• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.85-103
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• 2019

This study aims to analyze the direction of the reading education programs based on the 2015 revised curriculum and to seek the plans for the school library and the teacher librarian to be able to contribute them. For this purpose, the types of school-based reading education programs in the report of 100 excellent school curriculum in 2016, which was first applied by the amended curriculum were analyzed through multiple intelligences. Upon the analysis results, the reading education programs in the schools showed to be operated with interpersonal Intelligence. Community-aligned reading was the most frequently operated in the primary schools while student reading club activities were the most common in the middle and high schools. In case of reading education program related to linguistic intelligence, the most commonly operated ways were reading books, writing with literatures, and writing book report, in primary, middle, and high schools, respectively. In case of reading education program related to spatial intelligence, media production type showed the most commonly operated in all types of schools. However, there was no reading program related to naturalist intelligence. Based on these analysis results, the plans to contribute the activation of reading education programs by school libraries under the 2015 amended curriculum were suggested in the aspects of development of connection programs with teachers, students and parents as the center of education community, installation and operation of maker spaces and enhancement of program management and inquiry-based learning competency of teacher librarians.

• The Journal of Korean Association of Computer Education
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• v.22 no.1
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• pp.87-98
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• 2019

It is easy to assume that contradictions are logically incorrect or empty sets that have no solvability. This dilemma, which can not be done, is difficult to solve because it has to solve the contradiction hidden in it. Paradoxically, therefore, contradiction resolution has been viewed as an innovative and creative problem-solving. TRIZ, which analyzes the solution of the problem from the perspective of resolving contradictions, has been used for people rather than computers. The Butterfly model, which analyzes the problem from the perspective of solving the contradiction like TRIZ, analyzed the type of contradiction problem using symbolic logic. In order to apply an appropriate concrete solution strategy for a given contradiction problems, we designed the Butterfly algorithm based on decision making tree. We also developed a visualization tool based on Python tkInter to find concrete solution strategies for given contradiction problems. In order to verify the developed tool, the third grade students of middle school learned the Butterfly algorithm, analyzed the contradiction of the wooden support, and won the grand prize at an invention contest in search of a new solution. The Butterfly algorithm developed in this paper systematically reduces the solution space of contradictory problems in the beginning of problem solving and can help solve contradiction problems without trial and errors.

• Journal of Korean Library and Information Science Society
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• v.50 no.2
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• pp.149-168
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• 2019

The purpose of this study is to analyze the characteristics of teacher librarians' efficacy and to propose some ways to enhance their efficacy. To do this, questionnaires on 30 teacher librarians who participated in the level 1 certification program of K university in 2018 were conducted. The results showed that their average efficacy was 3.38, the efficacy of teaching method was 3.60, the collective efficacy was 3.38, and the personal efficacy was 3.18. They had high personal efficacy on classroom management, the willingness to lead poor students and the possibility of problem student guidance, and collective efficacy on conflict management with fellow teachers and parents. On the other hand, personal efficacy in problem analysis and guidance for problem students, difficult contents and course instruction were low. Also, the collective efficacy of the conflict between the manager and the education office was low. They have a strong willingness to improve teaching methods for students and showed high efficacy about student synchronization and preparation for teaching. However, they were aware of the lack of learning skills and the lack of various teaching methods. The variables influencing their efficacy were graduation, education level, school size, and degree. Especially, the higher the education level, the more confident and enthusiastic about teaching problemmatic students and disadvantaged students. In addition, teacher librarians with high academic standards showed high confidence in conflict resolution with peers and parents and teaching methods. The improvement direction to enhance their efficacy in this study are increasing the ratio of teacher education in the field of education, reforming teacher librarians training before appointment, establishing supervision organizations for school libraries and improving their professionalism by going to graduate school.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• Education of Primary School Mathematics
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• v.25 no.2
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• pp.179-195
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• 2022

Mathematical communication competency, one of the six mathematical competencies emphasized in the latest mathematics curriculum, plays an important role both as a means and as a goal for students to learn mathematics. Therefore, it is meaningful to find instructional methods to improve students' mathematical communication competency and analyze their communication competency in detail. Given this background, this study analyzed 64 sixth graders' mathematical communication competency after they participated in the lessons of fraction division emphasizing mathematical communication. A written assessment for this study was developed with a focus on the four sub-elements of mathematical communication (i.e., understanding mathematical representations, developing and transforming mathematical representations, representing one's ideas, and understanding others' ideas). The results of this study showed that students could understand and represent the principle of fraction division in various mathematical representations. The students were more proficient in representing their ideas with mathematical expressions and solving them than doing with visual models. They could use appropriate mathematical terms and symbols in representing their ideas and understanding others' ideas. This paper closes with some implications on how to foster students' mathematical communication competency while teaching elementary mathematics.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.5-24
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• 2023

In ancient Egypt, temples were not only religious sanctuaries but also community centers. One of the core spaces created in the temple is the facility where priests and scribes copied and preserved texts on papyrus and other media. Its common designation was 'pr-mḏȝ'(House of Books) and the 'per-(nw)-seshw'(House of Scrolls). The general term used during that time was 'Per Ankh', and the modern term for it is 'temple library'. Therefore, this study first identified the character and identity of the Per Ankh attached to the temple, and then traced whether it is appropriate to designate 'healing place of the souls' depicted on the hypostyle hall(Per Ankh) in the Ramesseum(mortuary temple) built by Ramses II of the New Kingdom as a library. As a result, Per Ankh, a hieroglyph combining the Per(house) and Ankh(life), was revealed to be a multi-purpose complex facility consisting of a learning and research center, a treatment and healing center with medical facilities and sanatoriums, a religious ceremony and a center for the celebration of eternal life, a scriptorium and a library. Therefore, the traditional argument that Per Ankh refers to a library cannot be justified. In the same context, the inscription 'Ψυχῆς ἰατρεῖον' on the doorplate of the hypostyle hall of the Ramesseum, which was first introduced by Greek historian Hecataeus of Miletus in the 4th century BC, was translated into Latin as 'Psychēs Iatreion' by Diodorus Siculus in the 1st century BC and described as the motto of the sacred library. However, Psyche is the goddess of Greek and Roman mythology, and Iatreion means hospital(clinic, healing center) and pharmacy, so Per Ankh in the Ramesseum is a space to heal the soul of the pharaoh (Ka). Therefore, 'Psychēs Iatreion = library' is a distortion and a mistranslation. It is not the motto of the library, but a metaphor for the Per Ankh.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.832-847
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• 2007

The purpose of the study was to analyze and interpret parent-child dyadic discourses in depth with emphases on the ZPD system in a museum, an informal learning setting. Second graders and their parents from Seoul and its environs were voluntarily participated in the study. Data were collected from the museum documents, the photos of exhibits, and the video recordings of dyadic discourses at and between exhibits. The documents and the photos were analyzed to investigate what the topics, medium and goals of the exhibits were. The video recordings were all transcribed and analyzed to understand what and how they talked to each other through the lens of the ZPD system; situation definition, intersubjectivity, and semiotic mediation, The results of the study consisted of two parts. First, it showed that parent-child dyadic discourses were categorized in four: (1) within the actual developmental level; (2) in the zone of proximal development; (3) toward the potential developmental level; and (4) out of developmental level. The most common categories were the dyadic discourses within the actual developmental level and in the zone of proxima I development. Second, the representative cases in each categories were described and interpreted to understand the nature of parent-child dyadic discourses. It can be concluded that we gained some important understandings of an intrinsic attribute of parent-child discourses in a museum, an informal learning setting. Based on the results of the study, it can be suggested that museums make efforts to cultivate the affordance of exhibit environment to promote visitor's learning.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.768-775
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• 2005

Lentinus edodes powder was added at 1-3%(w/w) to improve functional properties of jocheong. Content of crude protein, ash, crude lipids, total mineral, free sugar and reducing sugar increased with increasing amount of L. edodes powder, while viscosity and solid and carbohydrate contents decreased. Through amino acid analysis, 17 amino acids were identified and quantified, glutamic acid being the major amino acid. No significant differences were observed in fatty acid composition and pH between control and L. edodes powder-added jocheong. Addition of mushroom powder in jocheong decreased lightness, yellowness and redness in Hunter's color value. Sensor score of jucheong containing 1% of L. edodes powder was similar to that of control. Results showed jocheong containing less than 2% L. edodes powder gave highest scores in quality characteristics and sensory evaluation.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.