• International Journal of Knowledge Content Development & Technology
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• v.13 no.1
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• pp.111-130
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• 2023

There are the national science and technology standard classification system used in Korea, the classification according to the standard classification system for educational organization units, and the Korean standard education classification by the National Statistical Office. It is not suitable for calculation or evaluation, and classification is still mixed depending on the purpose of use. Therefore, in this study, the current status of academic standard classification, issues related to the standard classification system such as research foundation associations and research institutes, and issues related to the academic standard classification through the analysis of existing prior research issues, etc. As a result of the research, first, it is necessary to maintain and strengthen the linkage of the academic classification system, such as maintaining the linkage between the relevant departmental classification systems and strengthening the linkage with the relevant classification system, as a result of analysis of major issues in the academic standard classification system, and the systematic improvement cycle of the revision process and management system and settings are required.

• Journal of the Korean Society for information Management
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• v.24 no.4
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• pp.153-171
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• 2007

This study suggests that improvement of theses and dissertations retrieval can be made by applying appropriate academic department classification. We applied the Korea Research Institute for Vocational Education and Training(KRIVET)'s department classification to these and dissertations being serviced by Korea Education & Research Information Service(KERIS). The results show that the chosen classification appropriately represents diverse academic department information contained in the theses and dissertations either published or used within the recent three year period. The study also makes a number of suggestions that will facilitate the application of an academic department classification to a live system.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.4
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• pp.477-488
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• 2017

This study presents a discussion on the biomedical laboratory science (formally clinical laboratory science or medical laboratory science) with the identity of biomedical laboratory science, as well as the academic classification system for systematic approach. The field of biomedical laboratory science is not registered in the academic research area classification system of the National Research Foundation of Korea. Since the inception of the first department of biomedical laboratory science in 1963, about 52 departments were since established. Despite the scientific identity, biomedical laboratory science have not been acknowledged professionally in most institutions. Observing the academic research area classification, the physical therapy, occupational therapy, and dental hygiene science are systematically classified and approved the identities by the authorities. This study is freshly academic area classification system of the biomedical laboratory science. The contents of this study are summarized as follows. The medical laboratory technologist's discipline is considered within the medical and science category, clinical pathology in class, and biomedical laboratory science in division. Sections of biomedical laboratory science include hematology, transfusionology, immunology, biochemistry, microbiology, parasitology, science, molecular biology, histology, cytology, cardiopulmonary physiology, and neurophysiology.

• Journal of Korean society of Dental Hygiene
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• v.10 no.1
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• pp.173-183
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• 2010

Objectives : The purpose of this study was to provide basic materials for defining educational direction of dental hygiene department and establishing the instruction to improve direction consulting and academic effect of dental hygiene department student. Methods : The researcher surveyed the relation among job aptitude, academic ability and academic performance by selecting 131 dental hygiene department students of P university as study targets. Results : For high school classifications, direction searches and academic abilities of dental hygiene department students of P university, it was found that classical high school was 68.7% and vocational high school was 31.3%. For job aptitude, social type was 58.0% and artistic type was 26.0% so they were usual. For academic ability, interpersonal relation($12.78{\pm}1.34$), music/rhythm was($12.32{\pm}1.09$) and natural($12.32{\pm}1.00$) showed high scores in order over the first, the second and the third field and language/vocabulary(22.6%) and music/rhythm(21.6%) was the next. For academic performance depending on high school classification, job aptitude and academic ability, there was a significant difference in high school classification by classical high school($86.55{\pm}8.21$) and vocational high school($85.34{\pm}11.31$)(p<0.05) and there was also a significant difference in job aptitude by social type($85.45{\pm}9.42$) and artistic type($88.41{\pm}6.93$)(p<0.05). In the mutual relation between academic ability and academic performance, the high academic ability score in the first field was led to the high score in the second and the third field, showing significant mutual relation(p<0.00). Conclusions : This research has been accomplished by college students of dental hygeine department, so you have to consider before generalizing these results. Therefore it is required to research more, likewise using a comparison with other students or it should be conducted by general people.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.53-63
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• 2023

The utilization of department information in bibliometric analysis using scientific and technological literature is highly advantageous. In this paper, the department information dataset was built through the screening, data refinement, and classification processing of authors' department type belonging to university institutions appearing in academic journals in the field of science and technology published in Korea, and the automatic classification model based on deep learning was developed using the department information dataset as learning data and verification data. In addition, we analyzed the co-authorship structure and network in the field of computer science using the department information dataset and affiliation information of authors from domestic academic journals. The research resulted in a 98.6% accuracy rate for the automatic classification model using Korean department information. Moreover, the co-authorship patterns of Korean researchers in the computer science and engineering field, along with the characteristics and centralities of the co-author network based on institution type, region, institution, and department type, were identified in detail and visually presented on a map.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.61-77
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• 2017

Recently, the number of academic conference information on the Internet has rapidly increased, the automatic classification of academic conference information according to research subjects enables researchers to find the related academic conference efficiently. Information provided by most conference listing services is limited to title, date, location, and website URL. However, among these features, the only feature containing topical words is title, which causes information insufficiency problem. Therefore, we propose methods that aim to resolve information insufficiency problem by utilizing web contents. Specifically, the proposed methods the extract main contents from a HTML document collected by using a website URL. Based on the similarity between the title of a conference and its main contents, the topical keywords are selected to enforce the important keywords among the main contents. The experiment results conducted by using a real-world dataset showed that the use of additional information extracted from the conference websites is successful in improving the conference classification performances. We plan to further improve the accuracy of conference classification by considering the structure of websites.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.86-94
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• 2019

This study investigated the differences of perceived parenting attitude and academic stress on smartphone addiction according to the classification of addiction-risk group among middle school students. A descriptive research design was used. The subjects were 358 middle school students from five middle school in Daegu. Data were analyzed by descriptive statistics, ${\chi}^2$ test, t-test, and stepwise multiple regression using SPSS 18.0. Smartphone addiction-risk group was 97(27.0%) and general group was 261(72.9%). Smartphone addiction-risk group was more negatively perceived parenting attitude and higher academic stress than the general group. The most influential factors on addiction-risk group was using time in a day(${\beta}=.29.4$, p=.003) and general group was academic stress(${\beta}=.298$, p=.000). It is important to develop an intervention program to prevention the smartphone addiction according to the classification of addiction-risk group.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• International Journal of Knowledge Content Development & Technology
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• v.2 no.1
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• pp.51-65
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• 2012

While science and technology information service portals and heterogeneous databases produced in Korea and other countries are integrated, methods of connecting the unique classification systems applied to each database have been studied. Results of technologists' research, such as, journal articles, patent specifications, and research reports, are organically related to each other. In this case, if the most basic and meaningful classification systems are not connected, it is difficult to achieve interoperability of the information and thus not easy to implement meaningful science technology information services through information convergence. This study aims to address the aforementioned issue by analyzing mapping systems between classification systems in order to design a structure to connect a variety of classification systems used in the academic information database of the Korea Institute of Science and Technology Information, which provides science and technology information portal service. This study also aims to design a mapping system for the classification systems to be applied to actual science and technology information services and information management systems.