• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.141-166
• /
• 2018

Information technology improves the efficiency of humanities research. In humanities research, information technology can be used to analyze a given topic or document automatically, facilitate connections to other ideas, and increase our understanding of intellectual history. We suggest a method to identify and automatically analyze the relationships between arguments contained in unstructured data collected from humanities writings such as books, papers, and articles. Our method, which is called history mining, reveals influential relationships between arguments and the philosophers who present them. We utilize several classification algorithms, including a deep learning method. To verify the performance of the methodology proposed in this paper, empiricists and rationalism - related philosophers were collected from among the philosophical specimens and collected related writings or articles accessible on the internet. The performance of the classification algorithm was measured by Recall, Precision, F-Score and Elapsed Time. DNN, Random Forest, and Ensemble showed better performance than other algorithms. Using the selected classification algorithm, we classified rationalism or empiricism into the writings of specific philosophers, and generated the history map considering the philosopher's year of activity.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.151-166
• /
• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• The Journal of the Korea Contents Association
• /
• v.17 no.1
• /
• pp.550-566
• /
• 2017

Recent advances in wearable devices and quantified-self movement increase the number of personal informatics application that may cause an concern to health industry and user. In this light, the goal of this study is to identify more effective ways of design and evaluation of personal informatics application for self-tracking and delivering health information to users. For this goal, this study conducted areal-world study that processes such that user can assess, be aware of, and self-reflect on their data and behavior activity. In doing so, this study aims to determine the psychological effects of forms of health feedback (comparative vs. non-comparative) and presentation modes (text vs. image) on users' tendencies toward health conservation. Results from a between-subjects experiment revealed that health information in a comparative and textual format was more effective in encouraging health conservation in participants than identical information presented in a non-comparative and image format. In addition, participants' level of health consciousness emerged as a significant predictor. Through this analysis of quantitative data and inferences, this study make a number of contributions to the user affordance research and its methodology of health informatics study and designing personal informatics application that support user's behavior change in various contexts.

• Journal of Digital Convergence
• /
• v.13 no.4
• /
• pp.395-404
• /
• 2015

This study is exploring objective awareness of literary therapy by consideration of popular perception about literary therapy through analysis of big data. The purpose of this study is the deduction of meaning information through analysis in the viewpoint of big data at online social network service(SNS) about 'literary therapy'. Accordingly, the main way of research became content analysis of keyword linked to literary therapy by utilizing opinion mining method related to text mining. The study mainly grasped 'literary therapy' and analyzed 'bibliotherapy' comparatively. The period of study was from Oct. 10th to Nov. 10th, 2014(during 30 days), and SNS such as blog or twitter became the subject of search. Through the result of study analysis, the conclusion that the spread of literary therapeutic prospect, structural harmony of literary therapeutic field, and the solidity of perceptional axis about literary therapy are needed can be drawn. This study is worthwhile because it can investigate popular awareness about literary therapy and can suggest alternative for invigoration of literary therapy.

• The Journal of the Korea Contents Association
• /
• v.16 no.1
• /
• pp.424-437
• /
• 2016

The purpose of this study is to find out the effect of augmented reality contents based instruction for adult learners on academic achievement, interest and flow in learning. The subject populations were 80 students randomly sampled from an IT institute and they were evenly placed into two groups. One cell as an experimental group studied with augmented reality based contents and the other cell as a control group studied under textbook based instruction for two weeks. The experimental design of this study was the pre-posttest control group design. The results are summarized as follows: First, there was no significant difference in academic achievement between two groups. Second, the group studied with augmented reality based contents showed higher interest in learning than textbook based instruction group. Finally, there was a significant difference in flow in learning between two groups. The augment reality based instruction group showed higher scores of flow than the other group. The implication of this study is that augmented reality contents may have different effects for adult learners in academic achievement compared with younger learners.

• Journal of Korean Classical Literature and Education
• /
• no.33
• /
• pp.45-82
• /
• 2016

Class, the fundamental unit of school education and the meeting place of teacher and students, plays an important role in study of the subject matter of education. Class criticism is material to the theory or method that helps researchers deeply understand and analyze class phenomena or teachers' actions during a class. In this study, I make a critique on the features of a classic novel class as attempt to expand on new prospects in the field of research on classical literature education. The classic novel class in this class criticism is typical one, which reads the work analytically. Nevertheless, the teacher turns the students' vague repulsion into empathy and helps them appreciate and internalize the work. Students' empathy and response are reflected in the interpreting-centered class because the teacher's insights about the work and experience, knowledge, and method of literature education are projected during the class. Especially, a situation in which the teacher spends a relatively long time narrating the background of the work clearly shows the value and meaning disseminated in a classic novel class. Based on the aforementioned, attempts to collect a variety of cases of a classic novel class and to understand the meaning of these cases have to be part of future research. The research on the attributes of a class such as criticism of classic novels enables us to renew introspection to discover classical literature education.

• The Journal of the Korea Contents Association
• /
• v.19 no.6
• /
• pp.10-23
• /
• 2019

Although social big data can provide a multi-faceted perspective on school bullying experiences among children and adolescents, the complexity and variety of unstructured text presents a challenge for systematic collection and analysis of the data. Development of an ontology, which identifies key terms and their intricate relationships, is crucial for extracting key concepts and effectively collecting data. The current study elaborated on the definition of an ontology, carefully described the 7 stage development process, and applied the ontology for collecting and analyzing school bullying social big data. As a result, approximately 2,400 key terms were extracted in top-, middle-, and lower-level categories, concerning domains of participants, causes, types, location, region, and intervention. The study contributes to the literature by explaining the ontology development process and proposing a novel alternative research model that uses social big data in school bullying research. Findings from this ontology study may provide a basis for social big data research. Practical implications of this study lie in not only helping to understand the experience of school bullying participants, but also in offering a macro perspective on school bullying as a social phenomenon.

• Knowledge Management Research
• /
• v.23 no.3
• /
• pp.129-152
• /
• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of Digital Convergence
• /
• v.20 no.5
• /
• pp.627-635
• /
• 2022

As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

• Industry Promotion Research
• /
• v.7 no.3
• /
• pp.77-83
• /
• 2022

This study was conducted with the purpose of helping middle-aged women who experienced insecure attachment during personal growth experience positive changes by re-illuminating their own growth process. During this study period, 14 in-depth interviews were conducted from August 2020 to September 2021, and the interview contents were based on the narrative research methodology to examine the meaning of participants' experiences regarding unstable attachment and post-traumatic growth in existential contexts. The text was described in terms of, relational context, life context, etc. As a result of the study, three participants who experienced unstable attachment and post-traumatic growth were selected and the following conclusions were drawn. First, the meaning in the existential context is the desire for recognition, perfectionism, unstable family environment, how to cope with stress, the courage to face the wounds, self-acceptance and affirmation, gratitude to the people around you, and the hope of life is the meaning in the participant experience. was interpreted as Second, the meaning in the relational context was interpreted as experiences with parents, husbands, children, interpersonal relationships, and religion. Third, the meaning in the life context is the lack of care, the reproduction of control, the responsibility as the eldest daughter, the precious family, and the meaning and value of life is the present experience in which the various experiences with the parents in the past affect the lives of the current participants. interpreted in Through the above research results, this study aims to describe the experiences of insecure attachment during childhood and the post-traumatic growth process of participants using a narrative technique, and to suggest positive alternatives to their lives.