• Journal of the Korean Society of Earth Science Education
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• v.8 no.2
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• pp.193-205
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• 2015

In this study, a Virtual geological Field Trip(VFT) learning program using 3D panorama virtual reality techniques was developed to learn about the Gongju city 7 area located in Chungcheongnam-do, Korea. The developed $360^{\circ}$ 3D VFT program can show every face of observational points and interact as zoom-in, zoom-out and image rotation. For the educational effects of the materials, it is provided with a compass, a protractor, enlarged images, pop-up windows, etc.. The program was applied to the class of 35 gifted students in middle school to investigate the effectiveness of the program. The results showed that positive responses of the students were 90% or more. When geological field trip problems like cost, safety, distance occur in geological learning procedure of middle school science, this VFT program can become as a supplementary learning material and a solution.

• Earthquakes and Structures
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• v.22 no.4
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• pp.401-419
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• 2022

A novel hybrid extreme machine learning-multiverse optimizer (ELM-MVO) framework is proposed to predict the liquefaction phenomenon in seismically excited tunnel lining inside the sand lens. The MVO is applied to optimize the input weights and biases of the ELM algorithm to improve its efficiency. The tunnel located inside the liquefied sand lens is also evaluated under various near- and far-field earthquakes. The results demonstrate the superiority of the proposed method to predict the liquefaction event against the conventional extreme machine learning (ELM) and artificial neural network (ANN) algorithms. The outcomes also indicate that the possibility of liquefaction in sand lenses under far-field seismic excitations is much less than the near-field excitations, even with a small magnitude. Hence, tunnels designed in geographical areas where seismic excitations are more likely to be generated in the near area should be specially prepared. The sand lens around the tunnel also has larger settlements due to liquefaction.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.85-92
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• 1997

The field is a good learning environment that offers many opportunities. Status of conducting field trip in the middle and high school was investigated As a result, there was few teachers who disagree that a course in science should include learning in the field. Yet, on practical side, teachers who used to conduct the field trip over once a year in science class appeared to be about 23% only. Most of schools conducting field trips hadn't well-planned program for instructional activities. Many of teachers appealed that they should not get release time for conducting an extended field experiences, and that they could not obtained good information in relation to the contents, the methods and strategies of field activities. It says that a good field trip needs administrative and financial surport, many informations, and the teacher's exertion.

• Journal of Engineering Education Research
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• v.20 no.3
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• pp.25-31
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• 2017

As the world enters the era of the Fourth Industrial Revolution, which is represented by advanced technology, it not only changes the industrial field but also the education field. In recent years, Smart Learning has enriched learning by using diverse forms and technologies that utilize vast amount of information about learners' individual knowledge through the emergence of realistic and intelligent contents that combine high technology such as artificial intelligence, big data and virtual reality and there is an increasing interest in intelligent adaptive learning, which can customize individual education. Therefore, the purpose of this study is to explore intelligent adaptive learning method through recent smart education environment, beyond traditional writing-based communication education which is highly dependent on the competency of instructors. In this study, we analyzed the various learner information collected in the communication course and constructed a concrete teaching and learning method of intelligent adaptive learning based on the instructor's intended smart contents. The result of this study is expected to be the basis of highly personalized teaching and learning method of digital method in communication education which is emphasized in the fourth industrial revolution era.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.271-281
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• 2010

In an industrial Era, OJT(On-the-Job Training) has been accepted as the field learning. But in a breaking up era, traditional field training needs to change and make an evolutionary model. Also, we need to make evolutionary model for various changing ways and means and need means to maximize the transformation of learning by operating learning organization. In knowledge based society, as people work and learn new knowledge in order to pass the experience knowledge and capabilities, they are not the traditional relationship between trainer and trainee but maximize work and learning, development and performance through several different ways. So, the study about new learning model is needed because the learning is creating the value and makes low cost and high efficiency about the elements of cost and time. We study the evolutionary model, OJF(On-the-Job Facilitating) - new learning methodology - through operating learning organization in S Electronics and its application practices.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• 대한공업교육학회지
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• v.31 no.2
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• pp.332-349
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• 2006

The purpose of this study is to identify the relationship between the University students' learning styles in an e-learning according to the major field and learning styles in an e-learning according to the psychological types. The results of the research are as follows: First, The Major field made no difference to the learning styles in e-learning. E-learning styles of University student are as follows : the environment-dependent and self-directed learning style, positive and cooperative learning style, environment-independent and self-directed learning style. Second, In comparison with the boys and girl's the learning styles in e-learning, there were individual differences. But the environment-dependent and self-taught learning style was the highest learning style in Boys and Girl's but, especially there was more high in Girl's. Third, Amount to use internet made no difference to the learning styles in e-learning. The difference of learning styles in an e-learning environment according to Extraversion-Introversion, Sensing-Intuition, Thinking-Feeling among Psychological Types were statistically meaningless.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.1-9
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• 2021

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.430-445
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• 2022

The purpose of this study was to explore the learning effects in elementary school students who participated in a geological field trip conducted under the theme 'minerals and rocks', focusing on novelty space. A total of 10 sixth-grade students participated in this program held at a public elementary school in Seoul as part of after-school club activities. Students observed mineral and rock samples in a classroom and outdoor learning environment. The authors collected activity papers (texts, drawing), researchers' participation notes, video and audio recordings containing the study participants' activities, and post-interview data To analyze the learning effects in the cognitive domain of students, the observation analysis framework for rock classification of Remmen and Frøyland (2020) and the rock description analysis framework of Oh (2020) were used. Additionally, to explore the learning effects of psychological and geographic areas, students' drawings, texts, discourses, and interview data were inductively analyzed. The results showed that the students demonstrated 'everyday' and 'transitional' observations in the classroom learning environment, while in the outdoor learning environment (school playground, community-based activities), they demonstrated 'transitional' and 'scientific' observations. Moreover, as the scientific observation stage progressed, more types of descriptive words for rocks were used. In terms of psychological and geographic aspects, students showed their selection of places to explore familiar outdoor learning environments, positive perceptions of outdoor learning, and aesthetic appreciation. Finally, this study not only discussed novelty space as a tool for analyzing students' learning effects but also suggested the need for an academic approach considering new learning environments, such as learning through virtual field trips.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.217-227
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• 1996

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performin this specific task. In a previous work［6］, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification ad control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.