The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia form in a Web-based educational system makes learning a task-driven process. It motivates learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and to adaptively provide learning paths. However, although most personalized systems consider learner preferences, interests and browsing behaviors when providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended curriculums are matched to each other.
Therefore, our proposed approach is based on the evolvement technique through computerized adaptive testing (CAT). Then the genetic algorithm (GA) and case-based reasoning (CBR) are employed to construct an optimal learning path for each learner. This paper makes three critical contributions: (1) it presents a genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing; (2) it illustrates the case-based reasoning to develop a summative examination or assessment analysis; and (3) it uses empirical research to indicate that the proposed approach can generate the appropriate course materials for learners, based on individual learner requirements, to help them to learn more effectively in a Web-based environment.
The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia form in a Web-based educational system makes learning a task-driven process. It motivates learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. However, the structure of the presented domain and the content are usually presented in the same way, without taking into account the learners’ goals for browsing, their experience, their existing knowledge, etc. This is an issue that needs further attention, especially when it comes to Web-based instruction, where the learners’ population is usually characterized by considerable heterogeneity with respect to background knowledge, age, experiences, cultural backgrounds, professions, motivation, and goals, and where learners take the main responsibility for their own learning.
Curriculum sequencing is a well-established technology in the field of intelligent tutoring system (ITS). The idea of curriculum sequencing is to generate an individualized course for each student by dynamically selecting the most optimal teaching operation (presentation, example, question, or problem) at any given moment. By optimal teaching operation we mean an operation that in the context of other available operations brings the student closest to the ultimate learning goal. Most often the goal is to learn a required set of knowledge up to a specific level in a minimal amount of time. However, it is easy to imagine other learning goals, such as minimizing student error rates in problem solving.
Various approaches to sequencing have been explored in numerous ITS projects. The majority of existing ITSs can sequence only one kind of teaching operation. For example, a number of sequencing systems including the oldest sequencing systems (Barr et al., 1976 and Brusilovsky, 1993) and some others (Brusilovsky, 1993, Brusilovsky and Vassileva, 2003 and Rios et al., 1999) can only manipulate the order of problems or questions, an approach usually called task sequencing. A number of systems can do sequencing of lessons – reasonably big chunks of educational material complete with presentation and assessment (Brusilovsky, 1994 and Capell and Dannenberg, 1993). The most advanced systems are able to sequence several kinds of teaching operations such as presentation, examples, and assessments (Brusilovsky, 1992, Chen, Lee, et al., 2005, Chen, Chang, et al., 2005 and Khuwaja et al., 1996).
One could say that sequencing is an excellent technology for distance education. Indeed, sequencing is presently the most popular technology in Web-based ITS (Papanikolaou & Grigoriadou, 2002). Therefore, the proposed approach is based on a pre-test to collect incorrect learning concepts of learners through the computerized adaptive testing (CAT) (Hsu & Sadock, 1985). Afterwards the genetic algorithm and case-based reasoning are employed to construct a near-optimal learning path according to these incorrect response patterns of the pre-test.
The rest of this paper is organized as follows: Section 2 describes the related literatures review, and we present the research methodology in Section 3. This is followed by a description of the proposed system in Section 4, while the evaluation of the system is reported in Section 5. Finally, we draw our conclusion in Section 6.
