• The Journal of Korean Association of Computer Education
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• v.19 no.6
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• pp.55-68
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• 2016

Informatics curriculum revised 2015 proposed the use of block type and text type of programming language by organizing problem solving and the programming unit in a spiral. The purpose of this study is to find out whether the algorithms helps programming learning and whether there is a positive transition effect in block type programming learning to text type programming trailing learning. For 15 elementary school students was conducted block type and text type programming learning. As a result of the research, it is confirmed that writing the algorithm in a limited way can interfere with the learner's expression of thinking, but the block type programming learning has a positive transition to the text type programming learning. This study is meaningful that it suggested a plan for the programming education which is sequential from elementary school.

• Journal of architectural history
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• v.13 no.4 s.40
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• pp.7-18
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• 2004

From the results of an examination of the transition process of the site plan divided into 5 stages based on literature and materials relating to the Sangju Confucian School as well as the construction history, we can see the general transition flow as follows. The arrangement form of Sangju Confucian School shows the structures with both the sacrificial rites function and the learning function in the early period. This shows a large general flow where the form with the learning function structure at the front and sacrificial rites function structure at the back changed to a form where the learning function structure was positioned behind the boarding facilities, after which there was a transformation which left only the learning function (the form where the learning function structure was positioned in front of the boarding facilities). The type where the learning function structure is positioned in front of the boarding facilities is hard to find in the Yeongnam area, also, there are not many examples of the 2 story Myeonglyundang (hall of confucianism teachings) throughout the country Sangju Confucian School which possess the value of rarity is appraised as being a precious material showing another area characteristic in Sangju of the Yeongnam area. Also, during the late Chosun period the scale of the Dongseojae (boarding facility) was reduced and the appearance of Yangsajae can be said to be a typical example of confucian school constructions of late Chosun era.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.11-19
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• 1997

Students' protocols obtained from think-aloud interviews were analyzed in the aspects of the success at first two problem-solving stages (understanding and planning), the time to complete a problem, the time at each problem-solving stage, the number of transition, and the transition rate. These were compared in the aspects of the context of problem, the success in solving problem, students' logical reasoning ability, spatial ability, and learning approach. The results were as follows:1. Students tended to spend more time in everyday contexts than in scientific contexts, especially at the stages of understanding and reviewing. The transition rate during solving a problem in everyday contexts was greater than that in scientific contexts. 2. Unsuccessful students spent more time at the stage of understanding, but successful students spent more time at the stage of planning. 3. Students' logical reasoning ability, as measured with the Group Assessment of Logical Thinking, was significantly correlated with the success in solving problem. Concrete-operational students spent more time in completing a problem, especially understanding the problem. 4. Students' spatial ability, as measured with the Purdue Visualization of Rotations Test and the Find A Shape Puzzle, was significantly correlated with their abilities to understand a problem and to plan for its solution. 5. Students' learning approach, as measured with the Questionnaire on Approaches to Learning and Studying, was not significantly correlated with the success in solving problem. However, the students in deep approach had more transitions and greater transition rates than the students in surface approach.

• Journal of the Korean School Mathematics Society
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• v.9 no.3
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• pp.265-286
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• 2006

Given that less attention has been paid to teachers than students in mathematics education, this study attempted to provide theoretical foundations to understand better mathematics teacher learning and teaching practice in transition. First, this paper summarized three conceptions of teacher learning on the basis of the relationships of knowledge and practice followed by several implications to mathematics teacher education. Second, this paper provided a brief overview of cognition as situated, social, and distributed. This paper then explored new implications and issues about mathematics teacher learning that the overview brought to light. It is expected for teacher educators and researchers to participate in rich discussion of many implicit issues about teacher learning that this paper begins to raise.

• Korean Journal of Materials Research
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• v.34 no.7
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• pp.363-369
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• 2024

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module 'Matminer', 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the 'Fe' compositional ratio. The feature importance method provided by 'scikit-learn' was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.739-743
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• 2008

In this paper, we introduce a method for automatically generating accompaniment music, according to user's input melody. The initial accompaniment chord is generated by analyzing user's input melody. Then next chords are generated continuously based on markov chain probability table in which transition probabilities of each chord are defined. The probability table is learned according to reinforcement learning mechanism using sample data of existing music. Also during playing accompaniment, the probability table is learned and refined using reward values obtained in each status to improve the behavior of playing the chord in real-time. The similarity between user's input melody and each chord is calculated using pitch class histogram. Using our method, accompaniment chords harmonized with user's melody can be generated automatically in real-time.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1819-1826
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• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.21-34
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• 2016

In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.612-615
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• 2018

The undergraduate structure based on flip learning should be a necessary course to cultivate value creation capability based on students' problem solving capability through the change of university education in the fourth industrial revolution era. Introduction and spread of Flipping Learning combining project-based learning with MOOC is requied. As the introduction and spread of AI-based learning consulting (E-Advisor), which is becoming increasingly advanced, the transition to "personalized education" that meets the 4th Industrial Revolution should be made.