• Journal of Korean Elementary Science Education
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• v.30 no.2
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• pp.232-241
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• 2011

The purpose of this study was to compare socio-linguistic characteristics and instructional influences of two different types of texts, which were narrative and expository. Socio-linguistic characteristics of two different types of texts were analyzed in their content specialization, linguistic formality, and social-pedagogic relationships. Expository texts showed strong scientific classification, and medium level of linguistic formality, and low level of social-pedagogic relationships. Narrative texts showed different characteristics. The instructional effects were investigated with 91 fifth grade elementary students in three classes. Each class was randomly assigned into three groups: expository text group, narrative text group, control group. The results showed that the science achievement scores of the narrative text group was higher than those of other groups. The affective domain test scores of the expository text group were higher than other groups. The perception of students on informational science text were generally positive both types of texts.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.359-386
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• 2010

In order to evaluate the domain-specific model and process-specific model of spatial and nonspatial working memory (WM), this study manipulated the memory load of the delayed response task and examined how the neural correlates of memory load effect was influenced by the stimulus domain (face and location) at the maintenance stage of WM using an event-related fMRI experiment. One or three face stimuli were presented as target stimuli and participants were asked to maintain the face itself (face WM) or the location of face stimuli (spatial WM). The results of recognition judgment accuracy showed no difference between face WM and spatial WM, and showed equivalent memory load effects of both WM. As a result of brian image analysis, memory load effect at maintenance stage showed that inferior, middle, and superior PFC were recruited by both face WM and spatial WM, and showed that VLPFC was the commonly activated area by both WM, supporting functional specialization of PFC by process components of WM. This study provides evidence for process-specific model in which maintenance of WM is associated with VLPFC.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.221-234
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• 2015

The purpose of this paper is to investigate the differences between successful SMEs in Korea and German hidden champions, with respect to both strategic importance and performance of major competitive dimensions, and mode of technology development. Data were collected through survey and public databases, and the sample comprises successful SMEs located in regional area in Korea. The results show that Korean hidden champions are relatively low regarding specialization of business domain and globalization while German hidden champions have positioned in global niche market. It is also found that local firms utilize technology alliances actively for technology development while German hidden champions rely heavily on internal R&D as a primary source of innovation. Based upon these results, policy directions for fostering local hidden champions were suggested.

• Journal of Gifted/Talented Education
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• v.22 no.1
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• pp.23-37
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• 2012

The purpose of this study is to design a more satisfactory and efficient teaching strategy for the gifted by comparing teaching type and learning environment preferred by the gifted with that preferred by normal students. As a result, the following findings are obtained. First, while the normal class students show higher preference for clarification and organization, gifted students prefer for diversification and specialization. Second, with the respect to the gender-related forms of mathematics classroom environment, the overall female preference and the average score are higher, indicating significant difference in the area is only a psychological domain. Third, compared to the regular classroom, the gifted have significantly different preference for teaching method, classroom and teachers' attitude between in the gifted class and regular class.

• Journal of Educational Research in Mathematics
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• v.17 no.3
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• pp.253-270
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• 2007

by A.C. Clairaut was written based on the historico-genetic principle such as his . In this paper, by analyzing his we can induce six principles that Clairaut adopted to teach algebra: necessity and curiosity as a motive of studying algebra, harmony of discovery and proof, complementarity of generalization and specialization, connection of knowledge to be learned with already known facts, semantic approaches to procedural knowledge of mathematics, reversible approach. These can be considered as strategies for teaching algebra accorded with beginner's mind. Some of them correspond with characteristics of , but the others are unique in the domain of algebra. And by comparing Clairaut's approaches with school algebra, we discuss about some mathematical subjects: setting equations in relation to problem situations, operations and signs of letters, rule of signs in multiplication, solving quadratic equations, and general relationship between roots and coefficients of equations.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.