sequence are process combinators that are often better expressedin a separate, textual sublanguage. Individual activity steps of a learning activ-ity could be sequenced using additional constraints: lecture; !( labExercise1 +labExercise2 ); selfAssessment, which expresses that a lecture is followed by arepeated choice between two lab exercises followed by a test.3 Instruction ModellingThis modelling layer introduces a learning design focus into the modelling pro-cess. Instructional design through sequencing is our focus. Instructional sequenc-ing and interaction processes are part of the learning design, which determineshow we assemble learning objects to sound educational units of study.Looking into software technology can be beneficial due to similarities betweeninstructional sequences and processes between software services. Various process-related ontologies exist [8] – in particular service ontologies appear suitable.WSPO – the Web Service Process Ontology – can be distinguished from otherservice ontologies such as OWL-S and WSMO through its process-orientation[9]. In WSPO, the focus is on the behaviour – i.e. processes and interaction – ofsoftware. WSPO ontologies are based on a common template (Fig. 2) that weutilise here to express the instructional design of learning object assemblies.The central WSPO concepts are the states that separate individual activities– pre- and poststates of learning activities – and that characterise the state ofthe learning process in terms of objectives already achieved and prerequisites offurther activities. Other concepts capture objects that might be processed in anlearnerinputpostprepostconditionsemanticspreconditionsemanticsin-objectsyntaxlearningactivitylearnerprofilemasters(learningObjective)satisfies(prerequisities)feedbackout-objectsyntaxlearnerprofileFig. 2. Ontological Service Process Template (WSPO) – applied to E-Learning.activity (such as learner profile information) and conditions (such as prerequisitesand effects in terms of learning objectives that are mastered after successfullyworking through a learning activity). Two forms of relationships characterisethis ontology. The central relationship type is the instruction or learning processitself. This instructional relationship is enhanced by an instruction activity com-binator sublanguage (lecture; !(labExercise1+ labExercise2); selfAssessment isan instructional sequence of activities). These sequencing expressions describea learning process in terms of state changes. Auxiliary relationship types areso-called descriptional relationships, which associate the auxiliary concepts suchas learning objectives and prerequisites to states (see Fig. 2).The instruction model focuses on activities and how they are sequenced toprocesses. The combinators that we use are ’;’ (sequential composition), ’ !’ (it-eration), ’+’ (choice), and ’||’ (parallel composition). The symbol ◦ says that theactivity implementation might process these objects. The domain model processlecture; !( labExercise1 + labExercise2 ); selfAssessmentcan be mapped to a WSPO sequenced instruction expressionlecture ◦ profile;! ( labExercise1 ◦ (profile, input1); labExercise2 ◦ (profile, input2) );selfAssessment ◦ profile4 Implementation ModellingImplementation modelling relates the previous layers to the chosen platform. Ourplatform consists of SCORM SN as the instruction implementation language andLOM as the metadata notation for learning object reuse.– LOM aims at the abstract description of learning objects by providing aninteroperable form and vocabulary for semantic learning objects descriptionand discovery in repositories. Some of the technical aspects in the domainand instruction model provide input for the transformation to LOM.– Learning object-based instruction design is often called sequencing to indi-cate the process-like nature of learning object assemblies. SN is a sequencinglanguage that supports the instructional design based on learning activitiesxmlns:lom-general="http://ims.org/ims/schemas/lom-general"xmlns:lom-tech="http://ims.org/ims/schemas/lom-technical"xmlns:lom-edu="ims.org/ims/schemas/lom-educational"<lom-gen:title>Introduction to Programming</lom-gen:title><lom-gen:description>A third-level course for computing students addressing principles of programming languages</lom-gen:description><lom-tech:format>text/html<lom-tech:format><lom-edu:interactivityType>active-simulation</lom-edu:interactivityType><lom-edu:description>learning objective: to master the language concepts loop and choice</lom-edu:description>Fig. 3. LOM Template – applied to an E-Learning Case Study.and associated learning objects. WSPO already captures the essentials ofinstruction and interaction processes and can easily be translated into SN.A learning activity could both be published (using LOM) and integrated ina learning object instructional sequence (using SN). LOM descriptions capturesyntactic and semantic learning object descriptions, similar to WSPO with itsprerequisites and learning objectives. It adds, however, various aspects that canalso be included in discovery and matching. We have illustrated three categoriesof attributes - general, technical, and educational in Fig. 3. These descriptionsare published by providers and can be searched by potential users in education-specific repositories. The benefit of ontologies is the improved support of seman-tic description and discovery compared to syntactical formats such as LOM.SN is an XML notation, based on a WSPO-like operator calculus. Actions,which describe simple learning object applications, are assembled to define learn-ing processes using combinators such as sequence or iteration. Instruction is aninter-learning object perspective on process assembly for these learning objects.An example showing a SN specification generated for a sequenced programminglearning object is presented in Fig. 4. This excerpt is shortened and simplified.The complexity of this specification indicates the benefit of model-driven de-velopment and automatic code generation. Complex SN specifications can beautomatically generated if the user provides a simple abstract instruction spec-ification, a chosen SN templates, and a few other default settings.5 ConclusionsThe benefits of ontologies match the requirements of a platform such as learn-ing object development and deployment within SCORM, where often reuse oflearning objects is the main style of development. This style relies on interop-erable data formats and sound educational assemblies. In heterogeneous envi-ronments and cross-organisational development, information about a variety ofaspects – represented in ontologies – is vital. Model-driven development definesa development process for this context. We have identified activity-oriented sub-ject domain modelling, instructional design, and learning object implementationmodelling as three central development concerns. Ontology technology providesan integrated, coherent solution for these concerns at all modelling layers. Wehave limited our investigation to inter-learning object aspects. Learning objectdevelopment also comprises their generation from resources such as ontologies.Ontologies can be used to generate different learning object types [10].<item identifier="module"><imsss:sequencing><imsss:controlMode choice="false" flow="true" forwardonly="true"/></imsss:sequencing><item identifier="labex"><title>Lab Exercises</title><item identifier="lab1" identifierref="ex1_res"><title>Lab Exercise 1</title><imsss:sequencing><imsss:controlMode choice="false" flow="true" /></imsss:sequencing></item><item identifier="lab2" identifierref="ex2_res"><title>Lab Exercise 2</title><imsss:sequencing><imsss:controlMode choice="false" flow="true" /></imsss:sequencing></item><imsss:sequencing><imsss:controlMode choice="true" flow="false" /><imsss:sequencingRules><imsss:preConditionRule><imsss:ruleConditions conditionCombination="any"><imsss:ruleCondition condition="satisfied" operator="not"/></imsss:ruleConditions><imsss:ruleAction action="hiddenFromChoice"/></imsss:preConditionRule></imsss:sequencingRules><imsss:objectives><imsss:primaryObjective objectiveID=TEST"><imsss:mapInfo targetObjectiveID="LAB_PREREQ"/></imsss:primaryObjective></imsss:objectives></imsss:sequencing></item><item identifier="asses" identifierref="asses_res"><title>Self-Assessment</title><imsss:sequencing><imsss:controlMode choice="true" flow="false" /><imsss:sequencingRules><imsss:preConditionRule><imsss:ruleConditions conditionCombination="any"><imsss:ruleCondition condition="satisfied" operator="not"/></imsss:ruleConditions><imsss:ruleAction action="hiddenFromChoice"/></imsss:preConditionRule></imsss:sequencingRules><imsss:objectives><imsss:primaryObjective objectiveID=ASSESS"><imsss:mapInfo targetObjectiveID="LAB_PREREQ"/></imsss:primaryObjective></imsss:objectives></imsss:sequencing></item><imsss:sequencing><imsss:controlMode choice="true" flow="false"/></imsss:sequencing></item>Fig. 4. Generated SCORM SN Implementation (excerpt).While the aim in this paper was to outline a modelling approach and todemonstrate the feasibility and benefits, more needs to be done to implementand empirically evaluate the approach. We and others, like [3], have only madeinitial steps in this direction so far. Our aim is also to support our approachwith a wider range of suitable, learning technology-specific tools and to use theproposed methodology for a wider range of subjects.References1. Object Management Group. MDA Model-Driven Architecture Guide. OMG, 2003.2. H.-E. Eriksson, M. Penker, B. Lyons, and D. Fado. UML 2 Toolkit. John Wiley &Sons, 2004.3. S.-C. Hu. Application of the UML in Modeling SCORM-Conformant Contents.Intl. Conf. on Advanced Learning Technologies ICALT’05. IEEE, 2005.4. M.C. Daconta, L.J. Obrst, and K.T. Klein. The Semantic Web. Wiley, 2003.5. ADLNet. Sharable Content Object Reference Model SCORM, 2004.6. IEEE Learning Technology Standards Committee LTSC. IEEE P1484.12/D4.0Standard for Learning Object Metadata (LOM). IEEE Computer Society, 2002.7. R. Wilson. The Role of Ontologies in Teaching and Learning. Technology andStandards Watch Report TSW0402. JISC Joint Information Systems Committee,2004. http://www.jisc.ac.uk/uploaded documents/tsw 04 02.pdf.8. T. Payne and O. Lassila. Semantic Web Services. IEEE Intelligent Systems, 19(4),2004.9. C. Pahl. Ontology Transformation and Reasoning for Model-Driven Architecture.In International Conference on Ontologies, Databases and Applications of Seman-tics ODBASE’05. Springer LNCS Series, 2005.10. C. Pahl and E. Holohan. Ontology Technology for the Development and Deploy-ment of Learning Technology Systems. In Intl. Conf. on Educational Hyper- andMultimedia Edmedia’04. AACE, 2004.

Application of the UML

Ontology Technology for the Development and Deployment of Learning Technology Systems.

Ontology Transformation and Reasoning for Model-Driven Architecture.

Semantic Web Services.

Sharable Content Object Reference Model SCORM,

The Role of Ontologies

The Semantic Web.

Semantic modelling of learning objects and instruction

Name not available

Irish Universities

Abstract. We introduce an ontology-based semantic modelling frame-work that addresses subject domain modelling, instruction modelling, and interoperability aspects in the development of complex reusable learning objects. Ontologies are knowledge representation frameworks, ideally suited to support knowledge-based modelling of these learning ob-jects. We illustrate the benefits of semantic modelling for learning object assemblies within the context of standards such as SCORM Sequencing and Navigation and Learning Object Metadata. 

Claus Pahl

Mark Melia

CiteSeerX

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.2155

Semantic modelling of learning objects and instruction

Abstract

Similar works

Full text

Available Versions

Crossref

DCU Online Research Access Service

Name not available

Irish Universities

CiteSeerX