A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System (ITS) in E-learning

A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System (ITS) in E-learning

Sami A. M. Al-Radaei, R. B. Mishra
Copyright: © 2011 |Pages: 16
DOI: 10.4018/jiit.2011100104
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

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.
Article Preview
Top

1. Introduction

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. Section 2 provides the background. Section 3 puts across problem description. Section 4 describes our system architecture and its components. Section 5 is concerned with our courseware design. The semantic values computation is presented in Section 6. Implementation is presented in Section 7. Experimentation and Comparison with other models is presented in Section 8, and Conclusion is given in Section 9.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2024)
Volume 19: 1 Issue (2023)
Volume 18: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing