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With the global wave and evolution of Cultural and Creative Industries, Law for the Development of the Cultural and Creative Industries was successfully promulgated in 2010 in Taiwan. It encourages relevant craft industries transforming to Cultural and Creative Industries and establishing private brands. In order to promote Cultural and Creative Industries, the government also reinforces the integration of art creation with culture preservation, culture, and technology. Consequently, the application of cultural and creative products is moving towards diversification. Particularly, digital creative APP (Application) becomes a popular entertainment and interaction platform for modern youngsters. In this case, understanding the cultural advantage and integrating creativity and design are the key factors in the reinforcement of Cultural and Creative Industries with cultural and creative products in Taiwan. Understanding the digital creative APP usage intention and attitudes therefore becomes a primary research issue. This study aims to develop a cultural and creative APP as well as the strict and empirically meaningful digital creative APP usage intention and attitudes scale for digital creative APP users. The characteristics of cultural and creative APP are first generalized the usefulness, hedonism, and media interaction through literature review, expert interview, purification and prediction of measurement items, and the reliability and validity evaluation of formal questionnaire. Churchill (1979) and DeVellis (1991) as well as the suggestions from research with relevant scales are referred to measure the digital creative APP usage intention and attitudes. The results show that the best predictor of practicability, entertainment and media interactive for intention toward cultural creative APP design is use attitude (R2= 0.547), followed by intend to use (R2 = 0.528), practicability (R2 = 0.408), entertainment (R2 = 0.186). This study proposes a general extended technology acceptance model for Intention toward Cultural Creative APP Design. Our proposed model explained 52.8% of the variance in behavioral intention. Overall findings suggest that use attitude and intend to use may augment its function as a key factor for procedural Intention toward cultural creative APP design.

As RS (recommendation system) presents the advantage of adaptive recommendation, it is gradually applied to e-learning systems to recommend learners for the next learning content. Problems exist in current learning recommender system available to student in that they are often of a general learning content not offering a personalized service. To overcome this, An adaptive learning path recommendation system (ALPRS) is proposed comprising: (a) Fuzzy Delphi Method (FDM) is applied to acquire the key factors in learning content; (b) Fuzzy Interpretive Structural Model (ISM) is further utilized for establishing the adaptive learning path hierarchical structure; and (c) repertory grid technology (RGT) is further used for acquiring the effects of the recommender element attributes of the adaptive learning path. The result show that the learning outcome with ALPRS is better than it with general learning course guided recommendation mechanism, and the scores of system satisfaction with ALPRS and personal service are higher than 90; recall (95%), precision (68%), F1 index (45%), and MAE (8%) in ALPRS outperform other approaches. Finally, three contributions are organized in this study. (1) The novel hybrid ALPRS is proposed and tested the practicability. (2) A prototype gamification geometry teaching material module is developed for the promotion in MSTE (Mathematics, Science, and Technology Education) areas. (3) The adaptive geometry learning path diagram generated with ISM based on learning styles could offer reference for successive studies.

This article looks at combining the Curvature Scale Space (CSS) shape matching algorithm and the Watershed Algorithm with the aim of creating a simple yet effective leaf identification software. The CSS shape matching algorithm solves the problems of: Leaf self-intersection - Parts of a leaf overlap. ; Leaf rotation - Images that are rotated. ; Leaf variation - No two leaves are the same. The Watershed Algorithm provides an effective way of segmenting a leaf from a non-ideal background, which provides a method of creating pristine binary images. The result is a leaf identification software that has good accuracy, the ability to deal with non-ideal backgrounds, and large room for improvement.