• 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 Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.5
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• pp.19-27
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• 2013

This paper claims that the One Million-peyong Park(hereafter abbreviated as OMP) project is different from a typical citizen participatory park project by recognizing the exceptional leadership of the Civic Committee for the One Million-pyeong Park Construction(CCOMPC) in promoting and developing the OMP project. Since 2001 the CCOMPC has published a variety of written promotional materials to inform and educate the public about the project. In terms of approaching the promotional materials, this research focuses on the use of language on how the CCOMPC justifies the OMP project, namely the OMP justification discourse, and considers the discourse as a unique form of social document that represents the perspective of the CCOMPC in explaining the local environmental issues and values of urban parks to the public. Using a discourse analysis method, this research analyzes the justification discourses and investigates how they changed over the three main development phases of the OMP: the initiation and preliminary development phase(1999-2001.2), the development phase (2001.2-2008), and the time period after the greenbelt policy release on Dunchi Island(2008-present). In each discourse, the OMP project is rationalized as a citizen participation park project that (1) aims to enhance the quality of public green space in Busan, (2) is accompanied by various community engagement programs that emphasize the value of urban nature and environmental education to expand citizen participation, and (3) has contributed to the National Urban Park Bill. This research emphasizes the role of the discourses in helping the public gain a critical understanding about the local environment and values of urban parks. By analyzing the contents of the discourses, it explains the social learning values of the OMP expressed in the discourses.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.80-80
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• 2002

The main thesis of this article is that the decisive point of creativity education is the cultivation of critical thinking capability. Although the narrow conception of creativity as divergent thinking is not subsumed under that of critical thinking, the role of divergent thinking is not so crucial in the science context of creative problem-solving. On the contrary, the broad conception of creativity as focusing on the reference to utility and the third conception of creativity as a process based on the variation and combination of existing pieces of information are crucial in creative problem-solving context, which are yet subsumed under that of critical thinking. The emphasis on critical thinking education is connected with the characteristics of contemporary knowledge-based society. This rapidly changing society requires situation-adaptive or situation-sensitive cognitive ability, whose core is critical thinking capability. Hence, the education of critical thinking is to be centered on the learning of blowing-how and procedural knowledge but not of knowing-that and declarative knowledge. Accordingly, the learning of critical thinking is to be headed towards the cultivation of competence but not just of performance. In conclusion, when a rational problem-solving through critical and logical thinking turns out consequently to be novel, we call it creative thinking. So, creativity is an emergent property based on critical and logical thinking.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.81-90
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• 2002

The main thesis of this article is that the decisive point of creativity education is the cultivation of critical thinking capability. Although the narrow conception of creativity as divergent thinking is not subsumed under that of critical thinking, the role of divergent thinking is not so crucial in the science context of creative problem-solving. On the contrary, the broad conception of creativity as focusing on the reference to utility and the third conception of creativity as a process based on the variation and combination of existing pieces of information are crucial in creative problem-solving context, which are yet subsumed under that of critical thinking. The emphasis on critical thinking education is connected with the characteristics of contemporary knowledge-based society. This rapidly changing society requires situation-adaptive or situation-sensitive cognitive ability, whose core is critical thinking capability. Hence, the education of critical thinking is to be centered on the learning of blowing-how and procedural knowledge but not of knowing-that and declarative knowledge. Accordingly, the learning of critical thinking is to be headed towards the cultivation of competence but not just of performance. In conclusion, when a rational problem-solving through critical and logical thinking turns out consequently to be novel, we call it creative thinking. So, creativity is an emergent property based on critical and logical thinking.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.575-596
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• 2012

Students' perception on a science program for gifted was investigated. The whole program was designed in consistency and integrity based on the Autonomous Learner Model suggested by Betts & Kercher(1999). 7th, 8th and 9th grade students were enrolled in this program, offered by G Education Institute for Gifted(GEI) located in Seoul. A survey was done to ask students' perception regarding the effect of the program. The survey consisted of statements about the expected effects of the program and students were asked if they agreed with the statements. Most students strongly agreed that GEI's program has positive effects. Students replied that they learned useful and interesting science contents, enjoyed meaningful experience of cooperating with members in small groups, and were challenged by the inquiry tasks. They recognized that they were being trained to become autonomous learners. They also said that their choices and decisions were respected, which resulted in positive effects on their ability to negotiate or to inquire actively. These implies that Autonomous Learner Model had been successfully applied. Although it was not clear autonomy of students was fully grown, the possibility of becoming an autonomous learner was evident. Satisfaction level is higher for the older students, implying that the integrity in the program gave accumulating effect. Students response showed that three sub-programs of GEI, the classes of each subject, conference at the end of the year and autonomous learner training played equally important role for students to learn the process of scientific inquiry and autonomous learning. This was a positive sign that the strategies for scientific inquiry and autonomous learning were embedded and integrated deeply in the program. The results of current research suggests that the integrity of a program based on a specific education model for the gifted could provide better education environment for the gifted students.

• Hwankyungkyoyuk
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• v.18 no.3 s.28
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• pp.59-74
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• 2005

We can't deny Korean EE has basically developed on the basis of Environmental Possibilism (Environmental management or Reformism) in lots of aspects. I would show three representative proofs here, the first, the philosophy of Korean EE has been mainly focused on dichotomy of human-techno centrism and eco-centrism with no considering other alternative environmentalism since 4th Formal Curriculum, 1981. The second, simultaneously, the concept of EE has not distinguished from it of Science education. (Furthermore, unfortunately some says EE has been a part of Science education, although there should be many differences on its contextual aspect.) And the third one is that the limit of possibilism which market economists have worried, has scarcely mentioned in various kinds of EE-related teaching materials. Possibilism is basically likely to be accompanied by science and economics-oriented approach, and in this aspect this dichotomy, human-techno centrism and eco-centrism, has come from perspectives of Economical development process and over-addicted belief to Science. So it is enough to say that Korean EE has basically developed with biased to Environmental possibilism, in other words, biased to preference to it. And I'll critically focus on these two axes of possibilism, Science and Economics and its dichotomy. Of course, we should accept there are so many same parts in its contents between EE and Science, but we should know its contextual differences for triangular position of environmentalism suitable to EE and also overcome science-dependant approach to EE. Although science-dependant approach to EE and dichotomy could provide some tools for clearing up the causes of environmental problem, especially always it has insisted fundamental causes of environmental problem originated in human faults and over-use of eco-source or over-economic development, but now it is old-fashioned discourse, furthermore it come to have unavoidable limits in the debates of problem-solving mechanism to environmental problems. The paramount important thing is to supply the ways or thoughtful mechanism for solving or coordinating the Environmental problems, not just searching for cause of it. But scientific approach and its dichotomy based on possibilism have continuously born cause & effect in EE-related discourse. So there are so much needs to transfer from continuous bearing of cause & effect to constructive alternatives at least in environmentalism of EE. Traditionally, dichotomical division in EE Environmentalism, human-techno centrism and eco-centrism, couldn't have Provided any answers to our real society, it just gives us only cause & effects of Environmental problems. And also we can't find the description on the limits of capitalism market approach to Environmental problems especially in Korean EE text books, other teaching materials and its teaching-learning process, although market approach economist has been proved its fault beyond its functional merits as Environmental management tools. So we should introduce other alternative Environmental philosophy instead of Possibilism such as eco-socialism insisted by Schmacher M. and Boochin etc, or marxist-environmentalism for relative and comparative views to market-thought such as commodification. In this aspect we need to accept Oriental philosophy based on moderation(中庸) as new another alternatives with the reflection that we have recognized monism as representative Oriental philosophical environmentalism. Fundamentally monism has done its role with providing relative concepts to Dichotomy Enlightenment, but we can't say it has been core concept for understanding of oriental environmentalism, and we can't distinguish monism from oriental philosophy itself, just because oriental thought itself was basically monism. So conceptual difference should be recognized between EE and Science education in teaching-learning process on the basis of life-philosophy(Philosophie des Lebens) from epistemology. For this transformation, we should introduce existentialism in Science education, in other words, only existential Science education based on phenomenology or interpretivism can be EE. And simultaneously we need some ways for overcoming of scientific foundationalism which has been tradition making science not stand on existentialism, formulating and featuring of almost all of natural things and its phenomenon from after enlightenment in western world, but it has malfunctioned in fixing conception of science just into essentialism itself. And we also introduce integrated approach to science and society for EE like STS. Those are ways for overcoming of Environmental possibilism in EE.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Korean Journal of Food Science and Technology
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• v.51 no.2
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• pp.160-168
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• 2019

This study aimed to determine whether double-processed ginseng berry extract (PGBC) could improve learning and memory in an $A\hat{a}42$-induced Alzheimer's mouse model. Passive avoidance test (PAT) and Morris water-maze test (MWMT) were performed after mice were treated with PGBC, followed by acetylcholine (ACh) measurement and glial fibrillary acidic protein (GFAP) detection for brain damage. Furthermore, acetylcholinesterase (AChE) activity and choline acetyltransferase (ChAT) expression were analyzed using Ellman's and qPCR assays, respectively. Results demonstrated that PGBC contained a high amount of ginsenosides (Re, Rd, and Rg3), which are responsible for the clearance of $A{\hat{a}} 42$. They also helped to significantly improve PAT and MWMT performance in the $A{\hat{a}} 42-induced$ Alzheimer's mouse model when compared to the normal group. Interestingly, ACh and ChAT were remarkably upregulated and AChE activities were significantly inhibited, suggesting PGBC to be a palliative adjuvant for treating Alzheimer's disease. Altogether, PGBC was found to play a positive role in improving cognitive abilities. Thus, it could be a new alternative solution for alleviating Alzheimer's disease symptoms.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.45-54
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• 2021

This study was written to create educational content in non-destructive fields based on Mixed Reality. Currently, in the field of radiation, there is almost no content for educational Mixed Reality-based educational content. And in the field of non-destructive inspection, the working environment is poor, the number of employees is often 10 or less for each manufacturer, and the educational infrastructure is not built. There is no practical training, only practical training and safety education to convey information. To solve this, it was decided to develop non-destructive worker education content based on Mixed Reality. This content was developed based on Microsoft's HoloLens 2 HMD device. It is manufactured based on the resolution of 1280 ⁎ 720, and the resolution is different for each device, and the Side is created by aligning the Left, Right, Bottom, and TOP positions of Anchor, and the large image affects the size of Atlas. The large volume like the wallpaper and the upper part was made by replacing it with UITexture. For UI Widget Wizard, I made Label, Buttom, ScrollView, and Sprite. In this study, it is possible to provide workers with realistic educational content, enable self-directed education, and educate with 3D stereoscopic images based on reality to provide interesting and immersive education. Through the images provided in Mixed Reality, the learner can directly operate things through the interaction between the real world and the Virtual Reality, and the learner's learning efficiency can be improved. In addition, mixed reality education can play a major role in non-face-to-face learning content in the corona era, where time and place are not disturbed.