The learners’ interest forms the essential characteristics of the learner profile in various applications, such as information retrieval, classification, and recommender systems. This paper proposes a method to improve learner interest extraction from the frequently used documents of the learner by exploring the concept of WordNet. Initially, the web log files of each learner are obtained from the learning management system, and then the frequently visited documents of each learner are downloaded and processed to identify domain-related words. The learner’s interest is then extracted initially using the standard vector space model and then improved using the semantic-based representation of WordNet. The WordNet identifies a set of semantic concepts related to the document words. To select the appropriate meaning of a word from a set of concepts, “Word Sense Disambiguation (WSD)” semantic similarity algorithm is used. The experiments were performed in NetBeans IDE using Java language and WordNet 2.1. The effect of the proposed method is examined with classification experiments, and the result proved that the use of WordNet concepts in learner interest retrieval shows better classification performance than compared to the existing method of term representation, thereby obtaining a classification accuracy of 89%.

Learning style is one of the major factors of student performance in any learning environment. Determining the learning style of students enhances the performance of learning process. This paper proposes an approach to classify students learning style automatically based on their learning behavior. One of the best widely used classifier algorithm is decision tree which is proposed in this paper. The main concern in decision tree classifier is the construction of significant rules which are required for accurately identifying learning styles. Lack of significant rules would result in misclassification of learning style. Hence, the main focus of this paper is to construct most significant rules which would strengthen the existing decision tree classifier to precisely and accurately detect the learning style of students. The student behavior is obtained from the web log files and then mapped with three learning dimensions of standard Felder Silverman learning style model. Subsequently, by employing significant rules in decision tree classifier, the student behavior has been automatically classified with high accuracy. This approach was experimented on 100 students for the online course created in Moodle Learning Management System. The evaluation result is obtained using inference engine with forward reasoning searches of the rules until the correct learning style is determined. The result is then analyzed with a confusion matrix of actual class and predicted class which shows that processing dimension shows variance whereas perception and input dimension were detected correctly with an average accuracy of 87%.

Student profile describes the best way a student prefers to learn. It includes information on student’s characteristics such as background knowledge, learning preference, styles, interest, goals etc. The major challenge that the students face in learning system is that they are unable to retrieve relevant information based on their requirements. One of the methods used to obtain the requirement of the students is to construct an efficient student profile which would reflect the true student needs. The proposed work is to develop an intelligent ontology-based dynamic student profile that provides semantic retrieval using fuzzy concepts. The approach starts with the collection of both static and dynamic data of students. The dynamic dataof students particularly student interest and learning style are obtained by weblog analysis using algorithms such as semantic based representation using WordNet and decision tree classifier algorithm based on Felder-Silverman learning style model (FSLSM). The retrieved data is then used to construct student profile using ontology in which automatic student profile updating is obtained using ontology -based semantic similarity algorithm using WordNet. Finally, semantic retrieval of student information from ontology is achieved by integrating fuzzy concepts using fuzzy linguistic variable and ‘fuzzy IF THEN’ rules. Fuzzy linguistic variable is used to make precise representation on the existing ontology concepts which facilitate more specific classification and semantic retrieval of information. The predictive model of student profile is designed with the implementation of ‘fuzzy IF THEN’ rules using forward chaining reasoning process in the existing ontology model. The inference engine predicts the preference of a new student based on the reasoning process done for specific conditions particularly on student interest and learning style. The experiments were performed using NetBeans IDE, OWL API and Protégé 4.2 beta editor. The experiment result shows the successful completion of student profile generation, updating, fuzzy semantic retrieval and prediction through utilization of fuzzy concepts in student profile ontology.

Interactive multimedia applications deal with computer delivered electronic system that deliver different types of content such as text, images, audio, video, animation, etc. in an interactive way. This paper deals with different streams of developing interactive multimedia applications using two different software platforms, Adobe Flash CS5 and HTML5 with CSS. The paper describes the developing platforms, comparisons and its implementation in the title Oman Tourism. The developed application in two different platforms are evaluated with a set of 35 students based on four performance criteria in order to know the students’ response on the usage of media. Finally, the experimental result indicates that the students are comfortable with the usage of Adobe Flash CS5 compared to HTML with CSS.

Learning style is an important factor that accounts for the individual student learning in any learning environment. Every student has a different learning style and different ways to percept, process, retain and understand new information. In this paper, a new approach is proposed to classify students learning style automatically and dynamically depending on their learning behavior in a learning management system (LMS). There have been several approaches proposed for automatic learning style detection. One of the widely accepted and frequently used classification techniques is the decision tree classifier. The decision tree classifier mainly depends on the construction of strong decision rules which are required to identify learning styles accurately. The lack of strong decision rules would lead to the misclassification of individual students learning style. Hence, this paper mainly focuses on the construction of strong decision rules to strengthen the existing decision tree classifier to accurately and precisely classify students learning style thereby improving the classification accuracy. The proposed approach has experimented with an average of 300 students enrolled for the online courses in Moodle LMS. Initially, the students' behavior are extracted from the web log files of LMS and then preprocessed to build decision tree classifier using strong decision rules based on the three learning dimensions of standard Felder Silverman learning style model (FSLSM). The evaluation result is obtained using the inference engine with forward reasoning searches of the rules until the correct learning style is determined. Based on the result obtained, the prediction of learning style is done for the new students automatically and accurately using the significant rules built in the decision tree classifier. The experimental result proved that the processing dimension shows variance in classification whereas perception and input dimension shows less variance with an average accuracy of 87%.

Keyword extraction is one of the most important themes in E-Learning environments. In this paper a model which could improve the extraction of keywords from the frequently visited documents using WordNet are employed. First of all, learner’s words are selected using TFIDF algorithm. Then the related words are semantically represented using WordNet and then updated to the learner profile using semantic similarity using WordNet. The experiment shows the successful extraction and updating of learner interest to the learner profile. Keywords—E-Learning; Keyword extraction; WordNet

Nowadays we are living in the era of speed and technology. There are a lot of facilities to make the life easier. This project implements a student self-services system which allow student to be connected with World Wide Web. Student Self Service system provides all services needs to access to register for studying essential information that student will needs throughout their studies at College. The main goal of the project is to develop registration system for students at College. This project allows students in general to register for each semester by their own. The students need to access to the system through internet to register at College. This system is very suitable for student belonging in Muscat College as they can register every semester by their own. Also it saves time and effort for both student and staff. This system is a website application which is developed using ASP.NET framework and the result shows the successful completion of registration process and its related services.

The success of E-Learning system depends on the retrieval of relevant learning contents of learner. The best method to acquire learner needs is to construct an efficient learner profile which has to comply with the Semantic Web. Semantic Web relies heavily on formal ontologies to structure data. The proposed work suggests an approach to construct an efficient ontology based semantic learner profile by achieving the following objectives: First step is to collect static data using questionnaire and dynamic data using web log files. Second step is to preprocess weblog files to retrieve learner interest using semantic representation of WordNet and to retrieve learning style using decision tree classifier with significant rules. Third step is to construct an ontology using the retrieved data and to update ontology automatically using semantic similarity with WordNet. Finally an efficient fuzzy semantic retrieval is obtained using fuzzy linguistic variable which improves information retrieval and filtering.

E-learning refers to the use of electronic technologies to deliver and facilitate training anytime, anyplace. The major challenge in e-learning is that the learner is unable to extract relevant information on the web. Ontology-based semantic retrieval is a hotspot of current research. Ontologies play an important role in the development of knowledge. This paper proposes ontology based semantic retrieval for learner profile using Resource Description Framework (RDF). First, a data model is constructed to represent learner profile information on the Semantic Web. Then, semantic retrieval is obtained by applying the query language SPARQL (Simple Protocol and RDF Query Language). In addition SWRL (Semantic Web Rule Language) rule language is also applied to increase the expressiveness of OWL language. Experiments were performed using the editor Protégé 4.2 beta. The result shows the successful semantic retrieval of learner profile information on the Semantic Web.

E-Learning is a process in which electronic medium is used to access the defined set of applications and processes. In E-Learning environment, studies of the behaviors of the learner are essential to provide an adaptive E-Learning system. Ontology has the potential to play an important role in representing an area of knowledge. This paper proposes ontology to classify learner profile based on their activities and personal information. Two specific examples were designed to show the automatic classification of learner profile. Experiments were performed using the OWL reasoner Pellet and editor Protégé 4.2 beta version. The results of our performance evaluation show that the ontology is able to classify and locate learner profile, according to the desired area, age, interest, profession etc.

Learning is a process in which electronic medium is used to access the defined set of applications and processes. In e- Learning environment, automatic classification of learning materials is essential due to lack of common vocabulary of the knowledge area in some context. Ontology has the potential to play an important role in representing an area of knowledge. This paper proposes ontology to automatic classification of learning materials to the Project Management knowledge area domain. This ontology aims to facilitate the search for learning materials within the given domain. The Project Management Body of Knowledge (PMBOK) was used to define the hierarchical structures of knowledge as it is intended to cover broad area of Project Management. The Rational Unified Process (RUP) was used to add axioms to define the relationships between the main concepts. Two specific examples were designed to show the automatic classification of learning materials. Experiments were performed using the OWL reasoner Pellet and editor Protege 4.2 alpha version. The results of our performance evaluation show that the ontology is able to classify and locate learning materials from the Project Management area, according to the desired area, role, artifact and activity. KeywordsPMBOK; RUP; Project Management; Protege 4.2 alpha

Multimodal biometric systems have been widely used to overcome the limitations of unimodal biometric systems and to achieve high recognition accuracy.Among various multimodality options, fingerprint and finger vein has gained much attention among researchers to combine accuracy, universality and cost efficiency of the solution.This study proposes a survey of multimodal fingerprint and finger vein recognition techniques.Most of the existing work is systematically described and compared in five parts, i.e., fingerprint and finger vein image acquisition, preprocessing, feature extraction, feature matching and fusion.According to the available work in literatures and commercial utilization experiences, fingerprint and finger vein multimodality ensures higher performance and spoofing resistance.This multimodal technology has reached an unparalleled level of security, accuracy and performance.

The knowledge representation framework – Ontology has an important role in instructional design and the development of courses in e-Learning. Ontology can be used to represent knowledge about content. Instructors can utilise this in creating content of the courses in e-Learning and learners can avail this in accessing content in a knowledge guided way. The application of ontology for the educational context is still unclear. For numerous subjects, ontology does not exist. We have taken the course software engineering as the case study in this regard. The domain experts are mainly focused here to develop ontology to use in the delivery of course content. The relationship types are mainly concentrated here which helps us to model the domain more efficiently. The implementation of the work is done using protege 4.1 beta version.