Objective The objective of this study was to investigate the intra and interdevice reliability of two tooth color measurement devices: EasyShade (ES) and SpectroShade Micro (SSM). Materials and methods Tooth color was measured in six maxillary and mandibular. L*, a* and b* values and shade matches to VITA Classical and Vitapan 3D-Master shade guide systems were determined for all teeth. ÄE was assessed. Paired t-test and correlation coefficient (ICC) were used. Results Intradevices no significant differences (p > 0.05) were found between L*, a*, b*. Interdevice L* and b* were significantly higher for ES (p < 0.05), while a* was significantly higher for SSM (p < 0.05). ÄE showed no significant interdevice difference (p > 0.05). Intradevices ICC values were higher for ES but not significant (p > 0.05). Discussion The null hypotheses that they present no differences in their color measuring within devices or shade systems is accepted, but the results allow to reject the null hypotheses that they present no differences in their color measuring or shade systems between devices. Conclusion Both EasyShade (ES) and SpectroShade Micro (SSM) show excellent repeatability and so they can be used in office to evaluate tooth color or to assess color changes after treatment. Clinical significance Dental color can be diagnosed using dental spectrophotometers, allowing to detect in an objective way therapeutic dental color changes. How to cite this article Llena C, Lozano E, Amengual J Forner L. Reliability of Two Color Selection Devices in Matching and Measuring Tooth Color. J Contemp Dent Pract 2011; 12(1):19-23.

Articles AI MAGAZINE Modeling is regarded as fundamental to human cognition and scientific inquiry (Schwarz and White 2005). It helps learners express and externalize their thinking, visualize and test components of their theories, and make materials more interesting. Particularly, the importance of learners constructing conceptual interpretations of system behavior has been pointed out many times (Mettes and Roossink [1981], Elio and Sharf [1990], Ploetzner and Spada [1998], Frederiksen andWhite [2002]). Modeling environments can thus make a significant contribution to the improvement of science education.A new class of knowledge construction tools is emerging that uses logic-based (symbolic, nonnumeric) representations for expressing conceptual systems knowledge (

The purpose of this study was to evaluate the use of the Toothguide Training Box (TTB) for training dental students in color identiication. The seventy-four volunteers who took part in the study attended a seminar on the Vita 3D Master Guide (MG) and the TTB system as well as a demonstration of the equipment before training began. At the end of the training they took the TTB inal test. In addition, the participants were asked to recognize ten MG shade tabs in a blind manner before and after TTB training. The training times and percentages of correct answers were compared using the paired t-test. Variations in scores with training times and percentages of correct answers before and after training were compared using the ANOVA test. Training times between thirty-one and thirty-eight minutes provided a signiicantly higher mean score than training times of over thirty-eight minutes (p=0.036). The percentage of correct answers obtained with the MG before and after training shows a positive correlation. High TTB scores are associated with a greater number of correct answers in MG shade tab selection.

Abstract. Conceptual modelling tools allow users to construct formal representations of their conceptualisations. These models are typically developed in isolation, unrelated to other user models, thus losing the opportunity of incorporating knowledge from other existing models or ontologies that might enrich the modelling process. We propose to apply Semantic Web techniques to the context of conceptual modelling (more particularly to the domain of qualitative reasoning), to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for people who start modelling. This paper describes how semantic grounding techniques can be used during the creation of qualitative reasoning models, to bridge the gap between the imprecise user terminology and a well defined external common vocabulary. We also explore the application of ontology matching techniques between models, which can provide valuable feedback during the model construction process.

Problem-based learning has been applied over the last three decades to a diverse range of learning environments. In this educational approach, different problems are posed to the learners so that they can develop different solutions while learning about the problem domain. When applied to conceptual modelling, and particularly to Qualitative Reasoning, the solutions to problems are models that represent the behaviour of a dynamic system. The learner's task then is to bridge the gap between their initial model, as their first attempt to represent the system, and the target models that provide solutions to that problem. We propose the use of semantic technologies and resources to help in bridging that gap by providing links to terminology and formal definitions, and matching techniques to allow learners to benefit from existing models.