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Correction to: From academic achievement to career development: does self‐regulated learning matter?

International Journal for Educational and Vocational Guidance 2024年 1-2页

作者： Hsu, Anna J. C. Chen, Mavis Yi-Ching Shin, Nai-Fang Department of Management The Chinese University of Hong Kong Shatin Hong Kong Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Department of Management Huei Yeh Corporation Taipei Taiwan

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Do Job Candidates Perform Honest and Deceptive Impression Management Differently in Automated Video Interviews with Different Ai Interfaces?

SSRN

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SSRN 2022年

作者： Suen, Hung-Yue Collin Hung, Kuo-En Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City Taiwan

Automated video interviews powered by artificial intelligence (AI) are increasingly being adopted by employers to screen job candidates despite concerns regarding the humanity and transparency of AI. Accordingly, researchers and practitioners advocate overcoming these concerns by refining AI interfaces in terms of tangibility, immediacy, and transparency. However, AI video interviews featuring different interfaces may impact interviewees’ tendencies to engage in impression management behaviors (IMs), which can either improve or impair personnel selection validity. This study addressed calls for research to investigate the issues mentioned above by conducting a field study to explore the ways in which AI and AI interfaces affect candidates’ IMs in asynchronous video interviews (AVIs). We developed three AVI interfaces and measured real job applicants’ self-reported IMs across four experimental treatments. We found that different AI interfaces could increase or decrease candidates’ honesty IMs and deceptive IMs in different ways. An exploratory analysis also found that candidates’ interview anxiety could be mitigated by an AI interface. © 2022, The Authors. All rights reserved.

关键词： Transparency

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Advancing Elderly Care Through AI: The Jubo VitalLink System's Role in Home Nursing and Intelligent Monitoring 1

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Tsai, Yun-Cheng Liu, Cheng-Yang Peter Li, Yi-Fen Kang, Shih-Chung Department of Technology Application and Human Resource Development National Taiwan Normal University No. 162 He-Ping East Road Sec1 Taipei10610 Taiwan Smart Ageing Tech Co. Ltd. 6F No. 213 Sec. 3 Beixin Rd. Xindian District New Taipei City231030 Taiwan

ISBN: (数字)9789819758104

ISBN: (纸本)9789819758098

This study explores the use of AI in nursing care, focusing on the Jubo VitalLink VS-3 AI Vital Signs Measurement Cart in Long-Term Care (LTC) facilities. It highlights AI's role in improving patient outcomes and operational efficiency in LTC, using deep learning and the Isolation Forest algorithm for vital signs anomaly detection. The research addresses challenges such as algorithmic biases and privacy concerns, showing the AI model's high accuracy and recall rates in anomaly detection. It also discusses the importance of explainable AI in enhancing decision-making and integrating AI into LTC practices, emphasizing AI's potential to complement human judgment and improve nursing care in LTC settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nursing

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A study on the effects of using gamification with the 6E model on high school students’ computer programming self-efficacy, IoT knowledge, hands-on skills, and behavioral patterns

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Educational technology research and development 2023年第4期71卷 1821-1849页

作者： Hsiao, Hsien-Sheng Chen, Jyun-Chen Chen, Jhen-Han Chien, Yu-Hung Chang, Chung-Pu Chung, Guang-Han Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City Taiwan R.O.C. National Academy for Educational Research New Taipei City Taiwan R.O.C.

Since the late twentieth century, with the development of the Internet of Things (IoT), the IoT covers the application of comprehensive knowledge and technology in the fields of circuitry, physics, mechanics, and information, making it a suitable topic for hands-on science, technology, engineering, and mathematics (STEM) activities. The IoT covers a large amount of knowledge, practical skills, and programming skills in STEM fields, both teaching and learning the content can be difficult. Thus, this study used gamification with the 6E model and the software development method as the teaching strategies to explore their effects on high school students’ computer programming self-efficacy, IoT knowledge, and hands-on performance in IoT learning activities. In this study, a quasi-experimental design was used for 12 weeks, and the 132 students who participated in the experiment were divided into Experimental Group 1 (EG1, 66 students using gamification with the 6E model), Experimental Group 2 (EG2, 31 students using the 6E model only), and the Control Group (CG, 35 students using the software development method). Through Analysis of Covariance, the results showed that the students in EG 1 achieved higher academic performance in terms of computer programming self-efficacy, IoT knowledge, and hands-on skills. The results of the lag sequence analysis of behavioral patterns showed that all the students required frequent two-way communication with the teachers and needed to communicate with their group members. The students in EG 1 exhibited positive interactions and took the initiative in asking for help from other students, which indicated that the students in this group achieved better learning outcomes. In addition, those interested in exploring STEM hands-on activities would benefit from our findings.

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MiniNet: Dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems

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ISA Transactions 2023年 132卷 120-130页

作者： Tseng, Fan-Hsun Yeh, Kuo-Hui Kao, Fan-Yi Chen, Chi-Yuan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Information Management National Dong Hwa University Hualien Taiwan Computer Science and Engineering Department of National Sun Yat-sen University Kaohsiung804 Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan

In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with high accuracy under the limited hardware resource is a vital issue. In this paper, on the basis of MobileNet architecture, the dense squeeze with depthwise separable convolutions model is proposed, viz. MiniNet. MiniNet utilizes depthwise and pointwise convolutions, and is composed of the dense connection technique and the Squeeze-and-Excitation operations. The proposed MiniNet model is implemented and experimented with Keras. In experimental results, MiniNet is compared with three existing models, i.e., DenseNet, MobileNet, and SE-Inception-Resnet-v1. To validate that the proposed MiniNet model is provided with less computation and shorter training time, two types as well as large and small datasets are used. The experimental results showed that the proposed MiniNet model significantly reduces the number of parameters and shortens training time efficiently. MiniNet is superior to other models in terms of the lowest parameters, shortest training time, and highest accuracy when the dataset is small, especially. © 2022 ISA

关键词： Convolution

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Engineering design learning for high school and college first-year students in a STEM battlebot design project

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International Journal of STEM Education 2023年第1期10卷 1-15页

作者： Chien, Yu-Hung Liu, Chia-Yu Chan, Shaio-Chung Chang, Yu-Shan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Research Center for Testing and Assessment National Academy for Educational Research Taipei Taiwan Department of Product Design Ming Chuan University Taoyuan Taiwan

There is a worldwide trend to include engineering design in high school curricula as a bridge course to higher-level STEM education and to increase high school students’ interest in STEM fields. This study used a battlebot design curriculum to compare engineering design learning between high school and college first-year students and then proposed suggestions for curriculum planning that promoted the continuity of learning between different levels of engineering design education. This study used the creative product analysis matrix (CPAM) and lag sequential analysis (LSA) to explore the possible similarities and differences between the two groups’ understanding of engineering design. The results show that college first-year students were significantly better than high school students in CPAM, but the two groups were similar in their reflections on engineering design behaviors, indicating that the noncumulative learning results must be taken seriously. Higher-order engineering design thinking skills take a longer time to develop than technical skills. For both high school and college first-year students, it is important to enhance their higher-order engineering design thinking skills to promote higher engineering design performance. Moreover, high school students could be provided with convenient processing tools and easy-to-use, hands-on techniques to increase their technical skills. Educators from institutions of higher education and K-12 schools should work together to develop pedagogical models that provide rigorous, well-rounded education and outstanding engineering design instructions to most effectively cultivate STEM talent.

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Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews

Predicting behavioral competencies automatically from facial...

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作者： Su, Yu-Sheng Suen, Hung-Yue Hung, Kuo-En Department of Computer Science and Engineering National Taiwan Ocean University Keelung City Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City Taiwan

This work aims to develop a real-time image and video processor enabled with an artificial intelligence (AI) agent that can predict a job candidate’s behavioral competencies according to his or her facial expressions. This is accomplished using a real-time video-recorded interview with a histogram of oriented gradients and support vector machine (HOG-SVM) plus convolutional neural network (CNN) recognition. Different from the classical view of recognizing emotional states, this prototype system was developed to automatically decode a job candidate’s behaviors by their microexpressions based on the behavioral ecology view of facial displays (BECV) in the context of employment interviews using a real-time video-recorded interview. An experiment was conducted at a Fortune 500 company, and the video records and competency scores were collected from the company’s employees and hiring managers. The results indicated that our proposed system can provide better predictive power than can human-structured interviews, personality inventories, occupation interest testing, and assessment centers. As such, our proposed approach can be utilized as an effective screening method using a personal-value-based competency model. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Support vector machines

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A Study of Image Recognition for Standard Convolution and Depthwise Separable Convolution 12th

A Study of Image Recognition for Standard Convolution and De...

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12th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2020

作者： Tseng, Fan-Hsun Kao, Fan-Yi Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei10610 Taiwan

ISBN: (纸本)9783030571146

Artificial intelligence and deep learning techniques are all around our life. Image recognition and natural language processing are the two major topics. Through using TensorFlow-GPU as backend in convolutional neural network (CNN) and deep learning network, image recognition has been an extreme breakthrough in recent years. However, more and more model parameters result in overfitting problem and computation overhead. In the paper, the performance of image recognition between standard CNN and depthwise separable CNN is experimented and investigated. In addition, data augmentation technique is applied to both standard and depthwise separable CNNs to improve the image recognition accuracy. The experiments are implemented by an open source API called Keras with using CIFAR-10 dataset. Experimental results showed that the depthwise separable CNN improves validation accuracy compared with the standard CNN. Moreover, schemes with data augmentation achieve higher validation accuracy but training accuracy. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Convolution

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Engagement, Academic Emotions, and Artificial Intelligence application Creativity in the Metaverse: A Study of Facial Expressions

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Journal of Computer Assisted Learning 2025年第3期41卷

作者： Chang, Yu-Shan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan

Background: Due to the rapid development of artificial intelligence (AI) and the widespread adoption of online learning post-COVID-19, the metaverse has become an important strategy for innovative teaching. Objectives: This study aimed to investigate the impact of the metaverse on learning engagement, learning emotions, and creative performance in AI applications by analysing academic emotions through facial expressions. Methods: The study was conducted as a non-equivalent group pretest–posttest quasi-experimental construct involving 97 students from a public high school. The experimental teaching theme focused on AI image recognition and speech recognition, with the experimental group receiving online metaverse instruction and the control group receiving instruction through Google Meet. Results and Conclusion: Key findings include the following: the metaverse significantly and positively influenced cognitive, emotional, and social dimensions of learning engagement, but no impact was observed on the behavioural dimension;the metaverse had a substantial impact on the creative performance of AI application design, particularly in the empathy, definition, ideation, and testing stages;and the metaverse significantly affected academic emotions, with increased expressions of anger and sadness observed, particularly during the empathy, definition, and ideation stages of design thinking. Thus, the results of this study provide a pedagogical foundation for the adoption of the metaverse and auxiliary tools in teaching. © 2025 John Wiley & Sons Ltd.

关键词： academic emotions AI creativity engagement facial expression metaverse steam

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Enhancing Model Explainability in Financial Trading Using Training Aid Samples: A CNN-Based Candlestick Pattern Recognition Approach

Enhancing Model Explainability in Financial Trading Using Tr...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Yun-Cheng Tsai Jun-Hao Chen Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan R.O.C. Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C.

Candlestick pattern recognition is a widely adopted technique in financial trading, leveraging visual patterns to analyze price movements. Deep Convolutional Neural Networks (CNNs) have exhibited remarkable accuracy in this domain. However, the increasing demand for transparency and explainability in CNN-based models raises concerns regarding their applicability in trading decision-making. This paper addresses these concerns by presenting a framework that enhances the explainability of CNN-based candlestick pattern recognition models. Our approach introduces an innovative data augmentation method to generate training aid samples, facilitating the model’s learning process within human domains. By incorporating this framework, traders gain valuable insights into the decision-making process, comprehending the rationale behind the model’s predictions. Our proposed approach exposes the inherent “black box” nature of CNN-based models, improving their interpretability and empowering traders to make well-informed decisions based on transparent and understandable insights. This advancement holds significant potential for enhancing decision-making processes in financial trading and fostering trust among traders.

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