• Journal of The Korean Association For Science Education
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• v.35 no.4
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• pp.691-707
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• 2015

This study aims to develop biology teachers' education program based on argumentation activity about core concepts of evolution and to analyze the characteristics of core concepts of evolution learned during the program. The eight core concepts of evolution in this study were variation, heritability of variation, competition, natural selection, adaptation, differential reproductive rate of individuals, changes in genetic pool within a population, and macroevolution. The performances of teachers participating in the program were compared before and after argumentation activities; consisting of seven sessions on the eight core concepts of evolution. The process of the program was specially designed by learning cycle model for teacher education, consisting of seven phases: identification of the task, production of a tentative argument, small group's written argument, share arguments with the other groups, reflective discussion, final written argument, and organization by an instructor. Participants in the study were two pre-service biology teachers and four in-service biology teachers. The results suggest that biology teachers reduced the teleological explanation for biological evolution and improve its adequacy after the intervention. Teachers lacked the opportunity to discuss variation, heritability of variation, competition, and macroevolution because science textbooks lack information on the concepts of biological evolution. The results of this study suggest that because the argumentation program developed for teachers helps to improve understanding the concepts of evolution and to reduce inadequate conceptions in biology, teacher education programs using argumentation activity and eight core concepts of evolution will play a role for efficient evolution education for biology teachers.

• Korean Educational Research Journal
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• v.37 no.1
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• pp.67-101
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• 2016

The purpose of this study is to explore and propose policy alternatives by examining students' educational experiences at Chungbuk National University (CBNU). Some of the outstanding research findings of the study as follows. Students at CBNU think that they are capable of team-working and utilizing internet while less so with foreign language skill. With regard to academic achievement, students responded that they have seen a relatively low outcome in foreign language competency. In terms of their job placement, CBNU students highly recognize their logical thinking skill, creativity, and activity, whereas lower satisfaction with social service, study abroad, and internship experience. For further development of the survey analysis, this study suggests additional items included to make a sophisticated analysis possible such as scholarship, part-time job, educational outcome. This is expected to allow researchers to tab into the effect of finance of CBNU students. More detailed information on students' characteristics also need to be added; collaborative learning, student faculty interaction, co-work with students from diverse background, etc., which would allow the analysis of the impact of extra-curricula activities.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Management & Information Systems Review
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• v.39 no.3
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• pp.1-16
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• 2020

The current tariff return system requires tax officials to calculate tax amount by themselves and pay the tax amount on their own responsibility. In other words, in principle, the duty and responsibility of reporting payment system are imposed only on the taxee who is required to calculate and pay the tax accurately. In case the tax payment system fails to fulfill the duty and responsibility, the additional tax is imposed on the taxee by collecting the tax shortfall and imposing the tax deduction on For this reason, item classifications, together with tariff assessments, are the most difficult and could pose a significant risk to entities if they are misclassified. For this reason, import reports are consigned to customs officials, who are customs experts, while paying a substantial fee. The purpose of this study is to classify HS items to be reported upon import declaration and to indicate HS codes to be recorded on import declaration. HS items were classified using the attached image in the case of item classification based on the case of the classification of items by the Korea Customs Service for classification of HS items. For image classification, CNN was used as a deep learning algorithm commonly used for image recognition and Vgg16, Vgg19, ResNet50 and Inception-V3 models were used among CNN models. To improve classification accuracy, two datasets were created. Dataset1 selected five types with the most HS code images, and Dataset2 was tested by dividing them into five types with 87 Chapter, the most among HS code 2 units. The classification accuracy was highest when HS item classification was performed by learning with dual database2, the corresponding model was Inception-V3, and the ResNet50 had the lowest classification accuracy. The study identified the possibility of HS item classification based on the first item image registered in the item classification determination case, and the second point of this study is that HS item classification, which has not been attempted before, was attempted through the CNN model.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.55-69
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• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• Journal of Fashion Business
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• v.19 no.6
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• pp.82-100
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• 2015

Mobile fashion apps present much opportunity for marketers to engage consumers, however not all apps provide enough functions for their targeted audience. This study aims to determine how mobile fashion apps can be used to build brand community with consumer engagement. Qualitative data on fashion mobile apps were collected from the Apple app store and Android market during the spring and summer of 2015. A total of 110 fashion mobile apps were collected;, 50 apps were identified as apparel brands that either manufacture or sell apparel to consumers, which we categorized as "brand" fashion apps, and the remaining 60 were categorized as "non-brand" fashion apps. The result of the study can be summarized as below. The 60 non-brand fashion apps were grouped into 5 app types: shopping, searching, sharing, organizational, and informational. The main functions are for informational use and shopping needs, since at least half (31 apps) are used for either retrieving information or for shopping. However, in contrast, social networking and location were infrequent and not commonly utilized by these apps. The most common type of non-brand fashion apps available were shopping apps;, many shopping apps enable users to shop from several different websites and save their items into one universal shopping cart so that they only check out once. Most of these apps are informational and help consumers make more informed decisions on purchases;, in addition many offer location services to help consumers find these items in store. While these apps perform several functions, they do not link to social media. The 50 brand apps were grouped into 5 brand types: athletic, casual, fast fashion, luxury, and retailer. These apps were also checked for attributes to determine their functionality. The result shows that the main functions of brand fashion apps are for information (82% of the 50 apps) as well as location searching (72% of 50 apps). Conversely, these apps do not offer any photo sharing, and very few have organizational or community functions. Fashion mobile apps and m-marketing elements: To build brand community, mobile apps can be designed to motivate consumer's engagement with brands. The motivations of fashion mobile apps are useful in developing fashion mobile apps. Entertainment motives can be fulfilled with multimedia attributes, functionality motives are satisfied with organizational and location-based features, information motives with informational service, socialization with community and social network, learning and intellectual stimulation from informational attributes, and trend following through photo sharing. The 8 key attributes of mobile apps can correspond to the 4 m-marketing elements (i.e., Informative content, multimedia, interactions, and product promotions) that are further intertwined with m-branding elements. App Attributes and M-Marketing aim to Build Brand Community;, the eight key attributes can impact on 4 m-branding elements, which further contribute to building brand community by affecting consumers' perceptions of brands preference and advocacy, and their likelihood to be loyal.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.437-448
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• 2008

As the importance of knowledge is emphasized and the environment of battlefields is changing, the military also demands competent people equipped with creativity, cooperativeness and communication ability, and in this situation it is required to apply PBL to education in the navy. The present study went through three stages in order to develop a prototype to implement a naval e PBL support system for knowledge creation. First, databases in Korea Education and Research Information Service, National Assembly Library, etc. were searched using keywords such as PBL, e-PBL, knowledge creation and knowledge ecosystem. In addition, we selected and analyzed frequently quoted literature and recent research reports related to this study among domestic and foreign theses, books, research papers, etc. recommended by specialists in contents, and derived the key values of a knowledge creation type e-PBL support system and design strategies. Second, we developed a primary prototype based on the contents of analysis and, revising it according to teaching design specialists' opinions, we proposed the final prototype of knowledge creation type naval e PBL support system and it has values as follows. First, the knowledge creation type naval e PBL support system provides learners with opportunities to apply e PBL and helps them improve their creativity, cooperativeness and communication ability and accumulate know how of services. Second, it improves work efficiency by circulating knowledge through sharing among individuals or groups, and produces synergy that promotes the organizational culture of learning. Third, the knowledge creation type naval e-PBL support system enables teachers who apply PBL to school education to find new applications of PBL in constructing knowledge bases.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.107-133
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• 2021

This study was performed in order to provide suggestions on how to strengthen librarian competency by evaluating and analyzing the competency value of librarians as information professions. First, the study divided the common competency value of librarians as human capital of libraries into skills, knowledge, behavior and attitude, and analyzed each area of competency value for librarians of the A-library. As a result, the average of the 'librarian's behavior and attitude' area was the highest, followed by the 'librarian's skill' area and the 'librarian's knowledge' area. Second, in terms of 'librarian's skill', A-library librarians' competence values were high in the order of 'communication', 'leadership', 'technology' and in the terms of 'librarian's knowledge' ones were high in the order of 'law and policy', 'marketing', 'learning and growth' and 'finance and accounting'. In addition, in areas of 'librarian's behavior and attitude', the factors were high in the order of 'ethics and values', 'interpersonal relationships' and 'customer service'. Third, the analysis of whether the average difference exists depending on the characteristics of A-library librarians on their evaluation of the competency value shows that only the 'working period' factor in the total competency value and the two factors 'age' and 'working period' were statistically significant in the 'librarian's knowledge' area. Forth, as a result of a regression analysis to identify the characteristics of A-library librarians and their impact on competency value, only the 'final education' factor was statistically significant for the competency value of the 'librarian's skill' area. Fifth, in the survey on problems and desirable improvement measures in increasing the competency value of librarians, the proportion of presenting problems and improvement plan in systemic aspects such as the 'librarian qualification system' and 'librarian training system' was high.

• Journal of Korean Library and Information Science Society
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• v.54 no.2
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• pp.43-65
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• 2023

The purpose of all public libraries is to provide knowledge and information services for the intellectual and reading activities of local residents, program services that provide opportunities for cultural enjoyment and lifelong learning, and third-party spaces and facilities that contribute to the development of the community. To this end, social needs must be reflected in a timely manner with the establishment of a sound infrastructure as a prerequisite. This study analyzed the current status centered on key indicators of the public libraries in Daegu City and presented policy issues that need to be improved through a survey. The key indicators in Daegu City, compared to the national average, were not only significantly weak for the 3rd largest city but also showed considerable variation among local governments. While Daegu citizens valued public libraries in their daily lives, the dissatisfaction rate was high in the order of transportation inconvenience, lack of necessary materials and desired programs, and various regulations. Therefore, Daegu City should focus on increasing the acquisition budget, strengthening the development of new book collections, expanding the number of librarians, expanding the construction of public libraries to address service disparities among local governments, improving accessibility, and addressing factors that hinder usage. In addition, with the establishment and operation of the Daegu Library in 2024, efforts should be made to establish an operational system for public libraries, expand the scope through collaborative partnerships with other knowledge and cultural institutions, and enhance knowledge and cultural services for the Daegu citizen.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.