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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . Adaptive E-Learning System based on Semantic Web and Fuzzy Clustering Khaled M. Fouad Mofreh A. Hogo Computer Science Dep. - Community College - Taif University, Computer Science Dep. – Computers and Information Systems Kingdom of Saudi Arabia (KSA); College - Taif University, Kingdom of Saudi Arabia (KSA). Central Laboratory of Agriculture Expert Systems (CLAES), Egypt firstname.lastname@example.org email@example.com Shehab Gamalel-Din Nagdy M. Nagdy Computers Dept, Faculty of Science, King Abdul-Aziz Systems and Computers Engineering Dep. – Faculty of University, Kingdom of Saudi Arabia (KSA) Engineering – AlAzhar University, Egypt. firstname.lastname@example.org email@example.com Abstract— This work aims at developing an adaptive e-learning A. ADAPTIVE E-LEARNING system with high performance to reduce the challenges faces e- In the context of e-learning , adaptive e-learning systems learners, the instructors and provides a good monitoring system are more specialized and focus on the adaptation of learning for the complete e-learning systems as well as the system content and the presentation of this content. According to , structure. The work presents the different phases for the system an adaptive system focuses on how the knowledge is learned development of the adaptive system as: the first stage is the by the student and pays attention to learning activities, collection of the e-learners documents, the second stag is the cognitive structures and the context of the learning material. documents representation including the frequency count and the weighting of the documents with its frequencies, the third stage is The structure of an adaptive e-learning system is shown in the prediction and clustering of e-learners interests using the Fig. 1. fuzzy clustering method and the statistical K-means clustering Data about user method. The results obtained from this work shows that we have to have different e-learners ontologies using the results of the User Modeling clustering methods which reflect the e-learners interests. Finally System the work concluded the suggestions as well as the recommendations for the instructors and the systems User Model administrators. Adaptation Keywords-component; E-Learning; Semantic Web; Fuzzy Ckustering; User model; User Model Representation. Adaptation Fig. 1: The Structure of an Adaptive System  I. INTRODUCTION The system intervenes at three stages during the process of Electronic learning or E-Learning  is interactive learning adaptation. It controls the process of collecting data about the in which the learning content is available online and provides learner, the process of building up the learner model (learner automatic feedback to the student's learning activities, it is modeling) and during the adaptation process. It is not feasible much like computer-based training (CBT) and computer-aided in conventional WBE to create static learning material  that instruction (CAT), but the point is that it requires Internet for can be read in any arbitrary sequence, because of many access to learning material and for monitoring the student's interdependences and prerequisite relationships between the activities. E-Learners usually can communicate with their course pages. However, adaptive hypermedia (AH) methods tutors through the Internet. and techniques make it possible to inform learners that certain Semantic web could offer unprecedented support to the links lead to material they are not ready for, to suggest visiting network teaching in semantic query, meaning construction, pages the learner should consult, or automatically provide knowledge acquisition and sharing and collaborative learning. additional explanations at the pages the learner visits, in order Simultaneously semantic web also provides the support to to scaffold his/her progress. Adaptive educational hypermedia describe semantics of learner characteristic in the learner systems (AEHSs) apply different forms of learner models to model, and makes it possible to share learner model between adapt the content and the links of hypermedia course pages to systems. So we need to construct the learner model and the learner. AEHSs support adaptive learning, using technology clustering it; to simplify contents search that is based on that to constantly measure the learner's knowledge and progress in learner profile, in adaptive learning system based on semantic order to adapt learning content delivery, presentation, feedback, web. assessment, or environment to the learner's needs, pace, preferences, and goals. Such systems make predictions of what the learner needs to attain his/her goals, respond to such needs, 308 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . allocate resources, implement change, and thus improve The third layer accommodates RDF and RDF Schema as personalization of the learning process. The system can be mechanisms to describe the resources available on the Web. As designed to use predictive strategies prior to instruction such, they may be classified as lightweight ontology languages. delivery and learning sessions, during the instruction (based on Full ontology description languages appear in the fourth layer the learner's interaction), or both. as a way to capture more semantics. The topmost layer The open adaptive learning environment  is in which introduces expressive rule languages. The semantic web  is a learners dynamically select a learning route suitable to their space understandable and navigable by both human and needs and profile. The proposed environment is based on the software agents. It adds structured meaning and organization to IEEE/IMS learning object metadata (LOM) standard. The the navigational data of the current web, based on formalized nature of adaptations provided by this environment are centered ontologies and controlled vocabularies with semantic links to on the learner, and allow the LO to adapt to the evolving each other. From the E-Learning perspective, it aids learners in learner’s model in terms of background, learning modalities, locating, accessing, querying, processing, and assessing and learning styles. It was summarized  the role of learning resources across a distributed heterogeneous network; personalization in learning environments as follows: it also aids instructors in creating, locating, using, reusing, • Personalized learning environments enable one-to-one or sharing and exchanging learning objects (data and many-to-one learning paradigms (one teacher - one learner, components). The semantic web-based educational systems and many teachers – one learner), contrary to traditional need to interoperate, collaborate and exchange content or re- learning environments that always adopt one-to-many use functionality. A key to enabling the interoperability is to learning paradigm (one teacher, many students); capitalize on (1) semantic conceptualization and ontologies, (2) • Personalized learning environments impose no constraints common standardized communication syntax, and (3) large- in terms of learning time, location, etc., whereas traditional scale service-based integration of educational content and ones are fairly restricted by the learning setting; functionality provision and usage. The vision of the semantic • Personalized learning environments recognize the huge web-based E-Learning is founded on the following major variety in the learner's characteristics and preferences in premises: terms of the learning style, media, interests, and the like, and adapt instruction according to them; traditional ones are • Machine-understandable educational content usually designed for the "average learner"; • Shareable educational ontologies, including: • Personalized learning environments tailor instruction to suit Subject matter ontologies the learner's requirements (self-directed learning); in Authoring ontologies (modeling authors’ activities) traditional learning environments, the curriculum, learning • Educational semantic web services, for supporting: units, and the selection and sequencing of learning material Learning, e.g., information retrieval, summarization, are determined by the tutor. interpretation (sense-making), structure-visualization, argumentation, etc. B. E-LEARNING AND SEMANTIC WEB Assessment, e.g., tests and performance tracking It is generally agreed that credible evidence for mastery of Collaboration, e.g., group formation, peer help, etc. learned material  is the goal of instructors. While educators • Semantic interoperability and domain experts agree that decoding meaning from text Semantic interoperability, the key promise of the semantic plays a critical role in the acquisition of knowledge across all web, is defined as a study of bridging differences between disciplines, what particular evidence of mastery is required and information systems on two levels as following: what lends credibility to such evidence are the subjects of a • on an access level, where system and organizational lively debate among experts in the learning community. The need for new methods for semantic analysis of digital text is boundaries have to be crossed by creating standardized now widely recognized in the face of the rising tide of interfaces that share system-internal services in a loosely- information on the Web. The layered model  for the coupled way; and Semantic Web as shown in Fig. 2 puts the relationship among • On a meaning level, where agreements about transported ontology description languages, RDF and RDF Schema, and data have to be made in order to permit their correct XML in a better perspective. The bottom layer offers character interpretation. Interoperability requires the use of encoding (Unicode) and referencing (URI) mechanisms. The standard SW languages for representing ontologies, second layer introduces XML as the document exchange educational content, and services. standard. C. THE ONTOLOGIES The word ontology comes from the Greek ontos, for being, and logos, for word. In philosophy, it refers to the subject of existence, i.e. to the study of being as such. More precisely, it is the study of the categories of things that exist or may exist in some domain . Domain ontology explains the types of things in that domain. Ontology  comprises a set of knowledge terms, including the vocabulary, the semantic Fig. 2: The Architecture of the Semantic Web interconnections, and some simple rules of inference and logic 309 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . for some particular topic. Ontologies applied to the Web are hand the LIP standard is based on the classical notion of a CV creating the Semantic Web. Ontologies provide the necessary and inter-personal relationships are not considered at all. armature around which knowledge bases should be built, and set grounds for developing reusable Web-contents, Web- E. K-MEANS CLUSTERING METHOD services, and applications. Ontologies facilitate knowledge Clustering of objects is as ancient as the human need for sharing and reuse, i.e. a common understanding of various describing the salient characteristics of men and objects and contents that reaches across people and applications. identifying them with a type. Therefore, it embraces various Technically, an ontology is a text-based piece of reference- scientific disciplines: from mathematics and statistics to knowledge, put somewhere on the Web for agents to consult it biology and genetics, each of which uses different terms to when necessary, and represented using the syntax of an describe the topologies formed using this analysis. ontology representation language. There are several such The simplest and most commonly used algorithm, languages around for representing ontologies, for an overview employing a squared error criterion is the K-means algorithm and comparison of them. It is important to understand that . This algorithm partitions the data into K clusters (C1,C2, most of them are built on top of XML and RDF. . . . ,CK), represented by their centers or means. The center of The most popular higher-level ontology representation each cluster is calculated as the mean of all the instances languages were OIL (Ontology Inference Layer) and belonging to that cluster. The algorithm  starts with an DAML+OIL. An ontology developed in any such language is initial set of cluster centers, chosen at random or according to usually converted into an RDF/XML-like form and can be partially parsed even by common RDF/XML parsers. Of some heuristic procedure. In each iteration, each instance is course, language-specific parsers are necessary for full-scale assigned to its nearest cluster center according to the parsing. There is a methodology for converting an ontology Euclidean distance between the two. Then the cluster centers developed in a higher-level language into RDF or RDFS. In are re-calculated. The center of each cluster is calculated as the early 2004, W3C has officially released OWL (Web Ontology mean of all the instances belonging to that cluster: Language) as W3C Recommendation for representing ontologies. OWL is developed starting from description logic and DAML+OIL. The increasing popularity of OWL might Where N k is the number of instances belonging to cluster k lead to its widest adoption as the standard ontology representation language on the Semantic Web in the future. and µ is the mean of the cluster k. k Essentially, OWL is a set of XML elements and attributes, with A number of convergence conditions are possible. For well-defined meaning, that are used to define terms and their example, the search may stop when the partitioning error is relationships (e.g., Class, equivalent Property, intersection Of, not reduced by the relocation of the centers. This indicates that union Of, etc.). the present partition is locally optimal. Other stopping criteria can be used also such as exceeding a pre-defined number of D. LEARNER MODEL AND PROFILE iterations. Figure 3 presents the pseudo-code  of the K- The behavior of an adaptive system  varies according to means algorithm. the data from the learner model and the learner profile. Without Input: S (instance set), K (number of cluster) knowing anything about the learner, a system would perform in Output: clusters exactly the same way for all learners. It was described the 1: Initialize K cluster centers. application of learner models as follows: 2: while termination condition is not satisfied do An extensive learner model must contain information about 3: Assign instances to the closest cluster center. the learner’s domain knowledge, the learner’s progress, 4: Update cluster centers based on the assignment. preferences, goals, interests and other information about the 5: end while learner, which is important for the used systems. Learner Fig. 3. K-means Algorithm. models can be classified according to the nature and form of information contained in the models. Considering the subject The rest of the paper is organized as following: Section 2 is domain, the information stored in a learner model can be reserved for the related works; section 3 introduces the divided into two major groups: domain specific information proposed system structure architecture, section 4 presents the and domain independent information. experiments design and results analysis, section 5 presents the We examined two of the most important and well- concluded suggestions and recommendations to improve the developed standards - the PAPI standard  and the IMS LIP system performance, finally section 6 concludes the work and standard . Both standards deal with several categories for introduces the future work. information about a learner. These standards have been developed from different points of view. The PAPI standard II. RELATED WORKS reflects ideas from intelligent tutoring systems where the An accurate representation of a learners interests , performance information is considered as the most important generally stored in some form of learner model, is crucial to information about a learner. The PAPI standard also stresses on the performance of personalized search or browsing agents. the importance of inter-personal relationships. On the other Learner model is often represented by keyword/concept vectors or concept hierarchy. The acquired model can then be 310 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . used for analyzing and predicting the future learner access completes the user profiles model consistently on the related behavior. Learner model may be built explicitly, by asking feedback mechanism. learners questions, or implicitly, by observing their activity. In , the authors proposed a new approach to User In , authors investigated the techniques to create a user Model Acquisition (UMA) which has two important features. It profile automatically using the ontological approach. They doesn’t assume that users always have a well-defined idea of used a framework to gather the user information from different what they are looking for, and it is ontology-based, i.e., it was search space where user’s details could be found. The details dealt with concepts instead of keywords to formulate queries. include user’s general information to specific preferences. The first problem is that most approaches assume users to have They used Meta search in user’s blog, personal/organization a well-defined idea of what they are looking for, which is not web page, and any other cites to collect information about always the case. They solved this problem by letting fuzzy user user. This information is assigned to a pre- structure hierarchy models evolve on the basis of a rating induced by user or in reference ontology to create an initial user profile. More behavior. The second problem concerns the use of keywords, clearly, initial profile is learned by the concept/document not concepts, to formulate queries. Considering words and not collected from user’s details. In traditional user profiling the concepts behind them often leads to a loss in terms of the system feature extraction from document is done by vector quantity and quality of information retrieved. They solved this space model or considering term frequency, tf-idf methods problem by adopting an ontology-based approach. only. In this research, authors considered WordNet and In , the student interactions with the system are Lexico-Syntactic pattern for hyponyms to extract feature from monitored and stored in log files. The recorded data are then document. This profile further improved by taking cleaned and preprocessed (e.g. compute the relative frequency collaborative user methods. Where, they found a group of of learner actions, the amount of time spent on a specific action users with similar interest by taking similarity score among type, the order of navigation etc). Subsequently, these them. After that an ontology matching approach is applied to behavioral indicators are analyzed and based on them the learn the profile with other similar user which is called system can infer different learning preferences of the student. improved profile.  Introduced a method for learning and Finally, the identified learner model is used by the decision- updating a user profile automatically. The proposed method making component to select the most appropriate adaptation belongs to implicit techniques – it processes and analyzes actions, in order to provide the student with the educational behavioral patterns of user activities on the web, and modifies resources that suit her/his specific needs. a user profile based on extracted information from user’s web- logs. The method relies on analysis of web-logs for III. THE PROPOSED SYSTEM ARCHITECTURE discovering concepts and items representing user’s current and The design idea of adaptive learning system based on new interests. Those found concepts and items are compared creating an ideal learning environment for the learners so that with items from a user profile, and the most relevant ones are system can provide the adaptive learning support according to added to this profile. The mechanism used for identifying the learner's individual differences, and to promote learners to relevant items is built based on a newly introduced concept of study initiatively, and to achieve the knowledge construction. ontology-based semantic similarity. There are many different There are some objectives in the design of adaptive learning methods to Construct Learner Models : Machine Learning system. First, system can provide the adaptive learning content Methods, Bayesian Methods, Overlay methods, Stereotype based on the learner's interest and knowledge requirement. methods, Plan Recognition. Update of Learner Models Second, system can support the self-directed learning and methods are: Analysis of Learner Responses, Analysis of the collaborative Learning. Third, system can help teachers understand the learning process of learners, and adjust the Process of Problem Solution, Analysis of Learner Actions, pedagogical activities, and support the learning evaluation. Discounting Old Data. Last, system will support the courses development for staff. In , student model mainly included the cognitive model Based on these considerations, a new architecture of adaptive and the interest model. Cognitive model mainly pay attention to learning system is proposed in current paper. It is illustrated in learner background knowledge, study style and cognition level. figure 4. According to proposed architecture, our learning Through synthesizing the domestic and foreign research system is mainly composed of four processes: Learner's Web practice authors proposed to use Solomon Study Style Measure Log Analysis, Learner Interest Builder, and Knowledge Meter as preceding measure to test learner's study style. Requirement Acquiring. These Processes will be explained in Regarding the student’s cognition level’s estimate they took the the next section. thought of fuzzy set. It was combined the ontology and concept Learner space , indicated the feature items of user profile with Learner Web- Log Visited pages & Learner Interest semantic concepts, calculates learner’s interest-level to the Behavior Acquiring topic through establishing the word frequency and utilize the Learner profile suitable calculation methods, mining the concepts within the cluster user’s feedback files and the relationship between concepts, Domain Ontology Learner Model combines user’s short-term interests and long-term interests to Representation Generating Learner Interest create user profiles model with semantic concept hierarchy tree of profile Cluster Representation of cluster Clustering and embody the drifting of user profile and improves and Fig.4. The Architecture of the Proposed 311 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . A. LEARNER'S INTEREST ACQUIRING • DOMAIN CONCEPT FILTERING In this system, learner interest model’s knowledge This process discovers concepts which represent the learner’s expression uses the thought which is based on the space vector interests. These concepts and items are compared to the model’s expression method and the domain ontology . The domain ontology to check the relevant items to the learner figure 5 shows certain steps to acquire learner interest. profile. The most relevant ones update the learner profile. The Web - Document Extracted Concepts items relevance is based on ontology-based semantic Based Learning Representation similarity where browsed items by a learner on the web are compared to the items from a domain ontology and learner profile. The importance is combined with the semantic Check Domain Concept Domain-Related Concepts Filtering similarity to obtain a level of relevance. The page items are processed to identify domain-related words to be added to the Learner Domain learner profile. A bag of browsed items is obtained via a Model Ontology simple word indexing of the page visited by the learner. We filter out irrelevant words using the list of items extracted from Build domain ontology. Once domain-related items are identified, we evaluate their relevance to learner’s interests. The selected Fig.5: Acquiring E-learners interest Steps method was used in [20, 21] to compute semantic similarity function (S) based on a domain ontology. The similarity is • DOCUMENT REPRESENTATION estimated for each pair of items where one item is taken from The Vector Space Model [17,18, 19] is adapted in our a learner profile, while the other one from a set of browsed proposed system to achieve effective representations of items. The functions Sw is the similarity between synonym documents. Each document is identified by n-dimensional sets, Su is the similarity between features, and Sn is the feature vector where each dimension corresponds to a distinct similarity between semantic neighborhoods between entity term. Each term in a given document vector has an associated classes a of ontology p and b of ontology q, and ww , wu , and weight. The weight is a function of the term frequency, collection frequency and normalization factors. Different wn are the respective weights of the similarity of each weighting approaches may be applied by varying this function. specification component. Hence, a document j is represented by the document vector dj: d j = ( w1 j , w2 j ,..., wnj ) Where, wkj is the weight of the S(ap , bq ) = ww × Sw(ap , bq ) + wu × Su (ap , bq ) + wn × Sn (ap , bq ) ; kth term in the document j. For ww ; wu ; wn ≥ 0: The term frequency reflects the importance of term k within a particular document j. The weighting factor may be global or Weights assigned to Sw, Su, and Sn depends on the local. The global weighting factor takes into account the characteristics of the ontologies. importance of a term k within the entire collection of The similarity measures are defined in terms of a documents, whereas a local weighting factor considers the matching process [20, 21]: given document only. Document keywords were extracted by AI B using a term-frequency-inverse-document-frequency (tf-idf) S(a, b) = calculation [18, 19], which is a well-established technique in A I B + α ( a, b) A / B + (1 − α ( a, b)) B / A information retrieval. The weight of term k in document j is where A and B are description sets of classes a and b, i.e., represented as: synonym sets, sets of distinguishing features and a set of classes in semantic neighborhood; (A∩B) and (A/B) represent wkj = tf kj × (log n − log dfk + 1) 2 2 intersection and difference respectively, | | is the cardinality of a set; and α is a function that defines relative importance of Where: tfkj is the term k frequency in document j, dfk is non-common characteristics. A set of browsed items that are number of documents in which term k occurs, n = total number similar to items from the user profile is considered as a set of of documents in collection. items that can be added to this profile. Table 2 shows a Table 1 shows the term frequency in different documents. sample of the weighted terms in the documents; that The main purpose of this step is to extract interested items in found in table 1. the web page, then get term frequency that reflects the importance of term, finally get the weight of terms in the selected page. The output of this step is the weight of terms in selected page that can be used to build learner interest profile. 312 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . Table 1: Sample of the Documents with their representation Computer Computer Parallel DOC/items science AI Programming Software eng. Networks LAN WAN Arch. Processors processing Doc1 20 25 20 15 10 0 0 5 0 0 Doc 2 15 15 25 15 5 0 0 0 0 0 Doc 3 0 5 25 10 25 10 15 5 0 0 Doc 4 0 5 25 10 20 5 15 5 0 0 Doc 5 0 0 5 0 10 0 0 5 20 20 Doc 6 0 0 0 5 20 0 0 0 25 30 Doc 7 0 0 5 0 0 0 0 5 20 10 Doc 8 0 0 0 5 0 0 0 10 25 5 Doc 9 15 5 30 30 0 0 0 5 0 0 Doc 10 5 0 25 25 0 0 0 0 0 0 Doc 11 10 0 0 0 10 0 0 25 10 30 Doc 12 10 0 0 0 10 0 0 30 10 25 Doc 13 20 25 5 0 10 0 0 5 0 0 Doc 14 15 15 5 0 5 0 0 0 0 0 Doc 15 0 30 20 15 25 0 5 0 0 0 Doc 16 0 25 25 15 20 0 0 0 0 0 Table 2: Sample of the weighted terms in the documents computer Software Computer Parallel AI programming Network LAN WAN Processors Science Engineering Architecture Processing Doc1 40.00 45.75 28.30 25.17 14.15 0.00 0.00 9.15 0.00 0.00 Doc2 30.00 27.45 35.38 25.17 7.08 0.00 0.00 0.00 0.00 0.00 Doc3 0.00 9.15 35.38 16.78 35.38 40.00 51.23 9.15 0.00 0.00 Doc4 0.00 9.15 35.38 16.78 28.30 20.00 51.23 0.00 0.00 0.00 Doc5 0.00 0.00 7.08 0.00 14.15 0.00 0.00 9.15 48.30 48.30 Doc6 0.00 0.00 0.00 8.39 28.30 0.00 0.00 0.00 60.38 72.45 Doc7 0.00 0.00 7.08 0.00 0.00 0.00 0.00 9.15 48.30 24.15 Doc8 0.00 0.00 0.00 8.39 0.00 0.00 0.00 18.30 60.38 12.08 Doc9 20.00 9.15 42.45 50.34 0.00 0.00 0.00 9.15 0.00 0.00 Doc10 10.00 0.00 35.38 41.95 0.00 0.00 0.00 0.00 0.00 0.00 Doc11 20.00 0.00 0.00 0.00 14.15 0.00 0.00 45.75 24.15 72.45 Doc12 20.00 0.00 0.00 0.00 14.15 0.00 0.00 54.90 24.15 60.38 Doc13 40.00 45.75 7.08 0.00 14.15 0.00 0.00 9.15 0.00 0.00 Doc14 30.00 27.45 7.08 0.00 7.08 0.00 0.00 0.00 0.00 0.00 Doc15 0.00 54.90 28.30 25.17 35.38 0.00 17.08 0.00 0.00 0.00 Doc16 0.00 45.75 35.38 25.17 28.30 0.00 0.00 0.00 0.00 0.00 number of instances was 16, the number of attributes was 10, the number of clusters was 2 and their centers IV. THE EXPERIMENTS DESIGN AND RESULTS ANALYSIS are shown in Table 4 1. The First Experiment: predicting the e-learners 3. The Third Experiment: predicting the e-learners interests using the Simple Fuzzy-KMeans Where the interests using the fuzzy K-Means and attributes number of instances was 16, the number of attributes selection using weka.attributeSelection.BestFirst. was 10, the number of clusters was 2 and their centers are shown in Table 3. 2. The Second Experiment: predicting the e-learners interests using the weka.clusterers.XMeans, Where the 313 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 . Table 3: Results of the first experiment Computer Software Computer Parallel The learners group science AI Programming eng. Networks LAN WAN Arch. Processors processing Cluster 0 number of cases belong to it are 10 ( 9 15 20.5 13.5 12 1.5 3.5 2.5 0 0 63%) Cluster 1 number of cases belong to it are 6 ( 3.3 0 1.66 1.66 8.33 0 0 12.5 18.33 20 38%) Table 3: Results of the Second experiment The learners group Computer Software Computer Parallel science -AI Programming eng. Networks LAN WAN Arch. Processors processing Cluster 0 number of cases belong to it 7.8 10 11.7 8.9 8.9 0 0.35 6.4 7.8 8.5 are 14 ( 88%) Cluster 1 number of cases belong to it 0 5 25 10 22.5 7.5 15 5 0 0 are 2 ( 13%) Table 4: Results of the Third experiment (The number of selected attributes are: 5) The learners group AI Programming Computer Processors Parallel Arch. processing Cluster 0 number of cases belong to it are 10 cases 63% 15 20.5 2.5 0 0 Cluster 1 number of cases belong to it are 6 cases 38% 0 1.666 12.5 18.333 20 correspond to the several ontologies. Ontology-based user V. SUGGESTIONS AND RECOMMENDATIONS profiles typically maintain sophisticated representations of personal interest profiles. These representations can be utilized The Semantic Web can be used to organize information in for effective information retrieval in the e-learning systems. concept structures, while web services allow the encapsulation of heterogeneous knowledge and modularization of the architecture. The key idea of the Semantic Web is to have data VI. CONCLUSION defined and linked in such a way that its meaning is explicitly interpretable by software processes rather than just being In this paper, we presented a method for constructing implicitly interpretable by humans. learner model that represents the user’s interests by analyzing The user profiles can maintain sophisticated representations the web-log to extract the interested terms in visited pages by of personal interest profiles. These representations can be learner. Then the fuzzy clustering is used to extract clusters of utilized for effective information retrieval. Fuzzy clustering output learner profiles The goal of incorporating the semantic allows an entity to belong to more than one cluster with web is to build the semantically enhanced user models. Fuzzy different degrees of accuracy, while hard clustering assigns technique is used to cluster the extracted data of learner model each entity exactly to one of the clusters. Thus, fuzzy to classifying the learners for their interests. We recommend clustering is suitable in constructing the learner profiles to use Ontology-based user profiles to maintain sophisticated representations such as (learner ontology).Such representation representations of personal interest profiles. of user profiles is useful because some information is not forced to fully belong to any one of the user profiles. Fuzzy clustering methods may allow some information to belong to ACKNOWLEDGMENT several user profiles simultaneously with different degrees of The authors wish to thank Taif University for the support accuracy. of this research and the anonymous reviewers for their Our proposed approach depends on using semantic web to valuable comments. extract the learner model. Fuzzy technique is used to cluster the extracted data of learner model to classifying the learners REFERENCES for their interests. This classifying the interests of learners  D. Vladan. (2006), SEMANTIC WEB AND EDUCATION, ISBN: 0- enable learning systems to handle learner as groups to 387-35417-4, Springer Science+Business Media, LLC. recommend them what they must teach corresponding to their  F. Christoph, (2005). User Modeling and User Profiling in Adaptive E- learning Systems, Master’s Thesis At Graz University of Technology. interests in the learner profile clusters.  R. Marek and K. Sayed. 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He working p1484.2/d7, 2000-11-28 Draft Standard for Learning Technology - now as lecturer in Taif university in Saudi Arabia and is Public and Private Information (PAPI) for Learners (PAPI Learner), assistant researcher in Central Laboratory of Agriculture Technical report Available Online: Expert Systems (CLAES) in Egypt. His current research http://www.edutool.com/papi/papi_learner_07_main.pdf interests focus on Semantic Web and Expert Systems.  Smythe, C., F. Tansey, R. Robson (2001), IMS Learner Information Package Information Model Specification, Technical report Available Mofreh A. Hogo is a assistant professor at Benha Online: http://www.imsglobal.org/profiles/lipinfo01.html. University, Egypt. He is a assistant professor of Computer Science and Engineering. Dr. Hogo holds a  12.  F. Christoph. (2005), User Modeling and User Profiling in PhD in Informatics Technology from Czech Technical Adaptive E-learning Systems, Master’s Thesis at Graz University of University in Prague, Computer Science and Engineering Technology, Copyright 2005 by Christoph Fr¨oschl. Dept. 2004. He is the author of over 40 papers that have  D. Trong, N Mohammed, L. Delong , and J. Geun. (2009), A been published in refereed international Journals Collaborative Ontology-Based User Profiles System, N.T. Nguyen, R. (Information Sciences, Elsiver, UBICC, IJICIS, IJCSIS, Kowalczyk, and S.-M. Chen (Eds.): ICCCI 2009, LNAI 5796, pp. 540– IJPRAI, ESWA, IJEL, Web Intelligence and Agent 552, Springer-Verlag Berlin Heidelberg. Systems, Intelligent Systems, international journal of NNW, IJAIT Journal of Artificial Intelligence Tools,  B. Qiu, W. Zhao. (2009), Student Model in Adaptive Learning System IJCI). based on Semantic Web, In First International Workshop on Education Technology and Computer Science, 978-0-7695-3557-9/09, IEEE, DOI 10.1109/ETCS.2009.466. Shehab Gamalel-Din is Full Professor in King Abdul-Aziz University, Faculty of Science, Computers Dept. Jeddah, KSA  W. Cuncun, H. Chongben, T. Hengsong.(2009), A Personalized Model and Al-Azhar University, Department of Computers and for Ontology-driven User Profiles Mining, 2009 International Systems Engineering Cairo, Egypt. He has Professional Symposium on Intelligent Ubiquitous Computing and Education, 978-0- Memberships in Institute of Electrical and Electronics Engineers 7695-3619-4/09, IEEE. (IEEE), USA, Association of Computing Machinery (ACM),  C´elia and T. Andrea G.B. (2006), An Evolutionary Approach to USA, and Egyptian Society for Engineers, Egypt. Ontology-Based User Model Acquisition, V. Di Ges´u, F. Masulli, and A. Petrosino (Eds.): WILF 2003, LNAI 2955, pp. 25–32, c_Springer- Verlag Berlin Heidelberg. Nagdy Mahmoud Nagdy is Professor of engineering  B. Qiu, W. Zhao. (2009), Student Model in Adaptive Learning System applications and computer systems, Department of based on Semantic Web, 2009 First International Workshop on Systems Engineering and Computer Engineering - Education Technology and Computer Science, 978-0-7695-3557-9/09, Faculty of Engineering Al-Azhar University. He IEEE, DOI 10.1109/ETCS.2009.466 received his PH.D in 1986. He has Supervision of some master's and doctoral degrees in the Department of  P. Jianguo, Z. Bofeng, W. Shufeng, W. Gengfeng, and W. Daming. Systems Engineering and Computer and Electrical (2007), Ontology Based User Profiling in Personalized Information Engineering. Service Agent, Seventh International Conference on Computer and Information Technology, 0-7695-2983-6/07, IEEE.  P. Jianguo, Z. Bofeng, W. Shufeng, W. Gengfeng. (2007), A Personalized Semantic Search Method for Intelligent e-Learning, 2007 International Conference on Intelligent Pervasive Computing, 0-7695- 3006-0/07, IEEE , DOI 10.1109/IPC.2007.48. 315 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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