Web-based training is increasingly gaining popularity both in industry and education. Although a number of studies, experiments, and developments have been conducted in this area, few evidence cases of success have been reported. One likely reason for the lack of success is that just placing lecture notes on the web does not train. This situation can be improved through the use of training software such as Intelligent Tutoring Systems (ITS). ITS incorporate built-in expert systems in order to monitor the performance of a learner and to personalize instruction on the basis of adaptation to learners ’ learning style, current knowledge level, and appropriate teaching strategies. However, researchers and developers quickly find out that developing such systems is an enormous task, which is further complicated by the fact that one cannot simply borrow tools from other systems and incorporate them due to various levels of incompatibility at the programming and knowledge base level. To allow for more general ITS, which means that it can be used in other domains, it is required that ITS should be designed and implemented so as to support easy modification of lecture content, modification of decision rules in the expert system, and to support various methods to measure the performances of learning. In this paper, we propose a general framework and data model for web-based adaptive ITS that allows knowledge to be stored in such a way that is not only independent of the knowledge domain, but also supports the storage of transfer knowledge relationships and prerequisite knowledge relationships. We expect that our unified approach could contribute to the establishment of cumulative research traditions in ITS studies.

Users of educational hypertext are faced with the challenge of creating meaning both within and between texts. Cohesion is an important factor contributing to whether a reader is able to capture meaning and comprehend text. When readers are required to fill in conceptual gaps in text, comprehension can fail if they do not have sufficient knowledge. Cohesion helps low-knowledge readers to create a more coherent mental representation of the text. However, text that is too cohesive can inhibit active processing, and thus reduce coherence for more knowledgeable readers. Similar patterns have been found for hypertext, which requires readers to create coherence between multiple electronic texts. Domain novices are in greater need of explicit pointers to important links between documents and gain from having less control over system navigation. Domain experts are in less need of scaffolding within the system. We discuss the use of a multimedia reading strategy training program to help low-knowledge readers better understand less cohesive text. Finally, we discuss four principles to guide hypertext development geared toward improving coherence and metacognitive engagement.

Hypermedia proponents suggest that its ability to make information available in a multitude of formats, provide individual control, engage the learner, and cater to various learning styles and needs makes it the harbinger of a new learning revolution. However, despite nearly two decades of research on hypermedia in education, researchers have not yet solved some of the basic issues raised by this technology. In this chapter, we review empirical studies performed since Dillon and Gabbard’s (1998) landmark review in an attempt to analyze and draw conclusions from this diverse and extensive literature.

This study focused on the relationship between Kolb learning styles and the enduring time of online learning behaviors, the relationship between Kolb learning styles and learning outcomes and the relationship between learning outcomes and the enduring time of a variety of different online learning behaviors. Prior to the experiment, 104 students majoring in Educational Technology completed Kolb’s Learning Style Inventory (KLSI). Forty students were chosen to be subjects in an online learning experiment. Results indicated that there was a significant effect of Kolb learning style on the total reading time and total discussion time of the subjects. Although there was no significant effect between Kolb learning styles and learning outcomes, data from the experiment showed that the mean of learning outcomes of Convergers and Assimilators was higher than that of Divergers and Accommodators. There were two models of linear regression between learning outcomes and the enduring time of different online learning behaviors. Both of them were significant at the 0.001 level, and they accounted for 54.9 % and 60.8 % of the variance of the dependent respectively. The findings of this study were instrumental to instructors and moderators of online courses. First, instructors using online courses should seriously consider the diversity of learning styles when designing and developing online learning modules for different students. Second, they should provide a large number of electronic documents for students and give

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