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.
TopIntroduction
In a traditional classroom an instructor teaches the course using textbook and syllabus that covers the course in sequence. Students then follow fixed learning path, since they have no alternative learning path. Moreover, these students are with different prior knowledge, performance, preferences and often learning goals. Course sequencing is a well-established technology in the area of intelligent tutoring system (ITS), it is one of the vital aspects in ITS to provide individual course for each learner by dynamically selecting the most suitable and optimal learning path (Mishra & Mishra, 2010). Most of the researchers (Chengling & Liyong, 2006; Nguyen Viet, 2008; Norsham, Norazah, & Puteh, 2009) generate the learning path sequencing based on the relation between the course-wares and they ignore the importance of the semantic of the keywords in the course.
The prime objective of our work is to develop and build dynamic courseware sequencing method based on the relation between the course-wares. This relation is based on the semantic value of the keywords in each courseware. There are two values of the keyword’s semantic value, one is courseware semantic value and the other is coursework semantic value. Both values give us the importance of the keyword in the courseware and in the coursework, where coursework consists of almost all the course-wares. We developed a learning system for Java language programming and it is implemented in Java platform, using MySQL for database and Tomcat as web server.
The rest of the contents of the paper are divided into the following sections. The next section provides the background. The following section puts across problem description. Afterward, there is a section that describes our system architecture and its components. Later in the chapter, our courseware design is covered. A discussion of the semantic values computation follows. Implementation Experimentation and Comparison with other models is presented after that, and the Conclusion is given in the last section.