Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency- Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.

Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency- Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.

Test is one of the tools that are used to evaluate learner's achievement. Most of test scoring in e-learning systems are for true-false and fill in blank questions. Description questions are human efforts and time consuming. A courseware with its questions bank had been built based on ontology. Extracting the semantic keywords from the learner's answer would be used to score the answer. In this paper we introduce a method to score the learner's answer based on semantic keywords in the question's ontology. Position priority and frequency of occurrence the semantic keywords have been taken in our calculation. This scoring is used to evaluate the learner's performance to answer description questions.

E-learning has become an alternative solution for the traditional learning. There is a need to manage the learning materials in e-learning environments in order to deliver it to learners according to their requirements. Semantic Web Services (SWS) aim at developing a machine understandable and common conceptual framework which share and accumulate concepts from different web service resources to meet a particular objective in question. Different SWS composition methods have been developed for different purposes and objectives. In this paper we have developed an Agent-based SWS composition method using two sets of agents i.e. Service Requester Agent (SRA) and Service Provider Agent (SPA) to represent the user's side and the solution side respectively for the problem of a course composition in e-learning systems. The SRA corresponds to requirements of different ebook/chapter and the SPA corresponds to books containing the relevant and required chapters in courseware. The course composition is primarily based on the important and relevant prime keywords in a courseware. Learning materials and other actors are described semantically in form of ontologies. Also, we present the use of reasoning rule to infer different relations between Agents, ebook/chapter and other actors in the proposed model.