• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of the Korean earth science society
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• v.28 no.7
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• pp.762-774
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• 2007

The purpose of this study was to investigate misconceptions about plate tectonics which spread widely among freshmen at high school with drawing. For this, we chose 6 conceptions about plate tectonics by analysis of 7th curriculum and of 11 kinds of science textbooks. Questionnaire of drawing about plate tectonics were developed depending on them. Data was collected from 134 students who was freshmen at high school in Daegu. The result of this study was as follows. First, In structure of plate, 'upper mantle type' and 'crust type' misconceptions were more than half of the respondents. Second, In distribution of plate, 'cracked earthquake zone type' and 'earthquake frequency type' misconceptions were more than half of the respondents. Third, In formation of ocean ridge at oceanic crust- oceanic crust divergent plate boundary, 'divergence type' and 'collision type' misconceptions were more than half of the respondents. Fourth, In formation of mountain ridge at continental crust- continental crust convergent plate boundary, 'collision type' misconceptions were more than half of the respondents. Fifih, In formation of mountain ridge at oceanic crust- continental crust convergent plate boundary, 'subduction type' and 'fault type' misconceptions were more than half of the respondents. Sixth, In transform-fault at oceanic crust- oceanic crust transform-fault boundary, 'direction type' and 'section type' misconceptions were almost half of the respondents. In this study, students' drawings about plate tectonics showed similar misconceptions. This imply that drawing conceptions can be used by the strong evidence of misconceptions which spread widely among students. Furthermore, this study has a significance that this conclusion is useful to teachers as basic teaching-teaming materials of plate tectonics.

• Applied Microscopy
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• v.35 no.3
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• pp.135-152
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• 2005

Prefrontal cortex is a psychological and metaphysical cortex, which deals with feeling, memory, planning, attention, personality, etc. And it also integrates above-mentioned events with motor control and locomotor activities. Prefrontal cortex works as a highest CNS center, since the above mentioned functions are very important for one's successful life, and further more they are upgraded every moments through memory and learning. Many of these highest functions are supposed to be generated via forebrain basal nuclei (caudate nucleus, fundus striati nucleus, accumbens septi nucleus, septal nucleus, etc.). In this experiment, prefrontal efferent terminals within basal forebrain nuclei were ultrastructurally studied. Spraque Dawley rats, weighing $250{\sim}300g$ each, were anesthetized and their heads were fixed on the stereotaxic apparatus (experimental model, David Kopf Co.). Rats were incised their scalp, perforated a 3mm-wide hole on the right side of skull at the 11mm anterior point from the frontal O point (Ref. 13, Fig. 1), suctioned out the prefrontal cortex including cortex of the frontal pole, with suction instrument. Two days following the operations, small tissue blocks of basal forebrain nuclei were punched out, fixed in 1% glutaraldehyde-1% paraformaldehyde solution followed by 2% osmium tetroxide solutions. Ultrathin sections were stained with 1% borax-toluidin blue solution, and the stained sections were obserbed with an electron microscope. Degenerating axon terminals were found within all the basal forbrain nuclei. Numbers of degenerated terminals were largest in the caudate nucleus, next in order, in the fundus striati nucleus, in the accumbens septi nucleus, and the least in the septal nucleus. Only axospinous terminals were degenerated within the caudate nucleus and the fundus striati nucleus, and they showed the characters of striatal motor control system. Axodendritic and axospinous terminals were degenerated within the accumbens septi nucleus and the lateral septal nucleus, and they showed the characters of visceral limbic system. Prefrontal role in integrating the limbic system with the striatal system, en route basal forebrain nuclei, was discussed.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.193-202
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• 2019

This study focuses on what changes are required as to a curriculum of dance department in the university in the age of the 4th industrial revolution. By comparing and analyzing the curricula of dance department in the five universities in Seoul, five academic subjects as to curricula of dance department, which covers what to learn for dance education in the age of the 4th industrial revolution, are presented. First, dance integrative education, the integration of creativity and science education, can be referred to as a subject that stimulates ideas and creativity and raises artistic sensitivity based on STEAM. Second, the curriculum characterized by prediction of the future prospect through Big Data can be utilized well in dealing with dance performance, career path of dance-majoring people, and job creation by analyzing public opinion, evaluation, and feelings. Third, video education. Seeing the images as modern major media tends to occupy most of the expressive area of art, dance by dint of video enables existing dance work to be created as new form of art, expanding dance boundaries in academic and performing art viewpoint. Fourth, VR and AR are essential techniques in the era of smart media. Whether upcoming dance studies are in the form of performance or education or industry, for VR and AR to be digitally applied into every relevant field, keeping with the time, learning about VR and AR is indispensable. Last, the 4th industrial revolution and the curriculum of dance art are needed to foresee the changes in the 4th industrial revolution and to educate changes, development and seeking in dance curriculum.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.295-306
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• 2022

This study aims to analyze the aspect of inquiry activity for seven national science textbooks, which were recently authorized and developed for elementary schools. The study reviewed seven of the abovementioned textbooks, which were introduced to the authorized textbook system for 2022 in elementary schools. In-depth analysis was conducted on inquiry activities, which were frequently emphasized since the third curriculum. A chapter entitled "Stratum and Fossils" was selected to analyze the detailed types of inquiry activity using the framework. First, analysis was conducted on six types of inquiry activity. The result indicated that two out of seven inquiry activities different among the national and authorized textbooks. In other words, the two slightly differed from the inquiry activities presented in national textbooks. It was not reasonable that the introduction of the authorized textbook exerted an effect on the increase of autonomy and variety of teaching and learning activities compared with those of national textbooks. Second, science process skills, material, and inquiry process were established as standards for the detailed types of inquiry activity. The study demonstrated a slight difference among textbooks in terms of material and inquiry process, whereas the inquiry activities of the seven authorized textbooks were the same or similar. This finding indicated that students could experience seven types of inquiry activities. Moreover, the study examined inquiry activities related to essential learning contents in detail and found that there were more diverse than types of inquiry activities.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.69-94
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• 2001

The purpose of this paper is to provide an overview of the current status of the records management science education in Japan, and to examine the implications of the rapid growth of this filed while noting some of its significant issues and problems. The goal of records management science education is to improve the quality of information services and to assure an adequate supply of information professionals. Because records management science programs prepare students for a professional career, their curricula must encompass elements of both education and practical training. This is often expressed as a contrast between theory and practice. The confluence of the social, economic and technological realities of the environment where the learning takes place affects both. This paper reviews the historical background and current trends of records management science education in Japan. It also analyzes the various types of curriculum and the teaching staff of these institutions, with focus on the status of the undergraduate program at Surugadai University, the first comprehensive, university level program in Japan. The Faculty of Cultural Information Resources, Surugadai University, a new school toward an integrated information disciplines, was opened in 1994, to explore the theory and practice of the management diverse cultural information resources. Its purpose was to stimulate and promote research in additional fields of information science by offering professional training in archival science, records management, and museum curatorship, as well as librarianship. In 1999, the school introduced a master program, the first in Japan. The Faculty has two departments and each of them has two courses; Department of Sensory Information Resources Management; -Sound and Audiovisual Information Management, -Landscape and Tourism Information Management, Department of Knowledge Information Resources Management; -Library and Information Management, -Records and Archives Management The structure of the entire curriculum is also organized in stages from the time of entrance through basic instruction and onwards. Orientation subjects which a student takes immediately upon entering university is an introduction to specialized education, in which he learns the basic methods of university education and study, During his first and second years, he arranges Basic and Core courses as essential steps towards specialization at university. For this purpose, the courses offer a wide variety of study topics. The number of courses offered, including these, amounts to approximately 150. While from his third year onwards, he begins specific courses that apply to his major field, and in a gradual accumulation of seminar classes and practical training, puts his knowledge grained to practical use. Courses pertaining to these departments are offered to students beginning their second year. However, there is no impenetrable wall between the two departments, and there are only minor differences with regard requirements for graduation. Students may select third or fourth year seminars regardless of the department to which they belong. To be awarded a B.A. in Cultural Information Resources, the student is required to earn 34 credits in Basic Courses(such as, Social History of Cultural Information, Cultural Anthropology, History of Science, Behavioral Sciences, Communication, etc.), 16 credits in Foreign Languages(including 10 in English), 14 credits on Information Processing(including both theory and practice), and 60 credits in the courses for his or her major. Finally, several of the issues and problems currently facing records management science education in Japan are briefly summarized below; -Integration and Incorporation of related areas and similar programs, -Curriculum Improvement, -Insufficient of Textbooks, -Lack of qualified Teachers, -Problems of the employment of Graduates. As we moved toward more sophisticated, integrated, multimedia information services, information professionals will need to work more closely with colleagues in other specialties. It will become essential to the survival of the information professions for librarians to work with archivists, record managers and museum curators. Managing the changes in our increasingly information-intensive society demands strong coalitions among everyone in cultural Institutions. To provide our future colleagues with these competencies will require building and strengthening partnerships within and across the information professions and across national borders.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.