In education, we should pay close attention to developments and trends in ... These statements being natural semantic units, we can clearly see that RDF model ...
IADIS International Conference e-Learning 2008
SEMANTIC LEARNING CONTENT MANAGEMENT SYSTEM Radu Balog-Crisan, Ioan Roxin LASELDI, University of Franche-Comte, Multimedia Department Montbeliard, France
ABSTRACT Building semantics concepts into the learning tools and utilities will open the road for the next generation of the Learning Content Management System (LCMS). We propose here to name this new learning environment the Semantic LCMS. This paper presents some reflections about how the semantic kernel of an SLCMS could be viewed. This semantic kernel is in close relation with a set of primitive rules and a specified query language but also with available database resources written in RDF. The authors concentrate on Learning Object Metadata (LOM) in the context of this new e-Learning environment. KEYWORDS LOM, RDF, Rules, Semantic Learning Content Management System (SLCMS), Semantic Web, SPARQL
1. INTRODUCTION In education, we should pay close attention to developments and trends in internet mediated interactions. Almost all characteristics of the Web 2.0 are being applied to e-Learning [6], within Learning Content Management System (LCMS) platforms like Moodle, Sakai, Dokeos or even Connect Pro. The interactions with students and among students are more active and, because of this built-in interactivity, students are offered new possibilities to become involved, to interact with the content. One of the limits of Web 2.0 environments is the lack of contextual information, there is a lot of information but no one can organize it and structure it in a meaningful manner. Therefore, the Semantic Web technologies aim at providing contextual information and co-ordination through workflow tools as supporting infrastructure [4].
2. E-LEARNING PLATFORMS FOR SEMANTIC WEB Many efforts have been to make e-Learning platforms more adaptable and personalized to the learner. We can mention collecting data about the student's progress (usually through history trails, journals, standardized test scores, notes, etc.), adapting the course material's presentation, navigation, sequencing and annotation, creating the student model or using models of different students to form a homogeneous group of students for different kinds of collaboration but also setting a goal for students by focusing on their strengths [7]. There are a lot of difficulties in modeling the information request for the information retrieval systems in order to make facilitate more personal learning experiences. We intend to present here a possible solution based on using a semantic kernel, built in close relation with a RDF data model for the LOM applicationdomain, using the specified query language Simple Protocol and RDF Query Language (SPARQL) [15] for RDF data model, plus a set of primitive rules. The Semantic Web development technologies could bring a much needed expressiveness in the eLearning domain: machine-understandable educational material will be the base for machines really using and interpreting information automatically for the benefit of learners, authors and educators. This will open the way for the next generation of the LCMS who is based on the semantic concepts. We suggest naming this
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new learning environment the Semantic Learning Content Management System (SLCMS). A SLCMS will use semantics concepts into the tools for communication, productivity, student involvement, administration, course delivery, content development or any other learning utilities.
2.1 LOM in the Context of Semantic Web Semantic Web is a relational data model. The Resource Description Framework (RDF) statement can be mapped into a database, where a record is a RDF statement, a field is a RDF property type and a RDF object is stored into a table cell. From an informatics system point of view, it is possible to use ontological models like RDF or Web Ontology Language (OWL) in order to define concepts, to give a meaning to a concept and also to define logical relations between concepts. Learning Objects (LO), used to define a method of organizing and structuring the traditional learning content, have specific characteristics: they are self-contained (each learning object can be used independently), reusable (a single learning object may be used in multiple learning environments for multiple purposes), aggregated (learning object can be grouped into larger collections of content, including traditional course structures) and taggable with metadata [11]. In the context of the Semantic Web, we had suggested a LOM RDF binding model, in order to write the metadata in RDF [3]. The assets of this model are directly related to the learner, as he can use only one part of a course or search through one specific learning object. LO can also be organized and shared as showed in [10]. These statements being natural semantic units, we can clearly see that RDF model does not define the knowledge representation of any application-domain; it just supplies a mechanism to describe metadata for neutral-domains. RDF Schema (RDFS) provide a way to write an application-specific schema and namespace. The vocabulary can define the specific properties and classes of resources to which those properties can be applied. The RDFS offers therefore a set of modeling primitive’s class, subclass-of, property, subproperty-of, domain, range and type. With all of this RDFS is rather simple and it could not provide, at the optimum level, exact semantics of an application-specific domain. Comparatively, any ontology system comprises a set of knowledge terms, including the vocabulary, the semantic interconnections, and some simple rules of inference and logic for some particular topic [8]. We need to take into consideration that describing the schema for LOM RDF binding into RDFS could bring some other issues, and then an alternative OWL Description Logics (DL) has more semantic expressibility [5]. Given this advantages of using RDF to achieve expressiveness of metadata with semantics description and knowing that LO are actually described in metadata, we had propose RDF4LOM, an friendly-user application that can be used to generate a database of RDF documents, that describe LO, corresponding to our LOM-RDF metadata [3].
3. THE SEMANTIC KERNEL FOR THE SLCMS The Semantic Web field of research is dynamic and several semantic tools are being developed, we can remind here the Semantic Wikis (Semantic MediaWiki [9], SemWiki [14], WikSAR [2]), Semantic LDAP (LDAP2SPARQL, LDAP2OWL [13]), Semantic Platforms (RDF Gateway [12], Powl), Semantic Blogs etc. All this is new and exists only in beta versions, but these developed tools could be gathered together into one SLCMS, that have his roots to the semantics concepts. All this new tools for e-Learning platforms will be in close collaboration thought central unit of the platform. The central unit of SLCMS is the semantic kernel that makes the link between the ontological resources, a set of field-specific customized rules and the learner, as we see in the Figure 1. The semantic kernel will make available, at the use of learner, more specific resources related to his course domain, thanks to the semantic descriptions of the connected modules, like Semantic Blog, Wiki, Forum, etc. The benefit of this new approach is that the learner's support and knowledge database will be enriched with new data and information, which will result in a better understanding and faster learning. The query language SPARQL will play a strategic role: making the rule based interrogations between the ontological statements from resources.
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IADIS International Conference e-Learning 2008
To become functional the system needs explicit logic (rules); this will play an important role because the automatic composition and inter-operation of educational services will need some reusable procedures to be taken in consideration. These procedures could express the workflow of LO or other logical personalized rules. Applied to e-Learning, rules could help guiding the learner thought vast informational resources.
Figure 1. An overview the central part of an SLCMS
The RDF enables knowledge representation within the Semantic Web but it does not provide methods of extracting new knowledge from the asserted ones. Therefore, the rules are the standard and logical mechanism to achieve this. Rules have their origin in the field of logical programming [1]. To take the benefit from the rules into Semantic Web is needed an XML/RDF encoding that support this. Here we can see that rules could be used to guide the learners according to their performance, and even to generalize and personalize some other tools used into the SLCMS. A possible scenario of the learner into SLCMS could be as we can see in Figure 2: • Step 1: the learner starts a query regarding concept thing; • Step 2: the Semantic Search Engine (SSE) of the Semantic Course will search within its ontological DB;
Figure 2. Scenario over SLCMS • Step 3: the SEE will return its response to the learner; • Step 2a, 5: a parallel search demands it is sent to the Semantic Kernel; • Step 6: the Semantic Kernel will analyze the received demand and will forward it to the Internal
Interpret;
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ISBN: 978-972-8924-58-4 © 2008 IADIS
• Step 7: a special created thread will handle the demand, and is responsible to search in others modules
for the Semantic Blog, Wiki, Forum, etc. answers related with concept thing;
• Step 8, 9: the Internal Interpret will search within every semantic module connected (excluding module
that has launched the thread);
• Step 10, 11, 12: the Internal Interpret will prepare the answer to be sent to the module that launched the
thread;
• Step 3a, 4, 4a: the SEE of the module will return the answer to the learner, parallel with the locally
answer analyzed at step 2;
• Step 13: the learner finds in the received answer that there is something about the concept thing, in some
other module (here Semantic Blog) then follows the link there.
4. CONCLUSION e-Learning in the context of the Semantic Web is taking new forms. We have examined in particular the LOM and we have showed that with semantics the benefits are directly related to the learner. We have also examined the e-Learning platforms and we have shown that they are more adaptable to the learner, with a higher personalization potential. Several e-Learning applications (communication, productivity, course delivery tools etc.) that try to take advantage of semantics and therefore we heaved propose to name this new learning environment the Semantic Learning Content Management System (SLCMS), because of the weight of semantic in any learning tool. Our focus now is to build the semantic kernel from the SLCMS and to test it on a database of LOM RDF resources, to analyze the results and potential that could bring such semantic platform.
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