• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.133-139
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• 2024

In response to the challenges posed by a super-aged society, local authorities are conducting educational programs on smartphone usage tailored for the elderly. However, obstacles such as the limitations of one-to-many education and suboptimal learning outcomes for the elderly have hindered the efficacy of smartphone education. This study suggests an educational service intended for direct application in offline settings, considering the identified problems. Through the utilization of generative AI, the proposed app identifies specific challenges encountered by users during actual smartphone use, offering personalized exercises to facilitate customized and repetitive learning experiences for individual users. When integrated with existing local government education initiatives, this app is anticipated to enhance the efficiency of smartphone education by providing personalized, one-on-one training that is efficient in terms of time and content.

• Journal of Internet Computing and Services
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• v.3 no.3
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• pp.19-29
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• 2002

Recently, cyber evaluation systems on the Web-based remote education do not consider the personalized characteristic and propensity of individual students. Especially, in setting of the questions far examination, the traditional simple and general methods for all students group have been used for evaluation. This paper proposes on efficient cyber evaluation system using user profile. First, questions are filtered by using user profile for the personalized characteristic and propensity of individual students, This personalized characteristic and propensity have been disregarded in traditional evaluation systems. And then, filtered questions are set for examination, Therefore, efficiency of the evaluation system is enhanced and students make good results from their study. When user profile is adapted, the setting method of question for examination have combined category-based method with keyword-based method. This make students get the interest and pleasure for questions.

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.117-127
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• 2005

Numerous internet pop advertisement are being provided according to the rapid development of e-commercial and a rise in users. However, it has not been based on analysis of users' inclination but just one-sided providing. With that reason, many web-site provider want to advertis e more efficient and distinguished Internet-advertisement as analyzing Server's Log accessed. In this thesis, we have studied and tested relatively simply adoption system to provide personalized advertisement service. In order to influence personal disposition to system as the most effective way, it first of all uses History files as source data and after refining it, it can search not only visitors' inclination but also the others' visit-list on the other server. As a result of it, it can make advertisement more reality and activity.

• The Journal of Korean Association of Computer Education
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• v.14 no.1
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• pp.91-109
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• 2011

This study describes the design and development of personalized e-Tutor to facilitate e-Learning. An e-Tutor was developed to provide personalized cognitive, emotional, and social learning supports automatically and integrated into an e-learning system. Fourteen learning supports selected in this study were consisted of eight cognitive, three emotional, and three social factors respectively. Participants were 202 adult learners in corporate training environments. The result indicated that e-Tutor was perceived useful in general. Amongst three, cognitive support was perceived as the most useful. Emotional and social supports of encouraging learners and facilitating interactions among learners were also reported useful to facilitate the desired learning outcomes.

• The Journal of Korean Association of Computer Education
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• v.12 no.3
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• pp.41-51
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• 2009

In this thesis, we suggested an e-learning model, which is named 'goal driven personalized e-learning system' to improve educational effects, and implemented it. The system makes the learner choose the learning goal which could be a motivational power for learning, so it enabled self-directed learning. In order to implement the system, we proposed new standards related to personalization by modifying SCORM 2004 standard. New standards stand for the statistics on learning objects usage, a goal for driving learning. and information of the contents model and the sequencing information model, which are parts of the system previously suggested. We implemented the system, and then proved that personalize e-learning is possible by showing that the system could offer a learning path individually to learners who have different characteristics.

• Smart Media Journal
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• v.7 no.2
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• pp.47-53
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• 2018

Along with the development of information and communication technology, smart education that can learn without restrictions of time, place and equipment is activated even in the field of education. Although smart education is provided with content-based training solutions, construction of a system that grasps individual characteristics of learners and provides personalized learning is relatively weak. The activity of free choice is an important play activity of early childhood education, but it is not implemented efficiently by relying on the clinical observation of the teacher. If the IoT(Internet of Things) technology based on Hyper-Connected is applied to free-choice activities, it is possible to provide the child's personalized activity type and play-form analysis based on objective and stylized data. In this paper, we design and implement a system to monitor the child's activity of free choice by building an IoT environment that is based on open source hardware. The proposed system provides children's activity information as objective data and will be used as teacher's work mitigation and custom training material for each child.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.85-94
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• 2005

Today the web-based learning management information systems are developed as forms of many ordered learning information systems acting up to the personal characteristics, but the previous systems have difficulty in their mutual working, maintaining, and repairing between the systems, because of their one-sided "push" method or reuse of the contents. Also they were not managed together having the dissimilar learning management systems each. Therefore, I made up for the weak points of the previous systems and embodied the international standard SCORM-based ordered learning management information systems. After adapting to sequencing contents, all systems are supplied; giving lectures, solving the problems, evaluating the learners and the function of successive personalization learning based on the result of the learner evaluation systems.

• The Korean Journal of Food & Health Convergence
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• v.9 no.4
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• pp.19-23
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• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.