This article presents the educational possibilities of Web-based science education using a desktop virtual reality (VR) system. A Web site devoted to science education for middle school students has been designed and developed in the areas of earth sciences: meteorology, geophysics, geology, oceanography, and astronomy. Learners can establish by themselves the pace of their lessons using learning contents considered learner level and they can experiment in real time with the concepts they have learned, interacting with VR environments that we provide. A VR simulation program developed has been evaluated with a questionnaire from learners after learning freely on the Web. This study shows that Web-based science education using VR can be effectively used as a virtual class. When we consider the rapid development of VR technology and lowering of cost, the study can construct more immersive environments for the education in the near future.

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We present the educational possibilities of the Web based virtual experiment (VE) environments in the science education. As there are a lot of things that cannot be experimented with in the lab among the contents of experiment of science education, there are a lot of difficulties in the teaching-learning process. Therefore, we have developed virtual experiment environments on the Web designed to be compatible to the learner levels through level analysis in the learning contents. The students can select the learning level in the exploring step of learning cycle model: regular, advanced and remedial courses, according to the degree of their understanding or interest about the learning topic. The virtual experiment environments will support students to learn scientific phenomena and concepts focusing on: radiation balance, the earthquake waves, the earth's crust structure, the movement of sea water, and solar system in the science field of middle school. The VE environments have been evaluated to the responses of learners on a Web. This study shows that the Web-based VE environments in science education can be effectively used as a virtual class.

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Abstract. This paper presents a new approach method to recognize facial expressions in various internal states using manifold learning (ML). The manifold learning of facial expressions reflects the local features of facial deformations such as concavities and protrusions. We developed a representation of facial expression images based on manifold learning for feature extraction of facial expressions. First, we propose a zero-phase whitening step for illuminationinvariant images. Second, facial expression representation from locally linear embedding (LLE) was developed. Finally, classification of facial expressions in emotion dimensions was generated on two dimensional structure of emotion with pleasure/displeasure dimension and arousal/sleep dimension. The proposed system maps facial expressions in various internal states into the embedding space described by LLE. We explore locally linear embedding space as a facial expression space in continuous dimension of emotion.