• Journal of The Korean Association For Science Education
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• v.13 no.2
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• pp.210-218
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• 1993

Scientific literacy for all became a new goal and introducing STS issues into science curriculum shows new trends in science education. The educational importance of natural environment of a region is increasing because it can serve as a vehicle to meet the new goal and trends of science education. The opportunity to learn about Korean natural environment in schools was investigated. The characteristics and unique patterns of Korean natural environment are not well reflected in the intended curriculum. School teachers mostly believe that education of Korean natural environment will greatly contribute to the students' learning of the subject matter they teach and environmental problems. However, they have limited opportunity to teach Korean natural environment. Various elective courses dealing with our natural environment should be provided in schools, especially for non-science majors. Out-of-school activities and facilities for Korean natural environment should be made available.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.557-562
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• 2020

In the non-face-to-face learning environment forced into effect by the COVID-19 pandemic, online learning is being adopted as a major educational technique. Given the lack of research on how online learning patterns affect academic performance, this study focuses on the number and duration of online video learning sessions as a major factor based on midterm and final exams, and with a formative assessment for each type of learning. The correlation of the learning effects was analyzed. The analysis focused on computer programming subjects, which are among the most difficult liberal arts subjects for arts and science students at the university level. The analysis of cases of actual students showed no correlation among weekly formative assessments, the number of learning sessions, and the learning duration. On the other hand, the number of learning sessions (r=.39 p<0.05) and learning duration (r=.42 p<0.05) were correlated with the midterm and final exams. Elements, such as SMS text, bulletin board, and e-mail, were excluded from the analysis because not all students have access to them. Therefore, the results can be improved if future analysis of the students' learning patterns in a non-face-to-face lecture environment is performed considering more factors/elements and the learners' needs.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.129-137
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• 2017

The purpose of the study is to investigate the convergence influence of excessive smartphone use on attention deficit, learning environment, and academic procrastination in health college students. A self-reported questionnaire was completed by 255 college students in Busan drom March 6 to June 12, 2017. The degree of smartphone overuse, lack of attention, learning environment, and academic procrastination according to smartphone use characteristics showed significant effects on the time spent on smartphones per day, awareness of smartphone addiction, and personal use of smartphones during class time (p<0.001). It was shown that smartphone overuse was positively correlated with attention deficit (r=0.870, p<0.01), learning environment (r=0.812, p<0.01), academic procrastination (r=0.772, p<0.01), and attention deficit showed a positive relationship with learning environment (r=0.918, p<0.01) and academic procrastination (r=0.798, p<0.01) Learning environment was positively correlated with academic procrastination (r=0.777, p<0.01). The influence factors of smartphone overuse were attention deficit (p<0.001), followed by academic delay behavior (p<0.01). It is necessary to establish a healthy learning environment through prevention and proper use of smartphone.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.4
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• pp.474-484
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• 2024

Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We developed a simulation environment involving a cooperative robot transportation task. Experimental results show that successful tasks are achieved when SISR is applied, while tasks fail when SISR is not applied. We also analyzed how the amount of state information collected by the agents changes with the application of SISR.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.216-222
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• 2021

Advances in technology have given educators a tool to empower them to assist with developing the best possible human resources. Teachers at universities prefer to use more modern technological advances to help them educate their students. This opens up a necessity to research the capabilities of cloud-based learning services so that educational solutions can be found among the available options. Based on that, this essay looks at models and levels of deployment for the e-learning cloud architecture in the education system. A project involving educators explores whether gement Systems (LMS) can function well in a collaborative remote learning environment. The study was performed on how Blackboard was being used by a public institution and included research on cloud computing. This test examined how Blackboard Learn performs as a teaching tool and featured 60 participants. It is evident from the completed research that computers are beneficial to student education, especially in improving how schools administer lessons. Convenient tools for processing educational content are included as well as effective organizational strategies for educational processes and better ways to monitor and manage knowledge. In addition, this project's conclusions help highlight the advantages of rolling out cloud-based e-learning in higher educational institutions, which are responsible for creating the integrated educational product. The study showed that a shift to cloud computing can bring progress to educational material and substantial improvement to student academic outcomes, which is related to the increased use of better learning tools and methods.

• Korean Medical Education Review
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• v.16 no.3
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• pp.132-140
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• 2014

Higher education is under unprecedented pressure for quality improvement and cost containment/reduction due to global competition and ever-increasing tuition costs. These twin challenges require an unconventional approach, and massive open online courses (MOOCs) and flipped learning have recently emerged as two promising educational alternatives not only to address the current problems but also to direct the future of education. This paper discusses the rapidly changing environment for education, MOOCs, and flipped learning as learning alternatives, the relationship between MOOCs and flipped learning, and course redesign for the implementation of flipped learning. The case of Ulsan National Institute of Science and Technology (UNIST) is also discussed for benchmarking purposes since it has been pioneering an innovative educational methodology for teaching and learning IT-enabled active learning methods from its inception in 2009. It has redesigned almost 70 courses (20% of all the courses to offer) for flipped learning. The objectives of UNIST's educational experiment are three-fold: improving the quality of education for students, improving teaching productivity for the faculty, and containing/reducing education costs for the university.

• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.250-261
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• 2000

In this study, a concept learning strategy emphasizing social consensus during discussion (SCS) was developed. The instructional effects of this strategy were compared with those of cognitive conflict strategy (CCS) and traditional instruction in the aspects of students' achievement, conceptions, communication apprehension, perceptions of science learning environment, and perceptions of small group discussion. There were no significant differences in the scores of an achievement test. For the students of low communication competency, however, the scores of the CCS group were significantly higher than those of the traditional group. The adjusted mean of the SCS group was higher than those of the other groups in a conceptions test. The social consensus strategy was also found to be more effective in learning concept for those who were more competent in communicating. No significant differences were found in the communication apprehension. The scores of three groups did not differ significantly in the subcategories of 'personal relevance' and 'students' negotiation' of the test of the perceptions of science learning environment. However, the students in the SCS group scored higher in 'participation'. The students in the SCS group perceived small group discussions more positively.

• KIEAE Journal
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• v.17 no.5
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• pp.95-100
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• 2017

Purpose: The purpose of this study is to investigate the prior art based on deep learning to objectively calculate the metabolic rate which is the subjective factor for the PMV optimum control and to make a plan for future research based on this study. Methods: For this purpose, the theoretical and technical review and applicability analysis were conducted through various documents and data both in domestic and foreign. Results: As a result of the prior art research, the machine learning model of artificial neural network and deep learning has been used in various fields such as speech recognition, scene recognition, and image restoration. As a representative case, OpenCV Background Subtraction is a technique to separate backgrounds from objects or people. PASCAL VOC and ILSVRC are surveyed as representative technologies that can recognize people, objects, and backgrounds. Based on the results of previous researches on deep learning based on metabolic rate for occupational metabolic rate, it was found out that basic technology applicable to occupational metabolic rate calculation technology to be developed in future researches. It is considered that the study on the development of the activity quantity calculation model with high accuracy will be done.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.