• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.35-42
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• 2022

The article identifies the problem of monitoring the quality of higher education in three main areas, which are comparative pedagogical systems of education. The first direction is determined by dissertation works, the second - monographs and textbooks, and the third reveals scientific periodicals. According to its internal structure, monitoring the quality of education combines important management components identified in the article (analysis, evaluation and forecasting of processes in education; a set of methods for tracking processes in education; collecting and processing information to prepare recommendations for research processes and make necessary adjustments). Depending on the objectives, three areas of monitoring are identified: informational (involves the accumulation, structuring and dissemination of information), basic (aimed at identifying new problems and threats before they are realized at the management level), problematic (clarification of patterns, processes, hazards, those problems that are known and significant from the point of view of management). According to its internal structure, monitoring the quality of education combines the following important management components: analysis, evaluation and forecasting of processes in education; a set of techniques for tracking processes in education; collection and processing of information in order to prepare recommendations for the development of the studied processes and make the necessary adjustments. One of the priorities of the higher education modernization program during the COVID-19 pandemic is distance learning, which is possible due to the existence of information and educational technologies and communication systems, especially for effective education and its monitoring in higher education. The conditions under which the effectiveness of pedagogical support of monitoring activities in the process of distance learning is achieved are highlighted. According to the results of the survey, the problems faced by higher education seekers are revealed. A survey of students was conducted, which had a certain level of subjectivity in personal assessments, but the sample was quite representative.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.656-683
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• 2022

The COVID-19 pandemic has profoundly affected the world, inflicting nationwide lockdowns interrupting conventional schooling through schools, colleges and universities. Educational institutions are struggling to maintain learning continuity through remote learning solutions. Still, the students' perception of this 'new normal' mode and pace of learning needs to be examined to ensure the success of these efforts. This study aimed at examining the perception of higher education students in India and Croatia especially with respect to the association between cultural orientation and the e-learning. The period considered for the data collection was from March 2020 to September 2020. Correspondence analysis was attempted to create spatial maps to depict the respondent choices. Students from both the regions agreed to the high-power distance that existed in their cultures and considered the role of device and content to be an important dimension of e-learning for it to be effective, but the results also pointed out some differences in their choices on other culture dimensions as well as factors affecting e-learning which make this study unique and suggest in-depth future research for conclusive results.

• Journal of Astronomy and Space Sciences
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• v.41 no.2
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• pp.61-78
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• 2024

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1838-1844
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• 2010

In distance education, contents are to convey what to learn to learners, and the efficient quality assurance of contents is the very first step to the enhancement of distance education. Most studies of the quality assurance of contents have mostly centered around the development of evaluation tools, and few studies have ever focused on analysis of the quality of contents itself, since it's not easy to do that due to difficulties in the selection of evaluatees or of contents to be analyzed. The purpose of this study was to analyze the quality of 58 distance education contents of on-the-job training and another training for the acquisition of qualifications. As a result, the contents of the learning contents segment ranked first. Among the components of each segment, there was room for improvement in the level of learning and learning elements in the learning contents segment. In terms of instructional design, the quality of interaction components should be taken to another level to boost the quality of contents in this segment. The findings of the study are expected to give some suggestions about which parts of contents should be improved in quality from a perspective of contents developers or suppliers to enhance the overall quality of contents.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Journal of Korean Academy of Nursing
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• v.40 no.2
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• pp.182-190
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• 2010

Purpose: This study was done to identify self-directed learning readiness, achievement goal orientations, learning satisfaction and learning achievement, and to evaluate the factors affecting learning achievement for nursing students using a web-based Health Assessment e-Book. Methods: The research design was a cross-sectional study with a structured questionnaire and data were collected before using the web-based Health Assessment e-Book and 1 week after finishing. The participants were 80 nursing students who were taking the Health Assessment class from March to June 2009. Results: Mean score for subjective learning achievement was 31.26 and for objective learning achievement, 69.25. Subjective and objective learning achievement were positively correlated with self-directed learning readiness, mastery goal, attitude toward distance education, and learning satisfaction. In subjective learning achievement, learning satisfaction and mastery goal were significant predictive factors and explained 64% of the variance. Objective learning achievement was significantly predicted by learning satisfaction and self-directed learning readiness, which explained 24% of the variance. Conclusion: Learning satisfaction, mastery goal and self-directed learning readiness were found to be very important factors associated with learning achievement for nursing students using a web-based Health Assessment e-Book. To provide high quality and effective web-based courses and to improve nursing students' learning achievement and learning satisfaction, educators should consider the learner's characteristics from the initial stages of lecture planning.

• Journal of The Korean Association For Science Education
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• v.33 no.3
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• pp.679-693
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• 2013

The purpose of this study is to develop an inventory to classify task commitment types of science learning and to classify highschool students' task commitment types. Firstly, inventory questions were designed following the literature analysis on the task commitment components which involve self confidence, high goal setting, and focused attention. Prototype inventory underwent the content validity test, pilot test, and reliability test. Through these steps, final inventory was input to 462 high school students and underwent the factor analysis and cluster analysis. Factor analysis confirmed three components of task commitment as the three factors of inventory questions. In order to find how many clusters exist, factors of developed inventory became new variables. Each factor's factor mean was calculated and served as the new variable of the cluster analysis. Cluster analysis extracted five clusters as task commitment types. The 5 clusters were suggested by the agglomarative schedule and dendrogram gained from a hierarchical cluster analysis with the setting of the Ward algorithm and Squared Euclidean distance. Based on the factor mean score, traits of each cluster could be drawn out. Inventory developed by this study is expected to be used to identify student commitment types and assess the effectiveness of task commitment enhancement programs.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.127-134
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• 2024

The article highlights the problems of the digitalization of the educational process, which affect the pedagogical cluster and are of a psychological nature. The authors investigate the transformational changes in education in general and the individual beliefs of each subject of the educational process, caused by both the change in the format of learning (distance, mixed), and the use of new technologies (digital, communication). The purpose of the article is to identify the strategic trend of the educational process, which is a synergistic combination of pedagogical methodology and psychological practice and avoiding dialectical opposition of these components of the educational space. At the same time, it should be noted that the introduction of digital technologies in the educational process allows for short-term difficulties, which is a usual phenomenon for innovations in the educational sphere. Consequently, there is a need to differentiate the fundamental problems and temporary shortcomings that are inherent in the new format of learning (pedagogical features). Based on the awareness of this classification, it is necessary to develop psychological techniques that will prevent a negative reaction to the new models of learning and contribute to a painless moral and spiritual adaptation to the realities of the present (psychological characteristics). The methods used in the study are divided into two main groups: general-scientific, which investigates the pedagogical component (synergetic, analysis, structural and typological methods), and general-scientific, which are characterized by psychological direction (dialectics, observation, and comparative analysis). With the help of methods disclosed psychological and pedagogical features of the process of digitalization of education in a mixed learning environment. The result of the study is to develop and carry out methodological constants that will contribute to the synergy for the new pedagogical components (digital technology) and the psychological disposition to their proper use (awareness of the effectiveness of new technologies). So, the digitalization of education has demonstrated its relevance and effectiveness in the pedagogical dimension in the organization of blended and distance learning under the constraints of the COVID-19 pandemic. The task of the psychological cluster is to substantiate the positive aspects of the digitalization of the educational process.

• International Journal of Contents
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• v.17 no.3
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.