Purpose-The purpose of this paper is to conceptualise a framework that integrates information qua... more Purpose-The purpose of this paper is to conceptualise a framework that integrates information quality, system quality, function quality, and social influence based on the information system (IS) success model, to explore the relationship among these factors, which might be the key determinants of Facebook educational usage intention. Design/methodology/approach-An internet survey was conducted to collect empirical data from 221 Facebook users on their experiences of using Facebook. This study applied structural equation modeling (SEM) to demonstrate the proposed model's construct relations and multiple group analysis. Findings-The results of this study show that social influence and information quality are critical and direct determinants that affect users' continuous intention to use Facebook in learning; social influence also indirectly affect Facebook usage intention through the mediating effect of information quality. Except the path between social influence and usage intention in age subgroups, the relationships among these determining factors in the proposed model are stable, and there were no significant differences among gender subgroups and age subgroups examined using the multiple group comparison test. Originality/value-The findings provide a better understanding of the IS success model that influences the Facebook educational usage intention for researchers and practitioners.
ABSTRACT Determining the theoretical price for an option, or option pricing, is regarded as one o... more ABSTRACT Determining the theoretical price for an option, or option pricing, is regarded as one of the most important issues in financial research. In recent years, linear and non-linear GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) models were used to estimate volatility. However, the empirical analysis of various different volatility model estimations has not achieved consistent results. This study construct an Taiwan's existing tech index options price classification with various a values to determine the moneyness (at-the-money, in-the-money, out-the-money) of option price. This study tested 140 models, the combinations included 4 types of the kernel function in multi-SVM (Linear, Polynomial, RBF, Sigmoid), 7 types of volatility estimation (historical volatility, implied volatility, GARCH, IGARCH, GJR-CARCH, EGARCH, TBGARCH) and 5 types of α (2%, 4%,5%,6%,8%). Finally, the classification result shows that using α=2%, polynomial function multi-SVM with the three types of volatility estimation methods of TBGARCH, EGARCH and GJR-GARCH would yield better classification performance.
This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter val... more This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model outperforms the previous models. Specifically, the new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting.
International Journal of Mobile Learning and Organisation, 2014
In this study, a human affective norm (emotion and attention) recognition system for U-learning s... more In this study, a human affective norm (emotion and attention) recognition system for U-learning systems is developed. Fifth graders in an elementary school were recruited as participants firstly to see some emotional pictures from the International Affective Picture System (IAPS), and to do the attention test to obtain the affective information-electroencephalography (EEG) and electrocardiogram (ECG) for developing the affective norm recognition system of the study. These bio-physiology signals extract important features by using four types of linear Principal Component Analysis (PCA) to serve as the input variables for Support Vector Machine (SVM) model. The results of feature selection showed that factor analysis with covariance extraction method has higher accumulative variances than correlation extraction method. This study suggested that future researchers may try to adopt more non-linear feature selection methods in order to develop a high accuracy SVM-based emotion recognition system.
Few studies have focused on investigating the time interval effects on support vector machine (SV... more Few studies have focused on investigating the time interval effects on support vector machine (SVM) model in emotion recognition. This study tested original averaged value models and difference value models with three different time-intervals (5 seconds, 10 seconds, and 30 seconds) for SVM emotion recognition in our developed emotion norm database. Forty one elementary school students were recruited as participants to see some emotion pictures in international affective picture system (IAPS), and to collect their affective information—attention, meditation, electroencephalography (EEG), electrocardiogram (ECG), and SpO2 for developing the affective norm recognition system. This study selected 5480 IPAS photos physiological data as the tested dataset from our emotion norm database. The bio-physiology signals were averaged by seconds or difference the value by second and then to serve as the input variables value for C-SVM with RBF kernel function. The results showed that the original averaged models have better performance than the difference models. In addition, the original averaged value model with 30 seconds time interval is the optimal classification model. This study suggested that future research can adopt 30 seconds as the time interval for determining the size of time interval for their training dataset in emotion recognition problem.
Few studies have focused on integrating affective computing and soft computing technique for huma... more Few studies have focused on integrating affective computing and soft computing technique for human physiology signals recognition in digital content learning system. This study develops a human affective norm (emotion and attention) recognition system for U-learning system. Eight elementary school students (4 male and 4 female) were recruited as participants to see some emotion pictures in international affective picture system (IAPS), and to collect their affective information—attention, meditation, electroencephalography (EEG), electrocardiogram (ECG), and SpO2 for developing the affective norm recognition system. These bio-physiology signals were extracted important features selection (Factor analysis) to serve as the input variables for radial basis function (RBF) neural network model. The results showed that all types of factor analysis did not perform well in our emotion norm database. Factor analysis with covariance extraction has higher accumulative variances than correlation extraction. This study suggested that future research can adopt more nonlinear feature selection methods to develop a high accuracy support vector machine based emotion recognition system.
Journal of Hospitality and Tourism Technology, Jul 3, 2023
Purpose This study aims to explore the augmented reality (AR) effectiveness of museum visiting. D... more Purpose This study aims to explore the augmented reality (AR) effectiveness of museum visiting. Design/methodology/approach Based on the AR marketing of the motivation model and the inspiration model, the critical mental process and AR were revealed that could increase museum visits. The mixed-methods approach was adopted to analyze the qualitative research and test the model hypotheses to understand the perceptions of the increasing AR marketing of museum visits. Findings The authors found that inspiration and perceived quality AR are critical to enhancing the attitudinal of museum visits. AR developers can thus focus on utilitarian and hedonic benefits in perceptual augmentation quality to inspire users’ willingness to visit museums. Originality/value This study extended the inspiration model through a mixed-methods approach to investigate how AR technologies encourage and leverage visitor experience to increase museum visit intention psychologically.
Non-fungible token (NFT) products are important for industrial applications. In recent years, the... more Non-fungible token (NFT) products are important for industrial applications. In recent years, they have rapidly gained importance in the field of blockchain combined with metaverse. The concept of NFTs has developed gradually, as many industries have begun using NFTs creatively to raise new business innovation opportunities in entrepreneurship. However, few studies have been conducted analyzing critical features of NFTs for success, trends, and challenges in NFT products. In this study, group discussions, case analysis methods, and the OpenSea database were used to analyze fashion trends among NFT products. A mixed method was used in this study. Quantitative and qualitative data derived from the questionnaire and group discussions were analyzed using the case study method, and the actual historical trading data of NFT products obtained from the OpenSea platform were analyzed. This study analyzed NFT products, fashion characteristics, and trends in NFT artwork. The opportunities and ...
Background This study proposes a learning cycle and a comprehensive research framework that integ... more Background This study proposes a learning cycle and a comprehensive research framework that integrates Bloom’s taxonomy: the cognitive domain (cognitive load), affective domain (attitude and motivation) and psychomotor domain (implementation of science, technology, engineering, arts, and math [STEAM] activities) to explore the relationship between these learning domains and learning intention. The proposed innovative mediated-moderation model includes second-order factors derived from the technology acceptance model (TAM) (perceived usefulness, perceived ease of use, and perceived enjoyment), the attention, relevance, confidence and satisfaction (ARCS) model, and cognitive load (mental load and mental effort) to explain the continuous learning intention of STEAM education. Results A teaching material was designed for the STEAM activity, and an empirical experiment was subsequently conducted. The empirical experiment of STEAM activities with our design teaching material (micro:bit wi...
Background: This study proposes a learning cycle and a comprehensive research framework that inte... more Background: This study proposes a learning cycle and a comprehensive research framework that integrates Bloom's taxonomy: the cognitive domain (cognitive load), affective domain (attitude and motivation) and psychomotor domain (implementation of science, technology, engineering, arts, and math [STEAM] activities) to explore the relationship between these learning domains and learning intention. The proposed innovative mediated-moderation model includes second-order factors derived from the technology acceptance model (TAM) (perceived usefulness, perceived ease of use, and perceived enjoyment), the attention, relevance, confidence and satisfaction (ARCS) model, and cognitive load (mental load and mental effort) to explain the continuous learning intention of STEAM education. Results: A teaching material was designed for the STEAM activity, and an empirical experiment was subsequently conducted. The empirical experiment of STEAM activities with our design teaching material (micro:bit with artificial intelligence-based concept) was conducted at a university and an elementary school; a total of 145 questionnaire survey data were collected after the activities. University student participants were 20-24 years old and the elementary school student participants were at the K5-K6 level. The results showed that perceived usability directly influenced learning intention and strengthened the relationship between learning attitudes and intention. The ARCS plays a critical moderating role that positively influenced perceived usability and strengthened its effects on learning attitudes. Regarding the mediating effects, cognitive load negatively influenced perceived usability. Conclusions: The findings of this study revealed that critical factors affect students' learning attitudes and intentions regarding STEAM education. The theoretical and educational implications of these findings were proposed to future instructors. Highlights • The STEAM learning cycle was proposed to describe four phases for successful STEAM learning. • Affect factors of learning intention in STEAM education were investigated. • Motivation factors positively influences perceived usability and learning attitude. • Cognitive load has a significant negative effects influences perceived usability. • Perceived usability directly influences learning intention and strengthens the relationship between attitudes and intention.
Background: Synthetic biology is foreseen to have important applications in biotechnology and med... more Background: Synthetic biology is foreseen to have important applications in biotechnology and medicine, and is expected to contribute significantly to a better understanding of the functioning of complex biological systems. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to intrinsic parameter uncertainties, external disturbances and functional variations of intra-and extra-cellular environments. The design method for a robust synthetic gene network that works properly in a host cell under these intrinsic parameter uncertainties and external disturbances is the most important topic in synthetic biology. Results: In this study, we propose a stochastic model that includes parameter fluctuations and external disturbances to mimic the dynamic behaviors of a synthetic gene network in the host cell. Then, based on this stochastic model, four design specifications are introduced to guarantee that a synthetic gene network can achieve its desired steady state behavior in spite of parameter fluctuations, external disturbances and functional variations in the host cell. We propose a systematic method to select a set of appropriate design parameters for a synthetic gene network that will satisfy these design specifications so that the intrinsic parameter fluctuations can be tolerated, the external disturbances can be efficiently filtered, and most importantly, the desired steady states can be achieved. Thus the synthetic gene network can work properly in a host cell under intrinsic parameter uncertainties, external disturbances and functional variations. Finally, a design procedure for the robust synthetic gene network is developed and a design example is given in silico to confirm the performance of the proposed method. Conclusion: Based on four design specifications, a systematic design procedure is developed for designers to engineer a robust synthetic biology network that can achieve its desired steady state behavior under parameter fluctuations, external disturbances and functional variations in the host cell. Therefore, the proposed systematic design method has good potential for the robust synthetic gene network design.
The newly developed computer vision pose estimation technique in artificial intelligence (AI) is ... more The newly developed computer vision pose estimation technique in artificial intelligence (AI) is an emerging technology with potential advantages, such as high efficiency and contactless detection, for improving competitive advantage in the sports industry. The related literature is currently lacking an integrated and comprehensive discussion about the applications and limitations of using the pose estimation technique. The purpose of this study was to apply AI pose estimation techniques, and to discuss the concepts, possible applications, and limitations of these techniques in table tennis. This study implemented the OpenPose pose algorithm in a real-world video of a table tennis game. The research results show that the pose estimation algorithm performs well in estimating table tennis players’ poses from the video in a graphics processing unit (GPU)-accelerated environment. This study proposes an innovative two-stage AI pose estimation method for effectively addressing the current...
Purpose – The purpose of this paper is to conceptualise a framework that integrates information q... more Purpose – The purpose of this paper is to conceptualise a framework that integrates information quality, system quality, function quality, and social influence based on the information system (IS) success model, to explore the relationship among these factors, which might be the key determinants of Facebook educational usage intention. Design/methodology/approach – An internet survey was conducted to collect empirical data from 221 Facebook users on their experiences of using Facebook. This study applied structural equation modeling (SEM) to demonstrate the proposed model’s construct relations and multiple group analysis. Findings – The results of this study show that social influence and information quality are critical and direct determinants that affect users’ continuous intention to use Facebook in learning; social influence also indirectly affect Facebook usage intention through the mediating effect of information quality. Except the path between social influence and usage inte...
Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that... more Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that a large amount of direct and indirect profits can be obtained from effective and efficient SCM practices. Supplier selection has great impact on integration of the supply chain relationship. Effective and accurate supplier selection decisions are significant components for productions and logistics management in many firms to enhance their organizational performance. This study pioneers in using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method to find influential factors in selecting SCM suppliers. The DEMATEL method evaluates supplier performance to find key factor criteria to improve performance and provides a novel approach of decision-making information in SCM supplier selection. This research designs a fuzzy DEMATEL questionnaire sent to seventeen professional purchasing personnel in the electronic industry. Our research results find that stable delivery of goods is the most influence and the strongest connection to other criteria.
With the emergence of non-fungible tokens (NFTs) in blockchain technology, educational institutio... more With the emergence of non-fungible tokens (NFTs) in blockchain technology, educational institutions have been able to use NFTs to reward students. This is done by automatically processing transaction information and the buying and selling process using smart contract technology. The technology enables the establishment of recognition levels and incentivizes students to receive NFT recognition rewards. According to the Taxonomy Learning Pyramid, learning through hands-on experiences plays a crucial role in attracting students’ interest. In this study, we analyzed the potential for using NFTs in education and the current applications of NFTs in society. We conducted a case study and performed a preliminary investigation of the types of NFT applications in the education industry. We then analyzed different education industries using individual analysis combined with SWOT analysis to understand the impact, value, and challenges of NFT applications. The results revealed 10 educational ap...
Affective computing (AC) is an emerging research direction to deal with the great challenge of cr... more Affective computing (AC) is an emerging research direction to deal with the great challenge of creating emotional intelligence for a machine. Affective computing is a cross-disciplinary research knowledge that integrates recognition, interpretation, and simulation of human emotion into a system. This article describes the design of multimodal perception of affective computing system. Our multimodal physiological channels include facial expression recognition, heart rate monitoring, blood oxygen level (SpO2), skin conductance response (SCR), and electroencephalogram (EEG) signals for building our affective computing system. To solve the various data sampling problem, we developed a concurrent control integration mechanism to automatically average all the sensors' data into the same data sampling rate (one record per second) and rearrange all the data into the same time. We believed the proposed system design is benefit for helping researchers in collecting and integrating experiment data in affective computing area.
International Journal of e-Education, e-Business, e-Management and e-Learning, 2019
The purposes of this study aims to integrate STEAM (Science, Technology, Engineering, Art, and Ma... more The purposes of this study aims to integrate STEAM (Science, Technology, Engineering, Art, and Mathematics) Maker education with digital game-based learning (DGBL) method for developing a STEAM game via 6e teaching model. This study develops a STEAM teaching plan and material that includes a STEAM lecture and a STEAM education game by using APP Inventor2 program. The STEAM teaching materials in APP Inventor2 will be tested and evaluated to measure their reliability and validity. Further, in this study, we will teach students through our designed STEAM lectures and then helped them to finish their creative STEAM education game in APP Inventor 2. The learning performance of STEAM education will be evaluated through our designed questionnaire. Finally, this study proposes several key suggestions for STEAM education researchers.
New Trends in Intelligent Information and Database Systems, 2015
Few studies have focused on investigating the time interval effects on support vector machine (SV... more Few studies have focused on investigating the time interval effects on support vector machine (SVM) model in emotion recognition. This study tested original averaged value models and difference value models with three different time-intervals (5 seconds, 10 seconds, and 30 seconds) for SVM emotion recognition in our developed emotion norm database. Forty one elementary school students were recruited as participants to see some emotion pictures in international affective picture system (IAPS), and to collect their affective information—attention, meditation, electroencephalography (EEG), electrocardiogram (ECG), and SpO2 for developing the affective norm recognition system. This study selected 5480 IPAS photos physiological data as the tested dataset from our emotion norm database. The bio-physiology signals were averaged by seconds or difference the value by second and then to serve as the input variables value for C-SVM with RBF kernel function. The results showed that the original averaged models have better performance than the difference models. In addition, the original averaged value model with 30 seconds time interval is the optimal classification model. This study suggested that future research can adopt 30 seconds as the time interval for determining the size of time interval for their training dataset in emotion recognition problem.
This paper examined bankruptcy predictive accuracy of five statistics models-discriminant analysi... more This paper examined bankruptcy predictive accuracy of five statistics models-discriminant analysis logistic regression, probit regression, neural networks, support vector machine (SVM), and genetic-based SVM (GA-SVM) that influenced by variable selection. Empirical results indicate that the SVM-based models are very promising models for predicting financial failure, in terms of both best predictive accuracy and generalization ability. In addition, variable selection had the lowest influence of predictive accuracy in the GA-SVM model with optimal values of parameters.
Purpose-The purpose of this paper is to conceptualise a framework that integrates information qua... more Purpose-The purpose of this paper is to conceptualise a framework that integrates information quality, system quality, function quality, and social influence based on the information system (IS) success model, to explore the relationship among these factors, which might be the key determinants of Facebook educational usage intention. Design/methodology/approach-An internet survey was conducted to collect empirical data from 221 Facebook users on their experiences of using Facebook. This study applied structural equation modeling (SEM) to demonstrate the proposed model's construct relations and multiple group analysis. Findings-The results of this study show that social influence and information quality are critical and direct determinants that affect users' continuous intention to use Facebook in learning; social influence also indirectly affect Facebook usage intention through the mediating effect of information quality. Except the path between social influence and usage intention in age subgroups, the relationships among these determining factors in the proposed model are stable, and there were no significant differences among gender subgroups and age subgroups examined using the multiple group comparison test. Originality/value-The findings provide a better understanding of the IS success model that influences the Facebook educational usage intention for researchers and practitioners.
ABSTRACT Determining the theoretical price for an option, or option pricing, is regarded as one o... more ABSTRACT Determining the theoretical price for an option, or option pricing, is regarded as one of the most important issues in financial research. In recent years, linear and non-linear GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) models were used to estimate volatility. However, the empirical analysis of various different volatility model estimations has not achieved consistent results. This study construct an Taiwan's existing tech index options price classification with various a values to determine the moneyness (at-the-money, in-the-money, out-the-money) of option price. This study tested 140 models, the combinations included 4 types of the kernel function in multi-SVM (Linear, Polynomial, RBF, Sigmoid), 7 types of volatility estimation (historical volatility, implied volatility, GARCH, IGARCH, GJR-CARCH, EGARCH, TBGARCH) and 5 types of α (2%, 4%,5%,6%,8%). Finally, the classification result shows that using α=2%, polynomial function multi-SVM with the three types of volatility estimation methods of TBGARCH, EGARCH and GJR-GARCH would yield better classification performance.
This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter val... more This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model outperforms the previous models. Specifically, the new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting.
International Journal of Mobile Learning and Organisation, 2014
In this study, a human affective norm (emotion and attention) recognition system for U-learning s... more In this study, a human affective norm (emotion and attention) recognition system for U-learning systems is developed. Fifth graders in an elementary school were recruited as participants firstly to see some emotional pictures from the International Affective Picture System (IAPS), and to do the attention test to obtain the affective information-electroencephalography (EEG) and electrocardiogram (ECG) for developing the affective norm recognition system of the study. These bio-physiology signals extract important features by using four types of linear Principal Component Analysis (PCA) to serve as the input variables for Support Vector Machine (SVM) model. The results of feature selection showed that factor analysis with covariance extraction method has higher accumulative variances than correlation extraction method. This study suggested that future researchers may try to adopt more non-linear feature selection methods in order to develop a high accuracy SVM-based emotion recognition system.
Few studies have focused on investigating the time interval effects on support vector machine (SV... more Few studies have focused on investigating the time interval effects on support vector machine (SVM) model in emotion recognition. This study tested original averaged value models and difference value models with three different time-intervals (5 seconds, 10 seconds, and 30 seconds) for SVM emotion recognition in our developed emotion norm database. Forty one elementary school students were recruited as participants to see some emotion pictures in international affective picture system (IAPS), and to collect their affective information—attention, meditation, electroencephalography (EEG), electrocardiogram (ECG), and SpO2 for developing the affective norm recognition system. This study selected 5480 IPAS photos physiological data as the tested dataset from our emotion norm database. The bio-physiology signals were averaged by seconds or difference the value by second and then to serve as the input variables value for C-SVM with RBF kernel function. The results showed that the original averaged models have better performance than the difference models. In addition, the original averaged value model with 30 seconds time interval is the optimal classification model. This study suggested that future research can adopt 30 seconds as the time interval for determining the size of time interval for their training dataset in emotion recognition problem.
Few studies have focused on integrating affective computing and soft computing technique for huma... more Few studies have focused on integrating affective computing and soft computing technique for human physiology signals recognition in digital content learning system. This study develops a human affective norm (emotion and attention) recognition system for U-learning system. Eight elementary school students (4 male and 4 female) were recruited as participants to see some emotion pictures in international affective picture system (IAPS), and to collect their affective information—attention, meditation, electroencephalography (EEG), electrocardiogram (ECG), and SpO2 for developing the affective norm recognition system. These bio-physiology signals were extracted important features selection (Factor analysis) to serve as the input variables for radial basis function (RBF) neural network model. The results showed that all types of factor analysis did not perform well in our emotion norm database. Factor analysis with covariance extraction has higher accumulative variances than correlation extraction. This study suggested that future research can adopt more nonlinear feature selection methods to develop a high accuracy support vector machine based emotion recognition system.
Journal of Hospitality and Tourism Technology, Jul 3, 2023
Purpose This study aims to explore the augmented reality (AR) effectiveness of museum visiting. D... more Purpose This study aims to explore the augmented reality (AR) effectiveness of museum visiting. Design/methodology/approach Based on the AR marketing of the motivation model and the inspiration model, the critical mental process and AR were revealed that could increase museum visits. The mixed-methods approach was adopted to analyze the qualitative research and test the model hypotheses to understand the perceptions of the increasing AR marketing of museum visits. Findings The authors found that inspiration and perceived quality AR are critical to enhancing the attitudinal of museum visits. AR developers can thus focus on utilitarian and hedonic benefits in perceptual augmentation quality to inspire users’ willingness to visit museums. Originality/value This study extended the inspiration model through a mixed-methods approach to investigate how AR technologies encourage and leverage visitor experience to increase museum visit intention psychologically.
Non-fungible token (NFT) products are important for industrial applications. In recent years, the... more Non-fungible token (NFT) products are important for industrial applications. In recent years, they have rapidly gained importance in the field of blockchain combined with metaverse. The concept of NFTs has developed gradually, as many industries have begun using NFTs creatively to raise new business innovation opportunities in entrepreneurship. However, few studies have been conducted analyzing critical features of NFTs for success, trends, and challenges in NFT products. In this study, group discussions, case analysis methods, and the OpenSea database were used to analyze fashion trends among NFT products. A mixed method was used in this study. Quantitative and qualitative data derived from the questionnaire and group discussions were analyzed using the case study method, and the actual historical trading data of NFT products obtained from the OpenSea platform were analyzed. This study analyzed NFT products, fashion characteristics, and trends in NFT artwork. The opportunities and ...
Background This study proposes a learning cycle and a comprehensive research framework that integ... more Background This study proposes a learning cycle and a comprehensive research framework that integrates Bloom’s taxonomy: the cognitive domain (cognitive load), affective domain (attitude and motivation) and psychomotor domain (implementation of science, technology, engineering, arts, and math [STEAM] activities) to explore the relationship between these learning domains and learning intention. The proposed innovative mediated-moderation model includes second-order factors derived from the technology acceptance model (TAM) (perceived usefulness, perceived ease of use, and perceived enjoyment), the attention, relevance, confidence and satisfaction (ARCS) model, and cognitive load (mental load and mental effort) to explain the continuous learning intention of STEAM education. Results A teaching material was designed for the STEAM activity, and an empirical experiment was subsequently conducted. The empirical experiment of STEAM activities with our design teaching material (micro:bit wi...
Background: This study proposes a learning cycle and a comprehensive research framework that inte... more Background: This study proposes a learning cycle and a comprehensive research framework that integrates Bloom's taxonomy: the cognitive domain (cognitive load), affective domain (attitude and motivation) and psychomotor domain (implementation of science, technology, engineering, arts, and math [STEAM] activities) to explore the relationship between these learning domains and learning intention. The proposed innovative mediated-moderation model includes second-order factors derived from the technology acceptance model (TAM) (perceived usefulness, perceived ease of use, and perceived enjoyment), the attention, relevance, confidence and satisfaction (ARCS) model, and cognitive load (mental load and mental effort) to explain the continuous learning intention of STEAM education. Results: A teaching material was designed for the STEAM activity, and an empirical experiment was subsequently conducted. The empirical experiment of STEAM activities with our design teaching material (micro:bit with artificial intelligence-based concept) was conducted at a university and an elementary school; a total of 145 questionnaire survey data were collected after the activities. University student participants were 20-24 years old and the elementary school student participants were at the K5-K6 level. The results showed that perceived usability directly influenced learning intention and strengthened the relationship between learning attitudes and intention. The ARCS plays a critical moderating role that positively influenced perceived usability and strengthened its effects on learning attitudes. Regarding the mediating effects, cognitive load negatively influenced perceived usability. Conclusions: The findings of this study revealed that critical factors affect students' learning attitudes and intentions regarding STEAM education. The theoretical and educational implications of these findings were proposed to future instructors. Highlights • The STEAM learning cycle was proposed to describe four phases for successful STEAM learning. • Affect factors of learning intention in STEAM education were investigated. • Motivation factors positively influences perceived usability and learning attitude. • Cognitive load has a significant negative effects influences perceived usability. • Perceived usability directly influences learning intention and strengthens the relationship between attitudes and intention.
Background: Synthetic biology is foreseen to have important applications in biotechnology and med... more Background: Synthetic biology is foreseen to have important applications in biotechnology and medicine, and is expected to contribute significantly to a better understanding of the functioning of complex biological systems. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to intrinsic parameter uncertainties, external disturbances and functional variations of intra-and extra-cellular environments. The design method for a robust synthetic gene network that works properly in a host cell under these intrinsic parameter uncertainties and external disturbances is the most important topic in synthetic biology. Results: In this study, we propose a stochastic model that includes parameter fluctuations and external disturbances to mimic the dynamic behaviors of a synthetic gene network in the host cell. Then, based on this stochastic model, four design specifications are introduced to guarantee that a synthetic gene network can achieve its desired steady state behavior in spite of parameter fluctuations, external disturbances and functional variations in the host cell. We propose a systematic method to select a set of appropriate design parameters for a synthetic gene network that will satisfy these design specifications so that the intrinsic parameter fluctuations can be tolerated, the external disturbances can be efficiently filtered, and most importantly, the desired steady states can be achieved. Thus the synthetic gene network can work properly in a host cell under intrinsic parameter uncertainties, external disturbances and functional variations. Finally, a design procedure for the robust synthetic gene network is developed and a design example is given in silico to confirm the performance of the proposed method. Conclusion: Based on four design specifications, a systematic design procedure is developed for designers to engineer a robust synthetic biology network that can achieve its desired steady state behavior under parameter fluctuations, external disturbances and functional variations in the host cell. Therefore, the proposed systematic design method has good potential for the robust synthetic gene network design.
The newly developed computer vision pose estimation technique in artificial intelligence (AI) is ... more The newly developed computer vision pose estimation technique in artificial intelligence (AI) is an emerging technology with potential advantages, such as high efficiency and contactless detection, for improving competitive advantage in the sports industry. The related literature is currently lacking an integrated and comprehensive discussion about the applications and limitations of using the pose estimation technique. The purpose of this study was to apply AI pose estimation techniques, and to discuss the concepts, possible applications, and limitations of these techniques in table tennis. This study implemented the OpenPose pose algorithm in a real-world video of a table tennis game. The research results show that the pose estimation algorithm performs well in estimating table tennis players’ poses from the video in a graphics processing unit (GPU)-accelerated environment. This study proposes an innovative two-stage AI pose estimation method for effectively addressing the current...
Purpose – The purpose of this paper is to conceptualise a framework that integrates information q... more Purpose – The purpose of this paper is to conceptualise a framework that integrates information quality, system quality, function quality, and social influence based on the information system (IS) success model, to explore the relationship among these factors, which might be the key determinants of Facebook educational usage intention. Design/methodology/approach – An internet survey was conducted to collect empirical data from 221 Facebook users on their experiences of using Facebook. This study applied structural equation modeling (SEM) to demonstrate the proposed model’s construct relations and multiple group analysis. Findings – The results of this study show that social influence and information quality are critical and direct determinants that affect users’ continuous intention to use Facebook in learning; social influence also indirectly affect Facebook usage intention through the mediating effect of information quality. Except the path between social influence and usage inte...
Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that... more Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that a large amount of direct and indirect profits can be obtained from effective and efficient SCM practices. Supplier selection has great impact on integration of the supply chain relationship. Effective and accurate supplier selection decisions are significant components for productions and logistics management in many firms to enhance their organizational performance. This study pioneers in using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method to find influential factors in selecting SCM suppliers. The DEMATEL method evaluates supplier performance to find key factor criteria to improve performance and provides a novel approach of decision-making information in SCM supplier selection. This research designs a fuzzy DEMATEL questionnaire sent to seventeen professional purchasing personnel in the electronic industry. Our research results find that stable delivery of goods is the most influence and the strongest connection to other criteria.
With the emergence of non-fungible tokens (NFTs) in blockchain technology, educational institutio... more With the emergence of non-fungible tokens (NFTs) in blockchain technology, educational institutions have been able to use NFTs to reward students. This is done by automatically processing transaction information and the buying and selling process using smart contract technology. The technology enables the establishment of recognition levels and incentivizes students to receive NFT recognition rewards. According to the Taxonomy Learning Pyramid, learning through hands-on experiences plays a crucial role in attracting students’ interest. In this study, we analyzed the potential for using NFTs in education and the current applications of NFTs in society. We conducted a case study and performed a preliminary investigation of the types of NFT applications in the education industry. We then analyzed different education industries using individual analysis combined with SWOT analysis to understand the impact, value, and challenges of NFT applications. The results revealed 10 educational ap...
Affective computing (AC) is an emerging research direction to deal with the great challenge of cr... more Affective computing (AC) is an emerging research direction to deal with the great challenge of creating emotional intelligence for a machine. Affective computing is a cross-disciplinary research knowledge that integrates recognition, interpretation, and simulation of human emotion into a system. This article describes the design of multimodal perception of affective computing system. Our multimodal physiological channels include facial expression recognition, heart rate monitoring, blood oxygen level (SpO2), skin conductance response (SCR), and electroencephalogram (EEG) signals for building our affective computing system. To solve the various data sampling problem, we developed a concurrent control integration mechanism to automatically average all the sensors' data into the same data sampling rate (one record per second) and rearrange all the data into the same time. We believed the proposed system design is benefit for helping researchers in collecting and integrating experiment data in affective computing area.
International Journal of e-Education, e-Business, e-Management and e-Learning, 2019
The purposes of this study aims to integrate STEAM (Science, Technology, Engineering, Art, and Ma... more The purposes of this study aims to integrate STEAM (Science, Technology, Engineering, Art, and Mathematics) Maker education with digital game-based learning (DGBL) method for developing a STEAM game via 6e teaching model. This study develops a STEAM teaching plan and material that includes a STEAM lecture and a STEAM education game by using APP Inventor2 program. The STEAM teaching materials in APP Inventor2 will be tested and evaluated to measure their reliability and validity. Further, in this study, we will teach students through our designed STEAM lectures and then helped them to finish their creative STEAM education game in APP Inventor 2. The learning performance of STEAM education will be evaluated through our designed questionnaire. Finally, this study proposes several key suggestions for STEAM education researchers.
New Trends in Intelligent Information and Database Systems, 2015
Few studies have focused on investigating the time interval effects on support vector machine (SV... more Few studies have focused on investigating the time interval effects on support vector machine (SVM) model in emotion recognition. This study tested original averaged value models and difference value models with three different time-intervals (5 seconds, 10 seconds, and 30 seconds) for SVM emotion recognition in our developed emotion norm database. Forty one elementary school students were recruited as participants to see some emotion pictures in international affective picture system (IAPS), and to collect their affective information—attention, meditation, electroencephalography (EEG), electrocardiogram (ECG), and SpO2 for developing the affective norm recognition system. This study selected 5480 IPAS photos physiological data as the tested dataset from our emotion norm database. The bio-physiology signals were averaged by seconds or difference the value by second and then to serve as the input variables value for C-SVM with RBF kernel function. The results showed that the original averaged models have better performance than the difference models. In addition, the original averaged value model with 30 seconds time interval is the optimal classification model. This study suggested that future research can adopt 30 seconds as the time interval for determining the size of time interval for their training dataset in emotion recognition problem.
This paper examined bankruptcy predictive accuracy of five statistics models-discriminant analysi... more This paper examined bankruptcy predictive accuracy of five statistics models-discriminant analysis logistic regression, probit regression, neural networks, support vector machine (SVM), and genetic-based SVM (GA-SVM) that influenced by variable selection. Empirical results indicate that the SVM-based models are very promising models for predicting financial failure, in terms of both best predictive accuracy and generalization ability. In addition, variable selection had the lowest influence of predictive accuracy in the GA-SVM model with optimal values of parameters.
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Papers by Chih-Hung Wu