• International Journal of Contents
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• v.10 no.3
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• pp.26-34
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• 2014

Mobile learning is a practical learning method for busy adult learners because the mobility of digital devices can overcome the drawbacks of e-learning. However, research is strongly lacking in the theoretical exploration of mobile learning effects and functions and its empirical research. Moreover, the research of learning characteristics and learners' requirements must be considered before applying and disseminating mobile learning into the educational field. To address this shortcoming, this study conducted an online survey with 1,542 officers of the Ministry of Health and Welfare Affairs (MHWA) regarding learner recognition to mobile learning. The analysis of learners' attitudes toward mobile learning, based on age and position, indicated that subordinate workers appeared to place more value on mobile learning. Many participants preferred mobile learning because of its mobility and the effectiveness of anywhere and anytime. However, some participants continue to misunderstand mobile learning and its necessity. Therefore, consideration of learning effectiveness, the form of the content, and learner-centered learning must be reviewed in advance. This study could lead to practical implications of mobile learning.

• Smart Media Journal
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• v.9 no.3
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• pp.25-30
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• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.81-90
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• 2023

Climate change has resulted in increased frequency and intensity of heat waves, which poses a significant threat to the health and safety of construction workers, particularly those engaged in labor-intensive and heat-stress vulnerable working environments. To address this challenge, this study aimed to propose an interpretable machine learning approach for forecasting personal heat strain by considering the cumulative effect of heat exposure as a situational variable, which has not been taken into account in the existing approach. As a result, the proposed model, which incorporated the cumulative working time along with environmental and personal variables, was found to have superior forecast performance and explanatory power. Specifically, the proposed Multi-Layer Perceptron (MLP) model achieved a Mean Absolute Error (MAE) of 0.034 (℃) and an R-squared of 99.3% (0.933). Feature importance analysis revealed that the cumulative working time, as a situational variable, had the most significant impact on personal heat strain. These findings highlight the importance of systematic management of personal heat strain at construction sites by comprehensively considering the cumulative working time as a situational variable as well as environmental and personal variables. This study provided a valuable contribution to the construction industry by offering a reliable and accurate heat strain forecasting model, enhancing the health and safety of construction workers.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

• Culinary science and hospitality research
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• v.14 no.4
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• pp.27-40
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• 2008

This study focused on retraining programs for cuisiniers and drawing attention to the necessity of retraining and the new method of retraining for cuisiniers as a professional in a knowledge based society. Case study review and interviews followed by thorough review of related literatures were taken place to give suggestions and comments for the improvement of job performance enhance training for cuisiniers. Interviews were done with current participants and institutional trainers in March, 2008. Result from the interviews showed that retraining was really necessary. It was concluded that various retraining programs organized and developed by an association could be the solution. Methods of retraining can take formats of distance learning, classroom learning, and blended learning. However, working condition for most cuisiniers at present would allow them to squeeze some time off to attend higher education and most restaurant companies are not big enough to offer systematic job training on site or not stable enough to arrange replacement workers. It is therefore suggested to adopt a blended learning method and further study on this subject is highly recommended.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1846-1851
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• 2007

According to the Railroad Safety Act and section 42 of the Enforcement Ordinance, railway operators should conduct railway safety training regularly(6 or 3 hours per three months). But Overall Railroad Safety Audit conducted 2006 pointed out nonfulfillment of a regulations on railway worker's safety training to each of every 4 railway operators, which proved that the training management had not carried out properly. E-learning is used in various fields with development of Internet and IT technologies. It might be a good alternative tool for railway workers who is in shift working of the company 24 hours a day. Because it is difficult to collect those employees for training, e-learning could overcome obstacles of time and distance. In order to find out suitable e-learning model to railway sector, e-learning system for railway safety training has been researched through investigating e-learning technology and present railway safety training condition.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.193-202
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• 2004

In this paper, we studied about method that instructor and learner execute teaching-learning activities and about the educational effects of a web based practice instruction. And, in practice instruction, we examined about role and influence of instructor, learner and manager. This study shows the method that instructor and learner execute teaching-learning activities and interaction activities in a "construction of e-business system" curriculum to support the informational education for an industrial workers. The research subject was the 15 industrial workers who enrolled a informational instruction coulee provided for two weeks by kimpo college, As a research method, workers survey, interview, and profile analysis were used for this study. The result of this study show that interaction between instructor, learner and manager was not executed actively, but the manner of lecturing about interest of learner, usage of email, question and answer of bulletin board, online-practice were brought an effect on interaction of learner activities and the educational effects.l effects.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.11-21
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• 2021

In order to explore the job crafting experience of millennial safety workers, this study conducted a qualitative case research with five safety workers of S Energy from March 26 to September 27, 2021 . As a result of the analysis, task crafting showed 'matching one's strong suit with a given task', 'expanding work knowledge using social network service (SNS)', and 'making changes in job performance methods for preemptive safety management activities'. Also, Cognitive crafting showed 'recognition of social vocation as a safety job', 'recognition of a role to grow as a safety management expert', and 'cognitive changes from means of organizational adaptation to enjoyment and energy of life'. At the same time, in relation crafting, 'establishment of amicable relationships through SNS in non-face-to-face and rapid communicating situations', 'safety management made through with mutual cooperations between business people', and 'reborn as a mutual safety net in business relationships' appeared. These can be used as basic data to accumulate the theoretical basis for job crafting research of millennial safety workers and to improve their job satisfaction. A follow-up study was proposed for safety workers with occupations of various kinds.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.701-707
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• 2024

Industry 4.0 has brought about significant changes in the roles of workers through the introduction of innovative technologies. In smart factory environments, workers are required to interact seamlessly with robots and automated systems, often utilizing equipment enhanced by Virtual Reality (VR) and Augmented Reality (AR) technologies. This study aims to systematically analyze recent research literature on monitoring the physical and mental states of workers in Industry 4.0 environments. Relevant literature was collected using the Web of Science database, employing a comprehensive keyword search strategy involving terms related to Industry 4.0 and health monitoring. The initial search yielded 1,708 documents, which were refined to 923 journal articles. The analysis was conducted using VOSviewer, a tool for visualizing bibliometric data. The study identified general trends in the publication years, countries of authors, and research fields. Keywords were clustered into four main areas: 'Industry 4.0', 'Internet of Things', 'Machine Learning', and 'Monitoring'. The findings highlight that research on health monitoring of workers in Industry 4.0 is still emerging, with most studies focusing on using wearable devices to monitor mental and physical stress and risks. This study provides a foundational overview of the current state of research on health monitoring in Industry 4.0, emphasizing the need for continued exploration in this critical area to enhance worker well-being and productivity.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.