A web-based, stage-matched message intervention was designed in order to see the effect on the physical activity of young Taiwanese women with regard to several variables. The intervention was guided by the Transtheoretical Model (TTM) and was evaluated using a pre-post-test control group design. One hundred thirty female freshmen, enrolled in a nursing class in a university in Taipei, completed the survey at three different points in time. They were assigned to three groups: an experimental group with stage-matched messages on the website, a generic group with non-stage-matched messages on the website, and a control group that was given only lectures but had no access to the website. Results indicated that the subjects in the stage-matched group improved most in terms of stage-of-exercise and amount of physical activity, followed by the generic group at immediate post-test; meanwhile, the control group had a tendency toward downward change. The effect did not persist, however, for the first two groups. A significantly higher level of exercise self-efficacy was found in the stage-matched group than in the other two groups immediately after the intervention. The results suggested that a website conveying theory-based messages can serve as a useful tool for improving young females' physical activity and exercise self-efficacy. Additionally, we need research that explores different degrees and dimensions of tailoring in order to find the optimal degree of audience segmentation.

Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL). INDEX TERMS Classification rule mining, intelligent tutoring systems, ontologies, pedagogical rules, semantic web, web ontology language (OWL).

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