• Journal of the Korean Society of Earth Science Education
• /
• v.15 no.2
• /
• pp.235-248
• /
• 2022

With the changes in the future educational environment, such as the rapid decline of the school-age population and the expansion of students' choice of curriculum, changes are also required in PCK, the expertise of science teachers. In other words, the categories constituting the existing 'consensus-PCK' and the characteristics of 'science PCK' are not fixed, so more categories and characteristics can be added. The purpose of this study is to explore the potential area of science PCK required to cope with changes in the future educational environment in the form of 'Family Resemblance Science PCK (Family Resemblance-PCK, hereafter)' through Wittgenstein's family resemblance approach. For this purpose, in-depth interviews were conducted with three focus groups. In the focus group in-depth interview, participants discussed how the science PCK required for science teachers in future schools in 2030-2045 will change due to changes in the future society and educational environment. Qualitative analysis was performed based on the in-depth interview, and semantic network analysis was performed on the in-depth interview text to analyze the characteristics of 'Family Resemblance-PCK' differentiated from the existing 'consensus-PCK'. In results, the characteristics of Family Resemblance-PCK, which are newly requested along with changes in role expectations of science teachers, were examined by PCK area. As a result of semantic network analysis of Family Resemblance-PCK, it was found that Family Resemblance-PCK expands its boundaries from the existing consensus-PCK, which is the starting point, and new PCK elements were added. Looking at the aspects of Family Resemblance-PCK, [AI-Convergence Knowledge-Contents-Digital], [Community-Network-Human Resources-Relationships], [Technology-Exploration-Virtual Reality-Research], [Self-Directed Learning-Collaboration-Community], etc., form a distinct network cluster, and it is expected that future science teacher expertise will be formed and strengthened around these PCK areas. Based on the research results, changes in the professionalism of science teachers in future schools and countermeasures were proposed as a conclusion.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.69-94
• /
• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.57-78
• /
• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.1-19
• /
• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.219-240
• /
• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Korean Biological Nursing Science
• /
• v.4 no.1
• /
• pp.25-39
• /
• 2002

The purpose of this study is to develop and evaluate a web-based instruction Program(WBI) to help nurses improving their knowledge and skill of cardiopulmonary resuscitation. Using the model of web-based instruction(WBI) program designed by Rhu(1999), this study was carried out during February-April 2002 in five different steps; analysis, design, data collection and reconstruction, programming and publishing, and evaluation. The results of the study were as follows; 1) The goal of this program was focused on improving accuracy of knowledge and skills of cardiopulmonary resuscitation. The program texts consists of the concepts and importances of cardiopulmonary resuscitation(CPR), basic life support(BLS), advanced cardiac life support(ACLS), treatment of CPR, nursing care after CPR treatment. And in the file making step, photographs, drawings and image files were collected and edited by web-editor(Namo), scanner and Adobe photoshop program. Then, the files were modified and posted on the web by file transfer protocol(FTP). Finally, the program was demonstrated and once again revised by the result, and then completed. 2) For the evaluation of the program, 36 nurses who in K university hospital located in D city, and related questionnaire were distributed to them as well. Higher scores were given by the nurses in its learning contents with $4.2{\pm}.67$, and in its structuring and interaction of the program with $4.0{\pm}.79$, and also in its satisfactory of the program with $4.2{\pm}.58$ respectively. In conclusion, if the contents of this WBI educational program upgrade further based upon analysis and applying of the results the program evaluation, it is considered as an effective tool to implement for continuing education as life-long educational system for nurse.

• Journal of Korean Home Economics Education Association
• /
• v.22 no.3
• /
• pp.1-18
• /
• 2010

The purpose of this study was to develop a teaching-learning process plan for sustainable residing creating with neighbors and to apply it to the housing section of Technology-Home Economics according to the 2007 Revised Curriculum. Teachinglearning method solving practical problems was used for the teaching-learning process plans of 6-session lessons according to the ADDIE model. In the development stage, 17 activity materials and 15 teaching learning materials (6 reading texts, 6 moving pictures, 2 internet and 1 image materials) were developed. for the 6-session lessons, based on the stages of solving practical problems. The plans applied to the 3 classes of 8, 9, and 10th grade of the H. junior and senior high school in Myun district in Kyungbook during Sept. 1st to 14th, 2009. The results showed that students actively participated when the contents and materials were related to their own experience. The 6-session lessons about sustainable residing creating with neighbors was significantly increased the sense of community between before and after. Each of the 4 stages of the teachinglearning method solving practical problems were highly participated by the students. The satisfaction with the contents and methods of the 6-session lessons were evaluated over medium to somewhat higher levels. The practical activities to solve the community space and programs were got positive comments. Problem solving process and presentation and discussion were needed to learn more. Those results might support that the teachinglearning process plan this research developed. would be appropriate to the lessons for sustainable residing creating with neighbors.

• Cartoon and Animation Studies
• /
• s.44
• /
• pp.143-161
• /
• 2016

This study was conducted focusing on the role of auteurism animation. The purpose of auteurism animation is to criticize irregularities of a society in witty and fierce way through satires from the sharp perspectives of a animator that is not bound by tastes of people or the interests or standpoints of specific groups, and thus to induce positive changes in a society as a purifier. In the context, this study investigated satires shown in by Jan Svankmajer and by Norman Mclaren among the animators who utilize animation as a tool to produce social meaning. As a result, the following characteristics and meanings were found. First, Dimensions of Dialogue is an animation that satires absurdity and irregularities of a human society in symbolic and exceptional way through directing by segmentations of images and omnibus structures. The satire carries the lesson of improvement in the hidden part of cynical attack to history, society, and human beings. It also maximizes absolute reality and engagement of images of Jan Svankmajer through unique and grotesque images of the animator such as alienated world, confusing shapes, and amusement of irregularities. Second, the movie, is an exemplary animation that applied core concept of animation through pixilation techniques based on an event story structure by causal relationship. It satires the changing process of a good man to violent madness through confrontation and conflicts for material desires, with exaggerated slipstick movements and humors as a black comedy. The satire methods of both animation works are delivered through unique image styles and symbolic wordage of the animators who triggered ironical laughter in attacking humanism and moral insensitivity that might be felt seriously otherwise. That is, the animators try to show the positive will for changing the society to a sound one through the form of negativity in terms of moral perspective in animation rather than destruction against the target. As such, the satires in both works worked as an auteurism allegory that maximizes social functions and artistic influence of animation.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.227-240
• /
• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Cartoon and Animation Studies
• /
• s.47
• /
• pp.241-271
• /
• 2017

Film color is equivalent to other elements of film, including narrative, and has a textual meaning according to the identity of expression. In general, red has a function of focusing attention, and the meaning derived from it is diffused. In the interaction of text and context, the function of concentration and the meaning of diffusion can be presented. The concept of concentration and diffusion is shaped by the relationship between independent colors, colors and other cinematic elements, and interactions between colors. In order to confirm this, this study analyzes a series of popular Korean films, how film colors interact, and in particular, the concentration function of red and the meaning of proliferation. The results of this study are as follows. First, in Korean popular films, at its most basic, red symbolizes a nation, a people, and a nation. The red of nationalism surrounding ethnicity, nationality and country visualizes ideology and conflict. The purpose of an individual or group, the relationship between the offender and the victim is mediated through red. The flag, the name tag, the costume appearing in the film are red. This can be seen in films such as Train to Busan, Assassination, Masquerade, Miracle in Cell No.7, Brotherhood of War, Northern Limit Line, Joint Security Area, Welcome to Dongmakgol, and May 18. Second, the red color attached to the female body fixes or strengthens socio-cultural sexuality and gender. The examples are films like Ode to My Father, The Thieves, The Host, Purpose Of Love, Sunny, Like A Virgin, Forbidden Quest, Untold Scandal, Bewitching Attraction, and Ssanghwajeom. Third, the blood red in Korean films is a visual device that directs magical horror, anger, and asceticism. Such films include The Neighbors, Bunshinsaba, R-Point, A Tale Of Two Sisters, Whispering Corridors, The Uninvited, Thirst, SECTOR 7, Asura:The City of Madness, The Tiger, Veteran, and so on. Fourth, red of tears constitutes the specific emotions such as a beautiful desire and a brilliant tragedy in films like King and The Clown, Oldboy, Memories of Murder, 26 Years, The Attorney, Unbowed, Sympathy For Lady Vengeance, Happy End, Punch, Calling, The Yellow Sea, and He's on Duty.