Intelligent web-based learning system with personalized learning path guidance
Introduction
Traditional teaching resources, such as textbooks, typically guide the learners to follow fixed sequences to other subject-related sections related to the current one during learning processes. Web-based instruction researchers have given considerable attention to flexible curriculum sequencing control to provide adaptable, personalized learning programs (Brusilovsky et al., 1998, Jih, 1996, Lee, 2001, Lin and Hsieh, 2001, Mia and Woolf, 1998, Papanikolaou and Grigoriadou, 2002, Tang et al., 2000, Tang and Mccalla, 2003). Curriculum sequencing aims to provide an optimal learning path to individual learners since every learner has different prior background knowledge, preferences, and often various learning goals (Brusilovsky and Vassileva, 2003, Chen et al., 2005, Roland, 2000, Weber and Specht, 1997). In an educational adaptive hypermedia system, an optimal learning path aims to maximize a combination of the learner’s understanding of courseware and the efficiency of learning the courseware (Roland, 2000).
Moreover, as numerous web-based tutoring systems have been developed, a great quantity of hypermedia in courseware has created information, and cognitive overload and disorientation (Berghel, 1997, Borchers et al., 1998), such that learners are unable to learn very efficiently. To aid more efficient learning, many powerful personalized/adaptive guidance mechanisms, such as adaptive presentation, adaptive navigation support, curriculum sequencing, and intelligent analysis of student’s solutions, have been proposed (Chen et al., 2005, Papanikolaou and Grigoriadou, 2002, Tang and Mccalla, 2003, Weber and Specht, 1997). Nowadays, most adaptive/personalized tutoring systems (Lee, 2001, Papanikolaou and Grigoriadou, 2002, Tang and Mccalla, 2003) consider learner/user preferences, interests, and browsing behaviors when investigating learner behaviors for personalized services. However, these systems neglect the importance of learner ability when implementing personalized mechanisms. On the other hand, some researchers emphasized that personalization should consider levels of learner knowledge, especially in relation to learning (Chen et al., 2005, Chen et al., 2006, Papanikolaou and Grigoriadou, 2002). That is, the abilities of individuals may be based on major fields and subjects. Therefore, considering learner ability can promote personalized learning performance.
Over the years, designers of web-based learning have evolved several common lesson structures for different learning occasions. These lesson structures include the classic tutorial lessons, active-centered lessons, learner-customized tutorial lessons, knowledge-placed tutorial lessons, exploratory tutorial lessons, and generated lessons (Horton, 2000). Among the six kinds of lessons, the generated lessons aim to customize learning for those who have very specific needs and not much time or patience to complete topics they have learned (Horton, 2000). The generated lessons tailors a learning sequence based on the learner’s answers to questions on a pre-test or questionnaire at the start of the lesson (Horton, 2000). To construct a personalized learning path based on simultaneously considering courseware difficulty level and learning concept continuity during learning processes, a genetic-based curriculum sequence scheme is here presented to customize personalized learning path. The proposed approach is based on a pre-test to collect incorrect learning concepts of learners through some randomly selecting testing items (Hsu & Sadock, 1985), then the genetic algorithm is employed to construct a near optimal learning path according to these incorrect response patterns of pre-test. The goal of this study aims to help learners learn more effectively and efficiently by skipping the learning concepts that learner has given correct responses for the corresponding testing items in a pre-test process. Since the fitness function of genetic algorithm is determined by the difficulty parameter of courseware and the concept relation degree between two successive courseware in a generated learning path, the proposed curriculum sequencing scheme can generate high quality learning paths for individual learners. Experimental results indicated that the proposed genetic-based personalized e-learning system with curriculum sequencing mechanism generates appropriate course materials to learners based on individual learners’ requirements, and help them learn more effectively and efficiently in a web-based learning environment.
Section snippets
System architecture
This section describes the system architecture, system components, and details of the learning procedures for the proposed genetic-based personalized e-learning system.
Courseware modeling process
The courseware modeling process presents a detailed courseware design procedure to establish the difficulty parameters of courseware and courseware contents for personalized courseware generation. This study presents a statistics-based method derived from computerized adaptive testing (CAT) theory (Hsu & Sadock, 1985) through a conscientious test process to determine the difficulty parameters of courseware. The detailed flowchart of the courseware modeling process is illustrated as Fig. 2.
To
Evaluating concept relation degrees among courseware
In order to facilitate easier courseware concept relation analysis, all courseware in the courseware database has followed the standard of the metadata information model of Sharable Content Object Reference Model (SCORM) 1.2 (SCORM version 1.2-The SCORM Content Aggregation Model, 2001). Restated, each courseware in the courseware database has a corresponding XML binding file to record important SCORM metadata, which conveys the main courseware concept. In the meanwhile, this study also
Personalized learning path generation based on genetic algorithm
This section explains how to generate a personalized learning path for an individual learner utilizing the genetic algorithm (Rothlauf, 2002).
Experiments
Currently, the proposed genetic-based personalized e-learning system is available on the web to simultaneously provide both the freely browsing learning mode and the learning mode of curriculum sequencing recommendation. To verify the quality and effectiveness of planned learning path in the learning mode of curriculum sequencing recommendation for personalized web-based instruction, some elementary school students who were majoring in the course unit of “Fraction” of elementary school
Conclusion
This study proposes a genetic-based personalized learning path generation scheme for individual learners to support personalized web-based learning. The proposed personalized learning path generation scheme can simultaneously consider courseware difficulty level and the concept continuity of successive courseware according to the incorrect testing responses in a pre-test while implementing personalized curriculum sequencing during learning processes. Compared to the freely browsing learning
Acknowledgment
The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC95-2520-S-004-001.
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