National Taichung University of Education
Taichung, Taiwan

"NTCU" redirects here. For the Ukrainian public broadcaster, see National Television Company of Ukraine.The National Taichung University of Education is a university in West District, Taichung, Taiwan. Founded in 1899 at the Confucian Temple in Changhwa and moved to the current site in 1923.In 2005, the University was renamed National Taichung University, consisting of the College of Education, College of Humanities and Liberal Arts, and College of Mathematics and Information Science. In 2008, departments of management were established, and would be developed for the future college of management. Wikipedia.

Time filter
Source Type

Liu T.-M.,National Taichung University of Education
Journal of Environmental Planning and Management | Year: 2017

This study described the paradox created by the efforts of a conservation organization, which caused an emerging threat to the survival of an endangered species, as well as proposed recommendations to mitigate this paradox. In-depth interviews were conducted to investigate the problems encountered during the implementation of conservation practices by green sea turtle conservation workers (volunteers) in Lanyu, Taiwan. The staff believed that the implementation of the green sea turtle conservation measures violated the traditional cultural beliefs of the local residents (Tao/Yami ethnic group), which caused resentment by the residents towards the organization. Consequently, the residents did not cooperate with the organization and even discouraged conservation activities. In addition, green sea turtle ecotourism promoted by the conservation organization in recent years violated local customs of not having contact with green sea turtles or entering their habitats. © 2017 Newcastle University

Lee Y.-H.,National Taichung University of Education | Wu J.-Y.,National Chiao Tung University
Computers and Education | Year: 2013

Research showed distinct effects of different online activities on reading literacy or learning outcomes; however, no explanation about this link was provided. The current study investigated the effects of two genres of online reading activities on reading literacy based on knowledge of metacognitive strategies in a mediation analysis. Participants were 87,735 fifteen-year-old students (49.8% girls) across 15 regions in the PISA 2009 dataset. We divided online reading activities into social entertainment and information-seeking activities and controlled for gender, socioeconomic status, and the availability of Information and Communication Technologies (ICT) at home and at school. The indirect effects of knowledge of metacognitive strategies helped to explain why social entertainment and information-seeking activities would predict reading literacy differently. More frequent information-seeking activities predicted better knowledge of metacognitive strategies, which in turn predicted better reading literacy, while more frequent social entertainment activities predicted poorer knowledge of metacognitive strategies, which in turn led to poorer reading literacy. Suggestions were made to guide students in engaging in more online information-seeking reading activities, and incorporate instruction of metacognitive strategies for both online and offline reading, thereby improving students' reading literacy in both printed and digital formats. © 2013 Elsevier B.V. All rights reserved.

Huang H.-Y.,Fu Jen Catholic University | Kuo B.-C.,National Taichung University of Education
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

For the classification among different land-cover types in a hyperspectral image, particularly in the small-sample-size situation, a feature-extraction method is an approach for reducing the dimensionality and increasing the classification accuracy. Fisher's linear discriminant analysis (LDA) is one of the most popular feature-extraction methods. However, it cannot be applied directly to the classification of hyperspectral image because of several natures of Fisher's criterion. Nonparametric discriminant analysis (NDA) and nonparametric weighted feature extraction, on the other hand, are two extensions of LDA with a creative idea about emphasizing the boundary structure of class distributions. However, the overlap situation was not considered in these methods and thus decreased the robustness of these methods. In this paper, a new feature-extraction method is introduced based on a structure named double nearest proportion. This structure enables the proposed method to reduce the effect of overlap, allows a new regularization technique to be embedded, and includes LDA and NDA as special cases. These properties enable the proposed method to be more robust and thus, generally, have better performance. © 2010 IEEE.

Hsu K.-T.,National Taichung University of Education
Expert Systems with Applications | Year: 2011

For life insurance companies, identifying potential buyers of investment-linked insurance from among their existing policyholders may be an effective marketing strategy. The purchase decision, i.e. our dependent variable, is a binary variable. In the current study, we apply artificial neural networks to predict policyholder purchase decision of investment-linked insurance and compare the results with that of logistic regression. Because policyholders of investment-linked insurance bear the investment risk, their risk attitude should have a great impact on their purchase decision. We take financial risk attitude and general risk attitude into account simultaneously. Grey clustering statistic offers an alternative for classifying policyholder risk attitudes. We find that grey clustering is better suited to back propagation neural networks; while the average-and-standard-deviation method is better in combination with logistic regression. Further, financial risk attitudes rather than general risk attitudes may be major influences on policyholders' purchase decisions. © 2010 Elsevier Ltd. All rights reserved.

Hung C.-C.,Southern Polytechnic State University | Kulkarni S.,Southern Polytechnic State University | Kuo B.-C.,National Taichung University of Education
IEEE Journal on Selected Topics in Signal Processing | Year: 2011

Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of both FCM and FWCM models for high-dimensional multiclass pattern recognition problems. The methodology used in NW-FCM is the concept of weighted mean from the nonparametric weighted feature extraction (NWFE) and cluster mean from discriminant analysis feature extraction (DAFE). These two concepts are combined in NW-FCM for unsupervised clustering. The main features of NW-FCM, when compared to FCM, are the inclusion of the weighted mean to increase the accuracy, and, when compared to FWCM, the centroid of each cluster is included to increase the stability. The motivation of this work is to meliorate the well-known fuzzy C-Means algorithm (FCM) and a recently proposed fuzzy weighted C-Means algorithm (FWCM). Our finding is that the proposed algorithm gives greater classification accuracy and stability than that of FCM and FWCM. Experimental results on both synthetic and real data demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particularly for hyperspectral images. © 2010 IEEE.

Hsiao K.-L.,National Taichung University of Education
Library Hi Tech | Year: 2013

Purpose: The research goal of this study is to explore the factors influencing the adoption of Android smartphones and the intention to pay for mobile internet services. Design/methodology/approach: The present study proposes a framework based on theory of reasoned action (TRA) from the perspectives of software (interface convenience and perceived content), hardware (perceived infrastructure), design (design aesthetics) and perceived value (emotional value, price/value for money, performance/quality value, and social value). A web survey was conducted, and data were collected from a total of 881 users of Android smartphones in Taiwan. The casual model was validated using partial least squares (PLS) techniques. Findings: The results indicated that the influence of the factors on the intention of the mobile internet users and non-users were different. Surprisingly, the effect of design aesthetics was not significant in all of the groups. Male users were found to be more likely to read e-books on their smartphones, as are people with higher personal incomes. Practical implications: This study contributes to a theoretical understanding of the factors that promote mobile internet users' and non-users' intention to adopt Android smartphones and pay for mobile internet services. The proposed framework can be used by mobile internet service providers and smartphone manufacturers to design the products and marketing strategies. Originality/value: The primary value of this paper lies in providing a better understanding of users' and non-users' concerns about Android smartphone adoption and subscription of mobile internet services. © Emerald Group Publishing Limited.

Liaw S.-S.,China Medical University at Taichung | Huang H.-M.,National Taichung University of Education
Computers and Education | Year: 2013

The research purpose is to investigate learner self-regulation in e-learning environments. In order to better understand learner attitudes toward e-learning, 196 university students answer a questionnaire survey after use an e-learning system few months. The statistical results showed that perceived satisfaction, perceived usefulness, and interactive learning environments were all found to predict perceived self-regulation in e-learning environments. Further, perceived usefulness can be influenced by interactive learning environments, perceived self-efficacy, and perceived satisfaction. In addition, perceived satisfaction can be affected by interactive learning environments, perceived self-efficacy, and perceived anxiety. Finally, the study proposes a conceptual model to investigate learner self-regulation in e-learning environments. © 2012 Elsevier Ltd. All rights reserved.

Cheng I.S.,National Taichung University of Education
The British journal of nutrition | Year: 2012

Glycogen stored in skeletal muscle is the main fuel for endurance exercise. The present study examined the effects of oral hydroxycitrate (HCA) supplementation on post-meal glycogen synthesis in exercised human skeletal muscle. Eight healthy male volunteers (aged 22·0 (se 0·3) years) completed a 60-min cycling exercise at 70-75 % VO 2max and received HCA or placebo in a crossover design repeated after a 7 d washout period. They consumed 500 mg HCA or placebo with a high-carbohydrate meal (2 g carbohydrate/kg body weight, 80 % carbohydrate, 8 % fat, 12 % protein) for a 3-h post-exercise recovery. Muscle biopsy samples were obtained from vastus lateralis immediately and 3 h after the exercise. We found that HCA supplementation significantly lowered post-meal insulin response with similar glucose level compared to placebo. The rate of glycogen synthesis with the HCA meal was approximately onefold higher than that with the placebo meal. In contrast, GLUT4 protein level after HCA supplementation was significantly decreased below the placebo level, whereas expression of fatty acid translocase (FAT)/CD36 mRNA was significantly increased above the placebo level. Furthermore, HCA supplementation significantly increased energy reliance on fat oxidation, estimated by the gaseous exchange method. However, no differences were found in circulating NEFA and glycerol levels with the HCA meal compared with the placebo meal. The present study reports the first evidence that HCA supplementation enhanced glycogen synthesis rate in exercised human skeletal muscle and improved post-meal insulin sensitivity.

Li C.-Y.,National Taichung University of Education
Computers in Human Behavior | Year: 2013

Firms invest millions of dollars in the introduction of new information systems for long-term benefit. If employees are not willing to accept a new information system, such investments may be wasted. Employee acceptance of a new information system is in part determined by external influences. However, previous research has neglected the paths of persuasive strategies and external social influences on information system acceptance. Linkages between persuasive strategies and external social influences are also scarce. By integrating social influence theory and an elaboration likelihood model, this study explores the influence of persuasive messages (source credibility and argument quality) on social influence, affective response and cognitive response. This study also investigates the interrelationships among affective response, cognitive response and behavior intention. Furthermore, the moderating roles of social influences on the impact of affective response and cognitive response on behavior intention are identified. © 2012 Elsevier Ltd. All rights reserved.

Jia X.,University of New South Wales | Kuo B.-C.,National Taichung University of Education | Crawford M.M.,Purdue University
Proceedings of the IEEE | Year: 2013

Hyperspectral sensors record the reflectance from the Earth's surface over the full range of solar wavelengths with high spectral resolution. The resulting high-dimensional data contain rich information for a wide range of applications. However, for a specific application, not all the measurements are important and useful. The original feature space may not be the most effective space for representing the data. Feature mining, which includes feature generation, feature selection (FS), and feature extraction (FE), is a critical task for hyperspectral data classification. Significant research effort has focused on this issue since hyperspectral data became available in the late 1980s. The feature mining techniques which have been developed include supervised and unsupervised, parametric and nonparametric, linear and nonlinear methods, which all seek to identify the informative subspace. This paper provides an overview of both conventional and advanced feature reduction methods, with details on a few techniques that are commonly used for analysis of hyperspectral data. A general form that represents several linear and nonlinear FE methods is also presented. Experiments using two widely available hyperspectral data sets are included to illustrate selected FS and FE methods. © 1963-2012 IEEE.

Loading National Taichung University of Education collaborators
Loading National Taichung University of Education collaborators