• The Journal of Korean Academic Society of Nursing Education
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• v.2
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• pp.75-94
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• 1996

This survey study was aimed at identifying the degree of educational need of the KNOU(Korea National Open University) nursing students defined as admission purposes, satisfaction of distance learning education, learning methods, and courses after graduation. Among randomly assigned 1000 students, 320 KNOU nursing students who allowed to participate in the study completed the questionnaires. The data were analyzed using descriptive statistics, chi-square test, and t-test, Results of this study were as follows : 1. The admission purposes of the KNOU nursing students were 'in order to get a bachelor's degree(83.8%)', 'to acquire knowledge for task(61.3%)', or 'to be admitted for the graduate school (53.1%)' etc. Comparing the admission purposes by age, tow items- 'to explore new possibilities for myself' and 'excellent curriculum' showed statistically significant differences. These two items were also found to show significant differences by marital status. 2. For the media maintenance, the results showed that students use their own cassett radios(96.3%), VTR(49.4%), TV only for the study (44.1%), personal computer (3.31%), or joining Hitel (6.3%). 3. Listening rates of the radio lecture were 'over 80%(9.1%)', '50-80%(9.1%)', '20-50%(18.1%)', 'below 20%(30%)' and 'never(33.1%)', And record lecture showed listening rates as follows : 'over80%(17.2%)', '50-80%(15.9%)', '20-50%(24.4%)', 'below 20%(27,2%)' and 'never(14.4%)'. 4. The difficulties with KNOU life were 'listening radio lectures(38.8%)', studying by following teaching schedules (37.8%)', 'isolated self-study(10.3%)', and 'appearance in the attending classes(8.1%)'. 5. As for satisfaction with teaching methods, the data showed that 81.2% of the respondents were satisfied (or very satisfied) with 'attending classes' and 75%, with 'paper lectures'. On the other hand some of respondents were very dissatisfied with 'recorded lecture(12.8%)' and 'radio lecture(10.9%)' 6. The results also showed that the students want to have 'video conferencing lecture(77.2%)', 'cable TV(64.1%)' and 'CD ROM program' to improve learning effects. 7. Concerning learning attitudes, 48.8% of the students reported 'study mainly for examnination', and only 4.1% answered 'study every day with plan'. The learning attitude showed significant differences by marital status and age. The students also evaluated themelves as 'study very hard(5.9%)', 'study hard in general(41.6%)', 'study a little(40.3%)' and 'study little(11.9%)'. 8. The students responded the most effective learning material was the 'textbook (92.2%)'. 9. For the purposes of using the local center, the results showed 'for the attending classes(76.3%)', 'for the use of references(14.7%)', and 'for the study group(66.7%)'. 10. The results revealed that 20.3% of the respondents had ever experienced unregistration or temporary withdrawal, and 53.4% among them did not register more than one time. The most common reason for the unregistration was 'due to family affairs or their job (70.8%)'. 11. 88.1% of the respondents answered 'they will graduate without fail'. 12. Regarding the benefits from the KNOU graduation, respondents indicated 'graduate school admission(38.1%)', 'self-confidence in social life(17.5%)', and understanding social problems (10.9%)'. 13. 64.4% of the students showed that they have intention to enter the graduate school. The item 'changing work place' showed statistically significant differences by marital status and age.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.415-424
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• 2009

As one of the unsupervised learning neural network methods, self-organizing maps(SOM) are applied to various fields. It reduces the dimension of multidimensional data by representing observations on the low dimensional manifold. On the other hand, the minimal spanning tree(MST) of a graph that achieves the most economic subset of edges connecting all components by a single open loop. In this study, we apply the MST technique to SOM with subnodes. We propose SOM's with embedded MST and a distance measure for optimum choice of the size and shape of the map. We demonstrate the method with Fisher's Iris data and a real gene expression data. Simulated data sets are also analyzed to check the validity of the proposed method.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.300-312
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• 2019

The purpose of this study is to develop an astronomical observing education program that enables high school students to inquire the distance of astronomical bodies based on the research methods (observing open clusters and exploring collected big data) using small telescopes and DSLR cameras. After analyzing the 2015 revised science curriculum, we developed science club activity materials and teacher-student learning contents suitable for high school earth science education. A panel of six teachers and researchers of earth science education and astronomy, participated in developing the educational materials. The validity of the program was verified through establishing the agreement among the panels after in-depth discussions and clarifications. The program, developed with 10 lessons in total, showed high satisfactory content validity (CVI, .89) and conformity of school class (Likert's 5 point scales, 4.17). The feedback of the panels and the Delphi analysis continued to improve the quality of the program. The pilot testing result with high school students (N=9) showed that the students' satisfaction rate was high as 4.48. Using the astronomical observational education program of this study is expected to contribute in improving the convergence educational activity, interest, curiosity, and inquiry ability of students in the universe and the astronomical bodies.

• Journal of Christian Education in Korea
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• v.64
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• pp.203-235
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• 2020

The purpose of this study is to identify the degree of social presence perceived by students and to explore the factors that have affected it after practicing Christian Education classes as synchronous distance course due to Covid-19. It is also to suggest effective ways in the aspects of the design and operation to improve social presence. In order to measure social presence and derive influencing factors, research related to synchronous distance class and social presence is summarized through literature review. The researchers also surveyed 58 students in three courses of Christian education major at a University in Gyeonggi-do and conducted in-depth interviews with 6 students. The main findings are as follows: First, the sense of social presence was moderate, the emotional bond was the lowest by sub-factor, the open communication, the sense of community was moderate, and the mutual support and concentration were the highest. Second, factors that had a positive impact on the sense of social reality were group activities, selfintroduction activities, active participation in classes, mutual communication such as Q & A or response to peer learners' opinions during lectures by professors, questions, feedback, etc, and having a smaller number of students. Factors that had a negative impact on the perception of social presence were lack of private conversations, poor participation in classes, lack of communication with each other, and difficulty concentrating. The causes of these negative factors were technical problems and limitations arising from zoom, inconvenience and distracting surroundings, lack of time, and psychological awkwardness. Reflecting the results of the study, orientation to effective synchronous distance course, guidance on smooth communication methods, strengthening the role of professors to promote learning, strengthening group activities and learner-centered activities, and proposing a smaller scale of students were ways that are offered to improve the sense of social presence in synchronous distance courses.

• Journal of the Korean Society for information Management
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• v.26 no.2
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• pp.65-82
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• 2009

This exploratory study describes the social bookmarking perceptions and behaviors of students in university courses. Although an emerging discussion regarding the value of social bookmarking tools exists, how users adopt tools in practice is not well known. Students were asked to utilize the bookmarking tool del.icio.us to store information relating to course projects. They were also asked to comment how they employed del.icio.us for course projects. The study analyzed student perceptions and behaviors when using social bookmarking tools for university coursework. The study noted that the use of tags, notes, and networking within these social bookmarking tools remained less active and social bookmarking services in Web 2.0 as shared collaboration, shared communities, and vertical search were less present. Utilizing social bookmarking tools to facilitate personal information management includes the activities of information use, information re-use, and mobility.

• Smart Media Journal
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• v.5 no.3
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• pp.54-61
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• 2016

According to many smart devices are development, SNS(Social Network Service) users are getting higher that is possible for real-time communicating, information sharing without limitations in distance and space. Nowadays, SNS users that based on communication and relationships, are getting uses SNS for information sharing. In this paper, we used the SNS posts for users to extract the category and information provider, how to following of recommend method. Particularly, this paper focuses on classifying the words in the text of the posts and measures the frequency using Inception-v3 model, which is one of the machine learning technique -CNN(Convolutional Neural Network) we classified image word. By classifying the category of a word in a text and image, that based on DMOZ to build the information provider DB. Comparing user categories classified in categories and posts from information provider DB. If the category is matched by measuring the degree of similarity to the information providers is classified in the category, we suggest that how to recommend method of the most similar information providers account.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.444-455
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• 2013

The purpose of this study is to explore the selection attributes for parents of children with language disability when choosing a clinic. The data collection was carried out in 3 steps: the preliminary survey, first open survey and second survey in AHP(Analytic Hierarchy Process). The subjects of were 252 in total. The results were as follows: First, The order of priority attributes in superior categories for parents of children with language disability when selecting a clinic were 'therapist-related attributes', 'program-related attributes' and 'physical-related attributes' in turn. The top 5 priority attributes in subcategories were 'therapist's academic background and major', 'ability to make a rapport', 'clinical experience and qualification of therapist', 'kindness and confidence' and 'counseling program for parents'. Second, The parents of preschoolers age 6 and younger chose 'clinical experience and qualification of therapist', 'counseling program for parents' and 'learning materials' for the most priority attributes, whereas the parents of students age from 7 to 12, considered 'therapist's academic background and major', 'clinical fee' and 'distance transport parking' more importantly to select a clinic. The results of this study provided preliminary data for successful planning of speech and language therapy.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.697-702
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• 2023

Recently, with the introduction of drones, a core technology of the 4th industrial revolution, various convergence education using drones is being conducted in school education sites. In particular, drone theory and practice education is being conducted in connection with free semester classes and career exploration. The drone convergence education program has higher learner satisfaction than simple demonstration and practice education, and the learning effect is high due to direct practical experience. However, since practical education is being conducted for a large number of learners, it is impossible to restrict and control the flight of a large number of drones in a limited place. In this paper, we propose a monitoring system that allows the instructor to monitor multiple drones in real time and learners to recognize collisions between drones in advance when multiple drones are operated, focusing on education operated in schools. The communication module used in the experiment was equipped with GPS in Murata LoRa, and the server and client were configured to enable monitoring based on the location data received in real time. The performance of the proposed system was evaluated in an open space, and it was confirmed that the communication signal was good up to a distance of about 120m. In other words, it was confirmed that 25 educational drones can be controlled within a range of 240m and the instructor can monitor them.