Multimedia teaching materials are widely applied in various disciplines. More resources are provided by authorities to encourage elementary school teachers to use them. The resources provide an opportunity for teachers to share teaching resources for students. In this study, the technology acceptance model (TAM) was used as the basic model to explore the effects of the information technology (IT) environment on the perceived usefulness, perceived ease of use, and attitude towards using multimedia, and the relevance and influence of these attitudes on behavioral intention. There are 2317 teachers in Chiayi county, and 460 participants were selected by stratified random sampling. The results showed that the ease of use of the multimedia material would enhance the intention to use. The attitude toward use also influences the intention to use.

The aim of this study is to discover the influence of multimedia materials of Taekwondo Aerobic on students' learning attitude with diverse learning styles. Taekwondo Aerobic class is a new subject for junior high school teachers in Taiwan. The junior high school students are promoted to learn this activity to improve their health. With the advance of digital technology, multimedia-based teaching style has been emphasized and applied. Not every student handles the material well and their learning styles also influence their learning achievement. Participants were students on grade seventh and eighth. Students are divided into two groups, with one experiment group adopting multimedia-based teaching, and one control group adopting traditional teaching respectively. Students are given questionnaires after the experiment. There were 101 valid questionnaires. The results show that adopting multimedia-based teaching style promotes students' learning attitude. Using multimedia-based teaching style has significant effects on students' learning attitude with different learning styles in teaching Taekwondo Aerobic.

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On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms.

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