• Journal of Science Education
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• v.38 no.3
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• pp.703-717
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• 2014

In physics, metal simulation is an important mechanism to understand and create concepts. If students have difficulty in mental simulation, understanding the concept of physics also gets difficult. By providing guide for spatial manipulation to students, 3D simulation contents can help them understand the concept of physics. In this study, the 3D simulation contents developed to help understanding the concept of light going straight and shadow is applied to 20 college students. The results, Hake gain is 0.93, showing high level of understanding about the class. In addition, through mental simulation, students predict the phenomenon well about the new context. This is shown that students' understanding of concept through 3D simulation contents are carried out well.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.492-497
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• 2014

In this paper, we propose a method of retrieving opinioned tweets in Twitter, which is the one of the popular Social Network Services and shares diverse opinions among various users. In typical opinion retrieval systems, they may consider the presence of sentiment phrases (subjectivity) as the important factor even if the subjective phrases are not related to a given query or speaker. To alleviate these problems, we utilized the syntactic structure of a sentence to identify the relationships between 1) subjectivity-query and 2) subjectivity-speaker and 3) the syntactic role of subjectivity. Besides, our learning-to-rank approach is trained to retrieve opinioned tweets based on query-relevance, textual features, user information, and Twitter-specific features. Experimental results on real world data show that our proposed method can achieve better performance than several baseline methods in terms of precision and nDCG.