• Journal of the Korean Chemical Society
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• v.57 no.1
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• pp.147-158
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• 2013

This study is to search an optimal model on causal relationships of the motivations to learn and motivation strategy in chemistry education. The participants in this study are consisted of G and I high schools students (487) in Gwangju. They all answered to the questionnaire. Model I is hypothesized to be path model of the mediation between 'self-efficacy, achievement need, and entity theory of intelligence' and 'learning strategy and self-handicapping tendency of motivation strategy' by goal orientation to explore variables of study effecting the motivation strategy. And Model II is hypothesized path model of the mediation between goal orientation and 'learning strategy and self-handicapping tendency' by 'self-efficacy, achievement need, and entity theory' to explore variables of study effecting the motivation strategy. Based on these models, structural equation modeling techniques are used to evaluate for the path model among goal orientation(learning, performance approach, and performance approach goal orientation), self-efficacy, achievement need, entity theory of intelligence, self-handicapping tendency, and learning strategy in chemistry education. As the results, Model II is considered. Goodness-of-fit indexes of this model related modification models are identified and analyzed in phases. And this model is accomplished by correcting the model the fifth time to enhance goodness-of-fit indexes. In this optimal model II-5 (Fig. 3) on causal relationships of the motivations to learn and learning strategy (p

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.183-190
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• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.