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Yueh-Min Huang is serving as a vice president of Kun Shan University, Taiwan, where he is also a Chair Professor in Department of Engineering Science, National Cheng-Kung University. His research interests include e-Learning, multimedia communications, wireless networks, and artificial intelligence. He received his MS and Ph.D. degrees in Electrical Engineering from the University of Arizona in 1988 and 1991, respectively. He has co-authored three books and has published more than 250 refereed journal research papers. Dr. Huang has received many research awards, such as the Best Paper Award of 2007, IEA/AIE Conference, Best Paper Award of the Computer Society of the Republic of China in 2003, the Awards of Acer Long-Term Prize in 1996, 1998, and 1999, Excellent Research Awards of National Microcomputer and Communication Contests in 2006, and two national outstanding research awards in 2011 and 2014, given to Taiwan’s top 100 scholars. He has supervised over 60 Ph.D. and 250 MS thesis students. He received many funded research grants from National Science Council, Ministry of Education, Industrial Technology of Research Institute, and Institute of Information Industry. Dr. Huang has been invited to give talks or served frequently in the program committee at national and international conferences.
To echo the United Nations formulated Sustainable Development Goals (SDGs), SDG 4 is to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Furthermore, high-quality education is the base on which human lives can be improved and sustainable development can be accomplished. Therefore, the affective emotional tutoring system established in this study enables learning via mobile devices, which are indispensable in daily life. The real-time interactive agent in the system guides learners to turn negative emotions into positive ones. We explored the usability of and user satisfaction with the affective emotional tutoring system. Sixty-two students participated in the study which used a quantitative research design to explore a learning situation. The overall usability of the system was evaluated with the System Usability Scale (SUS), and the Questionnaire for User Interaction Satisfaction (QUIS) was used to evaluate user satisfaction with the different elements of the system. The results showed that both the usability of and satisfaction with the affective emotional tutoring system were high. The emotional feedback mechanism of the system can help learners turn negative emotions into positive ones.
Tao-Hua Wang; Hao-Chiang Lin; Hong-Ren Chen; Yueh-Min Huang; Wei-Ting Yeh; Cheng-Tsung Li. Usability of an Affective Emotional Learning Tutoring System for Mobile Devices. Sustainability 2021, 13, 7890 .
AMA StyleTao-Hua Wang, Hao-Chiang Lin, Hong-Ren Chen, Yueh-Min Huang, Wei-Ting Yeh, Cheng-Tsung Li. Usability of an Affective Emotional Learning Tutoring System for Mobile Devices. Sustainability. 2021; 13 (14):7890.
Chicago/Turabian StyleTao-Hua Wang; Hao-Chiang Lin; Hong-Ren Chen; Yueh-Min Huang; Wei-Ting Yeh; Cheng-Tsung Li. 2021. "Usability of an Affective Emotional Learning Tutoring System for Mobile Devices." Sustainability 13, no. 14: 7890.
The vigorous development of the Industrial Internet of Things brings the advanced connection function of the new generation of industrial automation and control systems. The Supervisory Control and Data Acquisition (SCADA) network is converted into an open and highly interconnected network, where the equipment connections between industrial electronic devices are integrated with a SCADA system through a Modbus protocol. As SCADA and Modbus are easily used for control and monitoring, the interconnection and operational efficiency between systems are highly improved; however, such connectivity inevitably exposes the system to the open network environment. There are many network security threats and vulnerabilities in a SCADA network system. Especially in the era of the Industrial Internet of Things, any security vulnerability of an industrial system may cause serious property losses. Therefore, this paper proposes an encryption and verification mechanism based on the trusted token authentication service and Transport Layer Security (TLS) protocol to prevent attackers from physical attacks. Experimentally, this paper deployed and verified the system in an actual field of energy management system. According to the experimental results, the security defense architecture proposed in this paper can effectively improve security and is compatible with the actual field system.
Yu-Sheng Yang; Shih-Hsiung Lee; Wei-Che Chen; Chu-Sing Yang; Yuen-Min Huang; Ting-Wei Hou. TTAS: Trusted Token Authentication Service of Securing SCADA Network in Energy Management System for Industrial Internet of Things. Sensors 2021, 21, 2685 .
AMA StyleYu-Sheng Yang, Shih-Hsiung Lee, Wei-Che Chen, Chu-Sing Yang, Yuen-Min Huang, Ting-Wei Hou. TTAS: Trusted Token Authentication Service of Securing SCADA Network in Energy Management System for Industrial Internet of Things. Sensors. 2021; 21 (8):2685.
Chicago/Turabian StyleYu-Sheng Yang; Shih-Hsiung Lee; Wei-Che Chen; Chu-Sing Yang; Yuen-Min Huang; Ting-Wei Hou. 2021. "TTAS: Trusted Token Authentication Service of Securing SCADA Network in Energy Management System for Industrial Internet of Things." Sensors 21, no. 8: 2685.
The widespread COVID-19 pandemic has not only posed a major health threat in Taiwan but also has challenged the nursing pedagogy. Both academia and the education industry are calling for a radical change of nursing pedagogy. Under such a call, the present study investigates an online collaborative knowledge co-construction mechanism—Crowdsourcing Collaborative Learning Strategy (CCLS)—to help student nurses acquire and practice functional knowledge on clinical operations targeted to the Objective Structured Clinical Examination (OSCE) at anytime and anywhere via the internet service. A t-test on the pre-and-post test between the control and experimental group explained the effectiveness of the CCLS online platform. Two questionnaires were used to explore students’ perception of the effectiveness and the usefulness of the CCLS online platform. The findings suggested the CCLS online platform can help students to revisit their clinical performance via the recorded videos, facilitate student nurses’ self-reflection on their performance, and help student nurses to minimize the academic-practice gap. Participants in this study scored the CCLS online platform as helpful and easy to use during the learning process.
Ying Geng; Po-Sen Huang; Yeuh-Min Huang. Crowdsourcing in Nursing Education: A Possibility of Creating a Personalized Online Learning Environment for Student Nurses in the Post-COVID Era. Sustainability 2021, 13, 3413 .
AMA StyleYing Geng, Po-Sen Huang, Yeuh-Min Huang. Crowdsourcing in Nursing Education: A Possibility of Creating a Personalized Online Learning Environment for Student Nurses in the Post-COVID Era. Sustainability. 2021; 13 (6):3413.
Chicago/Turabian StyleYing Geng; Po-Sen Huang; Yeuh-Min Huang. 2021. "Crowdsourcing in Nursing Education: A Possibility of Creating a Personalized Online Learning Environment for Student Nurses in the Post-COVID Era." Sustainability 13, no. 6: 3413.
In traditional schools, where education and teaching tend to be subject-oriented, the standardization of the teaching materials of health education courses would be obscurely related to know-how of daily life. This frustrates the learners from developing the awareness of engagement, thereby decreasing their willingness to acquire new information or skill. Therefore, in this study, a board game assimilating augmented reality (AR) into health education is presented. It associates the card game, slides, and learning sheets gamification teaching model with the learning experience; and proposes the efficacy of the board games mingled with augmented reality to enhance the motivation in learning and confidence in technology. In this experiment for a health education board game, 52 high school students participated in this experiment. There were 25 in the experimental group (with AR) and 27 in the control group (without AR). The IMMS (instructional material motivation survey) and the TAM (technology acceptance model) are applied to acquire quantitative data for examination. The findings are as follows: (1) The acceptance was significantly affected by the integration of AR into the health education board game and (2) the learning motivation was significantly affected by the integration of AR into the health education board game.
Hao-Chiang Lin; Yu-Hsuan Lin; Tao-Hua Wang; Lun-Ke Su; Yueh-Min Huang. Effects of Incorporating Augmented Reality into a Board Game for High School Students’ Learning Motivation and Acceptance in Health Education. Sustainability 2021, 13, 3333 .
AMA StyleHao-Chiang Lin, Yu-Hsuan Lin, Tao-Hua Wang, Lun-Ke Su, Yueh-Min Huang. Effects of Incorporating Augmented Reality into a Board Game for High School Students’ Learning Motivation and Acceptance in Health Education. Sustainability. 2021; 13 (6):3333.
Chicago/Turabian StyleHao-Chiang Lin; Yu-Hsuan Lin; Tao-Hua Wang; Lun-Ke Su; Yueh-Min Huang. 2021. "Effects of Incorporating Augmented Reality into a Board Game for High School Students’ Learning Motivation and Acceptance in Health Education." Sustainability 13, no. 6: 3333.
Accurate cellular traffic prediction becomes more and more critical for efficient network resource management in the Internet of Things (IoT). However, high-accuracy prediction results are usually accompanied by high computational capacity requirements. Although many lightweight neural network models have been proposed, some lightweight mechanisms will easily destroy the features of the raw data. Not all lightweight mechanisms are suitable for network traffic prediction. Therefore, this study proposes and optimizes an input data conversion method to extract the features of spatio-temporal dependencies based on convolutional neural network (CNN) architecture. In addition, we also propose a lightweight neural network model to reduce the computational cost for cellular traffic prediction problem and use mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) to evaluate the prediction accuracy. The experimental results show that the proposed model is better than CNN, ConvLstm, and Densenet as well as can greatly reduce the parameters of the neural network while maintaining prediction accuracy.
Wei-Che Chien; Yueh-Min Huang. A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things. The Journal of Supercomputing 2021, 77, 10023 -10039.
AMA StyleWei-Che Chien, Yueh-Min Huang. A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things. The Journal of Supercomputing. 2021; 77 (9):10023-10039.
Chicago/Turabian StyleWei-Che Chien; Yueh-Min Huang. 2021. "A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things." The Journal of Supercomputing 77, no. 9: 10023-10039.
Tessaratoma papillosa (Drury) first invaded Taiwan in 2009. Every year, T. papillosa causes severe damage to the longan crops. Novel applications for edge intelligence are applied in this study to establish an intelligent pest recognition system to manage this pest problem. We used a detecting drone to photograph the pest and employed a Tiny-YOLOv3 neural network model built on an embedded system NVIDIA Jetson TX2 to recognize T. papillosa in the orchard to determine the position of the pests in real-time. The pests’ positions are then used to plan the optimal pesticide spraying route for the agricultural drone. Apart from planning the optimized spraying of pesticide for the spraying drone, the TX2 embedded platform also transmits the position and generation of pests to the cloud to record and analyze the growth of longan with a computer or mobile device. This study enables farmers to understand the pest distribution and take appropriate precautions in real-time. The agricultural drone sprays pesticides only where needed, which reduces pesticide use, decreases damage to the environment, and increases crop yield.
Ching-Ju Chen; Ya-Yu Huang; Yuan-Shuo Li; Ying-Cheng Chen; Chuan-Yu Chang; Yueh-Min Huang. Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying. IEEE Access 2021, 9, 21986 -21997.
AMA StyleChing-Ju Chen, Ya-Yu Huang, Yuan-Shuo Li, Ying-Cheng Chen, Chuan-Yu Chang, Yueh-Min Huang. Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying. IEEE Access. 2021; 9 ():21986-21997.
Chicago/Turabian StyleChing-Ju Chen; Ya-Yu Huang; Yuan-Shuo Li; Ying-Cheng Chen; Chuan-Yu Chang; Yueh-Min Huang. 2021. "Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying." IEEE Access 9, no. : 21986-21997.
Not many review studies have explored the theoretical foundation of cross-cultural learning or the curricula in the research they were reviewing. Furthermore, some review studies only superficially discussed the methodology and findings of the reviewed articles. To address these issues, we reviewed twenty-three studies on technology-supported cross-cultural learning published between 2014 and 2020. We aimed to summarize and analyze previous research in the following areas: (1) theoretical foundation, (2) curricula, (3) technologies, and (4) methodology and findings. Our results showed that the reviewed studies built their research framework based on diverse theoretical foundations; however, the most frequently used models were Byram’s model and the cultural convergence theory. Curricula had the following main focuses: (a) cross-cultural learning, (b) linguistic skills, and (c) pre-service teacher training. The most frequently used technologies were Skype, e-mail, and blogs. We found that most reviewed studies involved the collection of both qualitative and quantitative data. Finally, most of the reviewed studies reported on the role of technologies in facilitating cross-cultural learning, FL/SL learning, and pre-service teacher training. Based on our findings, several implications along with suggestions were prepared. Our findings demonstrated that results from most studies were positive regarding technological support of cross-cultural learning. Therefore, it is suggested that educators and researchers take these results into consideration when designing future studies on cross-cultural learning. Because many scholars did not report some important information, such as what theoretical foundation they built studies on or participants’ demographics, we suggest that such information needs to be included in their research articles as it can be helpful in informing future studies. We also suggest that participants in future studies use variety of technological tools for supporting communication and content creation during cross-cultural learning.
Rustam Shadiev; Xueying Wang; Ting-Ting Wu; Yueh-Min Huang. Review of Research on Technology-Supported Cross-Cultural Learning. Sustainability 2021, 13, 1402 .
AMA StyleRustam Shadiev, Xueying Wang, Ting-Ting Wu, Yueh-Min Huang. Review of Research on Technology-Supported Cross-Cultural Learning. Sustainability. 2021; 13 (3):1402.
Chicago/Turabian StyleRustam Shadiev; Xueying Wang; Ting-Ting Wu; Yueh-Min Huang. 2021. "Review of Research on Technology-Supported Cross-Cultural Learning." Sustainability 13, no. 3: 1402.
This study aims to investigate whether the feedback designed based on EEG (electroencephalography) signals and mind-mapping contributes to student attention, performance, and self-efficacy. The EEG headset was used to collect and measure the participant’s attention levels. This study uses a mixed-methods of quasi-experimental design. The participants were 30 graduate students that randomly assigned to the control (non-feedback) group and experimental (with-feedback) group. A random grouping was used to divide the participants into two groups, control and experimental. The participants in experimental group will receive both negative and positive audio feedback. The research finding shows that the participants who receive the feedback had higher attention state and significant influence of self-efficacy compared to those in the groups without feedback. And the feedback does not influence the participant’s performance. Meanwhile, participant’s mind-maps score and performance between the two groups showed no significant influence. This study suggest for future studies, to explore the effect of different types of feedback on students attention.
Astrid Tiara Murti; Ting-Ting Wu; Yueh-Min Huang. Combining EEG Feedback on Student Performance and Self-efficacy. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 13 -22.
AMA StyleAstrid Tiara Murti, Ting-Ting Wu, Yueh-Min Huang. Combining EEG Feedback on Student Performance and Self-efficacy. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():13-22.
Chicago/Turabian StyleAstrid Tiara Murti; Ting-Ting Wu; Yueh-Min Huang. 2020. "Combining EEG Feedback on Student Performance and Self-efficacy." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 13-22.
This study uses learn, use, practice, design, apply/analyze (LUPDA) theory to combine science, technology, engineering, art, and math (STEAM) and computational thinking (CT) concepts to develop assessment principles. The STEAM teaching activity designs and implements an artificial intelligence (AI) webcam game with micro:bit technology, AI computer vision, and deep learning techniques to recognize the user’s hand gestures via webcam. The game in our teaching experiment which can automatically interpret the user’s gestures as scissors, stone, or cloth through the webcam, and then automatically react to the user through a motor. Finally, this study proposes a set of relevant assessment principles based on STEAM, LUPDA theory, and CT concepts.
Chih-Hung Wu; Yueh-Min Huang. Integration of LUPDA Theory and STEAM with Computational Thinking Concepts to Develop Assessment Principles for an AI Based STEAM Activity. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 268 -276.
AMA StyleChih-Hung Wu, Yueh-Min Huang. Integration of LUPDA Theory and STEAM with Computational Thinking Concepts to Develop Assessment Principles for an AI Based STEAM Activity. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():268-276.
Chicago/Turabian StyleChih-Hung Wu; Yueh-Min Huang. 2020. "Integration of LUPDA Theory and STEAM with Computational Thinking Concepts to Develop Assessment Principles for an AI Based STEAM Activity." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 268-276.
Reading comprehension is one of the English language abilities for academic learning and as a crucial component of lifelong learning. Through reading, students will develop themselves and achieve progress in every aspect of their life. Referring to the importance of reading, appear the question of how to improve students’ reading comprehension skills. Therefore, teachers should develop their method effectively and use appropriate learning strategies independently to improve students’ reading comprehension skills. One best strategic method to develop reading comprehension skills is the reciprocal teaching. Therefore, this study was preliminary research to obtain perceptions about the design of reciprocal teaching combine with collaborative learning in large classes. This study conducted a qualitative approach to collect some perceptions from the English expert. The participants were English teachers of Polytechnic in Indonesia. Then, the result of the study was students predicted more interactive, communicative, active group discussion, critical thinking, motivation, leadership, and cooperation.
Olivia De H. Basoeki; Ting-Ting Wu; Yueh-Min Huang. Design of Reciprocal Teaching-Collaborative Learning Approach in Enhancing Students’ Reading Comprehension Skill. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 23 -32.
AMA StyleOlivia De H. Basoeki, Ting-Ting Wu, Yueh-Min Huang. Design of Reciprocal Teaching-Collaborative Learning Approach in Enhancing Students’ Reading Comprehension Skill. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():23-32.
Chicago/Turabian StyleOlivia De H. Basoeki; Ting-Ting Wu; Yueh-Min Huang. 2020. "Design of Reciprocal Teaching-Collaborative Learning Approach in Enhancing Students’ Reading Comprehension Skill." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 23-32.
There has been an ongoing proliferation of online articles and other materials on the World Wide Web for e-learning. Although a generic search engine can be used to find materials in a subject domain (for example, computer science,) the search results often have advertising, media, and news mixed in. To improve the search quality, in this study an information search platform based on data mining technology was constructed. Using term frequency-inverse document frequency (TF-IDF), this platform calculates all terms in each web article to automatically filter out non-computer science category keywords and articles. The search platform enables students quickly find and read information in articles for a given set of search keywords. The experimental results show improved learning performance with increased computer science knowledge and concepts and more computer science articles found using the information search platform by filtering out articles in non-computer science categories.
Shu-Chen Cheng; Yu-Ping Cheng; Yueh-Min Huang; I. Robert Chiang. Constructing an Information Search Platform Using Data Mining to Improve Student Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 227 -235.
AMA StyleShu-Chen Cheng, Yu-Ping Cheng, Yueh-Min Huang, I. Robert Chiang. Constructing an Information Search Platform Using Data Mining to Improve Student Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():227-235.
Chicago/Turabian StyleShu-Chen Cheng; Yu-Ping Cheng; Yueh-Min Huang; I. Robert Chiang. 2020. "Constructing an Information Search Platform Using Data Mining to Improve Student Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 227-235.
This paper examines a method which can be used by instructors pursuing innovative methods for language teaching, which expands learners’ motivation in second language learning. Computational thinking (CT) is a problem-solving skill which can motivate students’ English language learning. Designing a learning activity which integrates CT into English language learning has been considered in only a few academic studies. This study aimed to explore whether integrating CT into English language learning can be useful for improving learners’ motivation and performance. The method of “present, practice, and produce” was applied as a method of presenting computational thinking in the English language learning classroom. Fifty-two elementary school students (52) participated in the experimental study. Following an experimental design, data were collected and analyzed from a combination of knowledge test scores, storytelling, motivation, and anxiety surveys. The experimental results indicate that the CT strategy improves students’ language learning and raises their motivation in the two dimensions of extrinsic and intrinsic goal orientation. These results imply the positive effect of CT strategy on strengthening problem-solving skills of students participating in digital storytelling and increases their motivation and performance in English language learning.
Nadia Parsazadeh; Pei-Yu Cheng; Ting-Ting Wu; Yueh-Min Huang. Integrating Computational Thinking Concept Into Digital Storytelling to Improve Learners’ Motivation and Performance. Journal of Educational Computing Research 2020, 59, 470 -495.
AMA StyleNadia Parsazadeh, Pei-Yu Cheng, Ting-Ting Wu, Yueh-Min Huang. Integrating Computational Thinking Concept Into Digital Storytelling to Improve Learners’ Motivation and Performance. Journal of Educational Computing Research. 2020; 59 (3):470-495.
Chicago/Turabian StyleNadia Parsazadeh; Pei-Yu Cheng; Ting-Ting Wu; Yueh-Min Huang. 2020. "Integrating Computational Thinking Concept Into Digital Storytelling to Improve Learners’ Motivation and Performance." Journal of Educational Computing Research 59, no. 3: 470-495.
In traditional school education, the content of health education courses cannot be easily linked to daily life experiences. This results in the low application of acquired knowledge and hinders students from gaining hands-on experience and a sense of accomplishment through courses, thereby lowering the learners’ engagement and willingness to learn. This study designed a board game integrated with augmented reality (AR) for health education; incorporated the card-game, slides, and learning-sheets (CSLS) gamification teaching model into the learning process; and discussed the effectiveness of board games with augmented reality in improving learning outcomes and emotions. The research participants were 52 senior high school students, who were assigned to the experimental (AR health education board game) or control (health education board game) group in the teaching experiment. The research findings reveal the following. The two groups were significantly different in terms of (1) learning outcomes, (2) negative emotions, (3) flow state in the game.
Hao-Chiang Lin; Yu-Hsuan Lin; Tao-Hua Wang; Lun-Ke Su; Yueh-Min Huang. Effects of Incorporating AR into a Board Game on Learning Outcomes and Emotions in Health Education. Electronics 2020, 9, 1752 .
AMA StyleHao-Chiang Lin, Yu-Hsuan Lin, Tao-Hua Wang, Lun-Ke Su, Yueh-Min Huang. Effects of Incorporating AR into a Board Game on Learning Outcomes and Emotions in Health Education. Electronics. 2020; 9 (11):1752.
Chicago/Turabian StyleHao-Chiang Lin; Yu-Hsuan Lin; Tao-Hua Wang; Lun-Ke Su; Yueh-Min Huang. 2020. "Effects of Incorporating AR into a Board Game on Learning Outcomes and Emotions in Health Education." Electronics 9, no. 11: 1752.
In this study, artificial intelligence and image recognition technologies are combined with environ-mental sensors and the Internet of Things (IoT) for pest identification. Real-time agricultural meteorology and pest identification systems on mobile applications are evaluated based on intelligent pest identification and environ-mental IoT data. We combined the current mature AIoT technology and deep learning and applied it to smart agriculture. We used deep learning YOLOv3 for image recognition to obtain the location of Tessaratoma papillosa and analyze the environmental information from weather stations through Long Short-Term Memory (LSTM) to predict the occurrence of pests. The experimental results showed that the pest identification accuracy reached 90%. Precise positioning can effectively reduce the amount of pesticides used and reduce pesticide damage to the soil. The current research provides the location of the pest and the extent of the pests to farmers can accurately use pesticide application at a precise time and place and thus reduce the agricultural workforce required for timely pest control, thus achieving the goal of smart agriculture. The proposed system notifies farmers of the presence of different pests before they start multiplying in large numbers. It improves overall agricultural economic value by providing appropriate pest control methods that decrease crop losses and reduce the environmental damage caused by the excessive usage of pesticides.
Ching-Ju Chen; Ya-Yu Huang; Yuan-Shuo Li; Chuan-Yu Chang; Yueh-Min Huang. An AIoT Based Smart Agricultural System for Pests Detection. IEEE Access 2020, 8, 180750 -180761.
AMA StyleChing-Ju Chen, Ya-Yu Huang, Yuan-Shuo Li, Chuan-Yu Chang, Yueh-Min Huang. An AIoT Based Smart Agricultural System for Pests Detection. IEEE Access. 2020; 8 (99):180750-180761.
Chicago/Turabian StyleChing-Ju Chen; Ya-Yu Huang; Yuan-Shuo Li; Chuan-Yu Chang; Yueh-Min Huang. 2020. "An AIoT Based Smart Agricultural System for Pests Detection." IEEE Access 8, no. 99: 180750-180761.
The rapid development of technologies such as tablet PCs and 4G/5G networks has further enhanced the benefits of mobile learning. Although mobile devices are convenient and provide a variety of learning benefits, they are unable to improve students’ learning outcomes without an appropriate learning strategy. Furthermore, little research has been conducted to examine the effects of using collaborative learning on mobile devices. This study proposed a cooperative learning framework using Google Docs to explore the learning outcomes of students of natural science in an elementary curriculum. The study was of a quasi-experimental design with an experimental group (cooperative learning) and a control group (personal learning). The results show that a cooperative learning approach using Google Docs improved learning outcomes, teaching interest, and understanding of campus plants, and reduced cognitive load. One conclusion of the study is that the collaborative learning approach associated with mobile learning is more effective than personal learning. In addition, this paper also provides brief recommendations to expand on the study’s limitations. Future work should investigate the impact of collaborative learning on different environments for mobile learning.
Po-Sen Huang; Po-Sheng Chiu; Yueh-Min Huang; Hua-Xu Zhong; Chin-Feng Lai. Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools. Sustainability 2020, 12, 6606 .
AMA StylePo-Sen Huang, Po-Sheng Chiu, Yueh-Min Huang, Hua-Xu Zhong, Chin-Feng Lai. Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools. Sustainability. 2020; 12 (16):6606.
Chicago/Turabian StylePo-Sen Huang; Po-Sheng Chiu; Yueh-Min Huang; Hua-Xu Zhong; Chin-Feng Lai. 2020. "Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools." Sustainability 12, no. 16: 6606.
Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortcoming could in many cases have a negative impact on the prediction power of both the EDM and LA based approaches. To address such issues, this work proposes an evaluation approach that automatically compares several clustering methods using multiple internal and external performance measures on 9 real-world educational datasets of different sizes, created from the University of Tartu’s Moodle system, to produce two-way clustering. Moreover, to investigate the possible effect of normalization on the performance of the clustering algorithms, this work performs the same experiment on a normalized version of the datasets. Since such an exhaustive evaluation includes multiple criteria, the proposed approach employs a multiple criteria decision-making method (i.e., TOPSIS) to rank the most suitable methods for each dataset. Our results reveal that the proposed approach can automatically compare the performance of the clustering methods and accordingly recommend the most suitable method for each dataset. Furthermore, our results show that in both normalized and nonnormalized datasets of different sizes with 10 features, DBSCAN and k-medoids are the best clustering methods, whereas agglomerative and spectral methods appear to be among the most stable and highly performing clustering methods for such datasets with 15 features. Regarding datasets with more than 15 features, OPTICS is among the top-ranked algorithms among the nonnormalized datasets, and k-medoids is the best among the normalized datasets. Interestingly, our findings reveal that normalization may have a negative effect on the performance of certain methods, e.g., spectral clustering and OPTICS; however, it appears to mostly have a positive impact on all of the other clustering methods.
Danial Hooshyar; YeongWook Yang; Margus Pedaste; Yueh-Min Huang. Clustering Algorithms in an Educational Context: An Automatic Comparative Approach. IEEE Access 2020, 8, 146994 -147014.
AMA StyleDanial Hooshyar, YeongWook Yang, Margus Pedaste, Yueh-Min Huang. Clustering Algorithms in an Educational Context: An Automatic Comparative Approach. IEEE Access. 2020; 8 (99):146994-147014.
Chicago/Turabian StyleDanial Hooshyar; YeongWook Yang; Margus Pedaste; Yueh-Min Huang. 2020. "Clustering Algorithms in an Educational Context: An Automatic Comparative Approach." IEEE Access 8, no. 99: 146994-147014.
Scholars suggest that not every student completely comprehends the content of a lecture in a foreign language as the medium of instruction, especially in the case of those with low language ability. To facilitate comprehension of lectures in a foreign language, learning content was presented to students in multiple modalities; that is, in addition to verbal (speech of the instructor) and visual (lecture slides) content, texts generated by speech-to-text recognition (STR) or speech-enabled language translation (SELT) were shown to the students. The goal was to compare how these two additional content modalities (i.e., STR-texts vs. SELT-texts) facilitate student comprehension of lecture content. Because processing multimodal content requires additional cognitive resources, another goal was to explore whether STR-texts versus SELT-texts impose any cognitive load on the students. To this end, two groups of students were recruited, where they attended two lectures at the intermediate and advanced levels. STR-texts were shown to a control group, and SELT-texts were shown to an experimental group. The posttest results and the cognitive load of the students in both groups after each lecture were compared. Four main findings were obtained: (a) The experimental group outperformed the control group on both posttests. However, when student language ability was considered, the difference was statistically significant for low ability students only; (b) there was not a significant between-group difference in cognitive load; however, if student language ability was considered, a significant between-group difference existed during the more difficult lecture; (c) between-group differences in self-efficacy were statistically insignificant; and (d) associations among some research variables were found. Based on these results, several implications were drawn for the teaching and research community.
Rustam Shadiev; Yu-Cheng Chien; Yueh-Min Huang. Enhancing Comprehension of Lecture Content in a Foreign Language as the Medium of Instruction: Comparing Speech-to-Text Recognition With Speech-Enabled Language Translation. SAGE Open 2020, 10, 1 .
AMA StyleRustam Shadiev, Yu-Cheng Chien, Yueh-Min Huang. Enhancing Comprehension of Lecture Content in a Foreign Language as the Medium of Instruction: Comparing Speech-to-Text Recognition With Speech-Enabled Language Translation. SAGE Open. 2020; 10 (3):1.
Chicago/Turabian StyleRustam Shadiev; Yu-Cheng Chien; Yueh-Min Huang. 2020. "Enhancing Comprehension of Lecture Content in a Foreign Language as the Medium of Instruction: Comparing Speech-to-Text Recognition With Speech-Enabled Language Translation." SAGE Open 10, no. 3: 1.
In this study, we carried out an online cross-cultural learning activity supported by speech-enabled language translation technology on a social network service with representatives from 13 nationalities. The participants were assigned into two groups: Group I discussed the traditions and related culture of interest whereas Group II discussed traditions, culture, and any other topics of interest. We tested the effectiveness of the learning activity supported by speech-enabled language translation technology on cross-cultural learning; analysed the social network; measured the cultural constructs, and investigated the relationship between the cultural constructs and cross-cultural learning. The results revealed that Group I outperformed Group II in terms of both procedural and declarative knowledge. The results showed that Group II had better social network characteristics; for example, Group I had fewer edges and a lower average network degree than Group II. In terms of cultural constructs, the results related to power distance, individualism, and uncertainty avoidance were contradictory to those of earlier research. Finally, we found no relationship between the cultural constructs and cross-cultural learning. In this paper, we discuss implications for and suggestions to the field of technology-supported cross-cultural learning based on the results.
Rustam Shadiev; Yueh-Min Huang. Exploring the influence of technological support, cultural constructs, and social networks on online cross-cultural learning. Australasian Journal of Educational Technology 2020, 36, 104 -118.
AMA StyleRustam Shadiev, Yueh-Min Huang. Exploring the influence of technological support, cultural constructs, and social networks on online cross-cultural learning. Australasian Journal of Educational Technology. 2020; 36 (3):104-118.
Chicago/Turabian StyleRustam Shadiev; Yueh-Min Huang. 2020. "Exploring the influence of technological support, cultural constructs, and social networks on online cross-cultural learning." Australasian Journal of Educational Technology 36, no. 3: 104-118.
To effectively increase the employment rate of higher education graduates, higher education institutions are doing their best to provide the most high-quality technologized interdisciplinary curriculum, to educate professional expertise in decision-making and to fortify student employability. Therefore, after executing a series of evaluated measurements, there are four highly valuable and contributive conclusions and findings. First, judgeability was the most critical decision-making employability factor and was directly influenced by the self-efficacy (SE), self-control (SC) and self-regulation (SR) of the autonomy-learning performance of social learning theory (SLT). Second, the SE of autonomy-learning performance of SLT was positively impacted by the behavioral intention to use and actual system use of the technology acceptance model (TAM), and monitor, control and evaluate decision-making, select the best solutions, clarify the objectiveness to be achieved and search for possible solutions of rational decision-making model (RDMM). It is necessary for higher education graduates to possess judgeability to confidently deal with problem-solving issues by actually using diversified technological applications for clarifying, monitoring, controlling and evaluating the decision-making objectiveness, and to comprehensively search the possible solutions, in order to eventually induce the best solutions for the problem. Third, define and diagnose the issues or problems of the RDMM model affected by the self-control (SC) of autonomy-learning performance of the SLT theory, because higher education graduates have to possess justifiability to define and diagnose the problem-solving issues in-depth, by exercising the introspective self-correcting capacities cultivated from an interdisciplinary curriculum. Lastly, actual system use of the TAM indeed impacted the SR of the autonomy-learning performance of SLT, because higher education graduates have to assess, revise and justify their self-actions in thinking, motivation, feeling, cognition and behaviors, by self-observing and accumulating experience from an interdisciplinary curriculum.
Yueh-Min Huang; Ming Yuan Hsieh; Muhammet Usak. A Multi-Criteria Study of Decision-Making Proficiency in Student’s Employability for Multidisciplinary Curriculums. Mathematics 2020, 8, 897 .
AMA StyleYueh-Min Huang, Ming Yuan Hsieh, Muhammet Usak. A Multi-Criteria Study of Decision-Making Proficiency in Student’s Employability for Multidisciplinary Curriculums. Mathematics. 2020; 8 (6):897.
Chicago/Turabian StyleYueh-Min Huang; Ming Yuan Hsieh; Muhammet Usak. 2020. "A Multi-Criteria Study of Decision-Making Proficiency in Student’s Employability for Multidisciplinary Curriculums." Mathematics 8, no. 6: 897.
This study investigated and compared the effectiveness of both digital game-based learning (DGBL) and static e-learning material for Newton’s laws of motion on students’ learning attention, affective experiences, cognitive load and academic achievement. Physiological signals and affective techniques were adopted to measure students’ learning affective states and cognitive load. After learning, a post-test was then conducted to discover the differences in academic achievement between DGBL and static e-learning. The results showed that the DGBL group displayed greater variance in positive emotion and attention than the traditional e-learning group during the learning process, as well as a greater cognitive load. Based on the timeline measurement of attention and positive emotion patterns in the DGBL and e-learning groups, the largest gap in both attention and positive emotion patterns was found when the DGBL group members were about to finish playing the game. The findings of this study revealed that emotional well-being and increased attention are the key advantages that DGBL learning provides when compared with traditional e-learning approaches.
Chih-Hung Wu; Yi-Lin Tzeng; Yueh-Min Huang. Measuring performance in leaning process of digital game-based learning and static E-learning. Educational Technology Research and Development 2020, 68, 2215 -2237.
AMA StyleChih-Hung Wu, Yi-Lin Tzeng, Yueh-Min Huang. Measuring performance in leaning process of digital game-based learning and static E-learning. Educational Technology Research and Development. 2020; 68 (5):2215-2237.
Chicago/Turabian StyleChih-Hung Wu; Yi-Lin Tzeng; Yueh-Min Huang. 2020. "Measuring performance in leaning process of digital game-based learning and static E-learning." Educational Technology Research and Development 68, no. 5: 2215-2237.