• Journal of Korea Entertainment Industry Association
• /
• v.13 no.2
• /
• pp.133-143
• /
• 2019

When writing historical dramas, there was an argument that restoring the language of the times was the responsibility of the playwright, but no full-scale research was done. There was no collaborative study between playwrights and Korean Language scholars. So far, many playwrights have considered it the responsibility of Korean Language scholars to discover and restore language. However, it is a medium that can easily meet the public like a play or movie, and it should have a great responsibility for creation. Language changes with time, so restoring the language of the time in plays and scenarios can lead to difficulties in communicating with modern audiences. However, the change of language according to the times means that it captures the social image and fashion of the time Therefore, language restoration in historical dream means that scenes and backgrounds can be described more realistically. Restore of language is not just necessary to improve the creative environment; it should be understood as the responsibility of the artist to meet the ability of the audience to understand the language of the times already learned. The playwright who writes the historical drama should not only learn the grammar of the background era, but also find out the lost pronunciation and the changed vocabulary so that he can use various dialogues.

• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.1-42
• /
• 2009

In a knowledge-based society, a firm's intellectual capital represents the wealth of ideas and ability to innovate, which are indispensable elements for the future growth. Therefore, the intellectual capital is evidently recognized as the most valuable asset in the organization. Considered as intangible asset, intellectual capital is the basis based on which firms can foster their sustainable competitive advantage. One of the essential components of the intellectual capital is a social capital, indicating the firm's individual members' ability to build a firm's social networks. As such, social capital is a powerful concept necessary for understanding the emergence, growth, and functioning of network linkages. The more social capital a firm is equipped with, the more successfully it can establish new social networks. By providing a shared context for social interactions, social capital facilitates the creation of new linkages in the organizational setting. This concept of "person-environment fit" has long been prevalent in the management literature. The fit is grounded in the interaction theory of behavior. The interaction perspective has a fairly long theoretical tradition, beginning with proposition that behavior is a function of the person and environment. This view asserts that neither personal characteristics nor the situation alone adequately explains the variance in behavioral and attitudinal variables. Instead, the interaction of personal and situational variables accounts for the greatest variance. Accordingly, the person-environment fit is defined as the degree of congruence or match between personal and situational variables in producing significant selected outcomes. In addition, information systems acceptance factors enable organizations to build large electronic communities with huge knowledge resources. For example, the Intranet helps to build knowledge-based communities, which in turn increases employee communication and collaboration. It is vital since through active communication and collaborative efforts can employees build common basis for shared understandings that evolve into stronger relationships embedded with trust. To this aim, the electronic communication network allows the formation of social network to be more viable to rapid mobilization and assimilation of knowledge assets in the organizations. The purpose of this study is to investigate: (1) the impact of person-environment fit(person-job fit, person-person fit, person-group fit, person-organization fit) on social capital(network ties, trust, norm, shared language); (2) the impact of information systems acceptance factors(availability, perceived usefulness, perceived ease of use) on social capital; (3) the impact of social capital on personal performance(work performance, work satisfaction); and (4) the mediating role of social capital between person-environment fit and personal performance. In general, social capital is defined as the aggregated actual or collective potential resources which lead to the possession of a durable network. The concept of social capital was originally developed by sociologists for their analysis in social context. Recently, it has become an increasingly popular jargon used in the management literature in describing organizational phenomena outside the realm of transaction costs. Since both environmental factors and information systems acceptance factors affect the network of employee's relationships, this study proposes that these two factors have significant influence on the social capital of employees. The person-environment fit basically refers to the alignment between characteristics of people and their environments, thereby resulting in positive outcomes for both individuals and organizations. In addition, the information systems acceptance factors have rather direct influences on the social network of employees. Based on such theoretical framework, namely person-environment fit and social capital theory, we develop our research model and hypotheses. The results of data analysis, based on 458 employee cases are as follow: Firstly, both person-environment fit(person-job fit, person-person fit, person-group fit, person-organization fit) and information systems acceptance factors(availability perceived usefulness, perceived ease of use) significantly influence social capital(network ties, norm, shared language). In addition, person-environment fit is a stronger factor influencing social capital than information systems acceptance factors. Secondly, social capital is a significant factor in both work satisfaction and work performance. Finally, social capital partly plays a mediating role between person-environment fit and personal performance. Our findings suggest that it is vital for firms to understand the importance of environmental factors affecting social capital of employees and accordingly identify the importance of information systems acceptance factors in building formal and informal relationships of employees. Firms also need to reflect their recognition of the importance of social capital's mediating role in boosting personal performance. Some limitations arisen in the course of the research and suggestions for future research directions are also discussed.

• Korean Journal of Optics and Photonics
• /
• v.28 no.5
• /
• pp.203-212
• /
• 2017

A scientific model refers to a conceptual system that can describe, explain, and predict a particular physical phenomenon. The co-construction of the scientific model is attracting attention as a new teaching and learning strategy in the field of science education and various studies. The evaluation and modification of models compared with the predicted models of data from the real world is the core of modeling strategy. However, there were only a limited data provided by the teacher in many studies of modeling comparing the students' predictions of their own models. Most of the students were not given the opportunity to evaluate the suitability of the model with the data in the real world. The purpose of this study was to develop a scientific model co-construction program that can evaluate the model by directly comparing the predicted models with the observed data from the real world. Through a collaborative discussion between teachers and researchers for 6 months, a 5-session scientific model co-construction program on the subject 'image formation by convex lenses' for second grade middle school students was developed. Eighty (80) students in 3 classes and a science teacher with 20 years of service from general public co-educational middle school in Gyeonggi-do participated in this 2-week program. After the class, students were asked about the helpfulness and difficulty of the class, and whether they would like to recommend this class to a friend. After the class, 95.8% of the students constructed the scientific model more than the model using the construction rule. Students had difficulties to identify principles or understand their friends, but the result showed that they could understand through model evaluation experiment. 92.5% of the students said that they would be more than willing to recommend this program to their friends. It is expected that the developed program will be applied to the school and contribute to the improvement of students' modeling ability and co-construction ability.

• Journal of Korean Home Economics Education Association
• /
• v.30 no.2
• /
• pp.77-102
• /
• 2018

The purpose of this study was to contribute to Home Economics(HE) teachers' culture by figuring out acknowledging characteristics of cultures of HE teachers and impeding factors on development of HE education. For this intensive interview were used. Intensive interviews were made with 14 HE teachers who completed coursework for master's or doctor's program of graduate school and belong to HE Teachers' Study Associations of each region or Korean Home Economics Education Association and analyzed by subject analysis method. The results of the study are as follows. First, HE teachers establish the philosophy of HE education, and practice education to provide profit to adolescents, their families, as well as society through HE class with their belief that HE is a practical and critical subject to benefit individual adolescents, families, and society. Second, HE teachers form culture to make an effort to continue to improve their expertises by attending graduate school, joining HE teachers' associations to enhance teaching methods, evaluation methods, and work ability or disclosing their own class. Third, HE teachers settle culture to conduct classes focusing on practical issues by converting the paradigm of HE education to that of practical critique. They also see that the system of three actions(technical action, communicative action, and emancipative action) should be applied in circulating ways to improve quality and value of life. Forth, for impeding factors of development of HE education, there are educational system and social recognition. However, with HE teachers' efforts, HE education settles well, as it reflects demands from students and society, finds students' talents, and actualizes its own goals. HE teachers believe that student will recognize that HE education is necessary for happiness of individuals and families. As a way to develop Home Economics teacher culture, Home Economics teachers should have the opportunity to develop more Home Economics teachers by participating in and working in research sessions in each area. It also called for a control tower to enable and lead collaborative networks between local Home Economics curriculum research committees. The Korean Home Economics Education Association should play a central role in the academic research community of each region and be able to help Home Economics teachers by moving more quickly and systematically to cope with the upcoming changes in education. Finally, participants said that in order to prepare a basic framework for the change in Home Economics education, practical critical Home Economics teacher training are needed. To this end, students can understand the essence of Home Economics education and establish their identity by taking a deeper Home Economics education curriculum philosophy for Home Economics teacher training.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.1-20
• /
• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.89-116
• /
• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.