• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.8-16
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• 2013

Determining relative importance among many quality attributes under financial constraints is an important task. The weighted value of an attribute particularly in QFD, will influence on engineering characteristics and this will eventually influence the whole manufacturing process such as parts deployment, process planning, and production planning. Several scholars have suggested weighting formulas using CSC (Customer Satisfaction Coefficient) in the Kano model. However, previous research shows that the validity of the CSC approaches has not been proved systematically. The aim of the present study is to address drawbacks of CSC and to develop APC (Average Potential Coefficient), a new approach for weighting of quality attributes. For this, the current study investigated 33 quality attributes of e-learning and conducted a survey of 375 university students for the results of APC, the Kano model, and the direct importance of the quality attributes. The results show that the proposed APC is better than other approaches based on the correlation analysis with the results of direct importance. An analysis of e-leaning's quality perceptions using the Kano model and suggestions for improving e-learning's service quality are also included in this study.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.879-894
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• 2003

We evaluated the efficiencies of applying attribute selection methods and prior discretization to supervised learning, modelled by C4.5 and Naive Bayes. Three databases were obtained from UCI data archive, which consisted of continuous attributes except for one decision attribute. Four methods were used for attribute selection : MDI, ReliefF, Gain Ratio and Consistency-based method. MDI and ReliefF can be used for both continuous and discrete attributes, but the other two methods can be used only for discrete attributes. Discretization was performed using the Fayyad and Irani method. To investigate the effect of noise included in the database, noises were introduced into the data sets up to the extents of 10 or 20%, and then the data, including those either containing the noises or not, were processed through the steps of attribute selection, discretization and classification. The results of this study indicate that classification of the data based on selected attributes yields higher accuracy than in the case of classifying the full data set, and prior discretization does not lower the accuracy.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.358-365
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• 2016

We propose a novel application of random forest, a machine learning-based general classification algorithm, to analyze the influence of design attributes on the silicon-to-SPICE (S2S) gap. To improve modeling accuracy, we introduce magnification of learning data as well as randomization for the counting of design attributes to be used for each tree in the forest. From the automatically generated decision trees, we can extract the so-called importance and impact indices, which identify the most significant design attributes determining the S2S gap. We apply the proposed method to actual silicon data, and observe that the identified design attributes show a clear trend in the S2S gap. We finally unveil 10nm key fin-shaped field effect transistor (FinFET) structures that result in a large S2S gap using the measurement data from 10nm test vehicles specialized for model-hardware correlation.

• Korean journal of medical education
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• v.30 no.4
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• pp.339-346
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• 2018

Purpose: The purpose of this study was to examine whether non-cognitive student attributes such as learning style and personality type affected academic performance in a flipped learning classroom of a pre-dental undergraduate science course. Methods: 'Biodiversity and Global Environment,' a 15-week, 3-credit course, was designed as a flipped class in Seoul National University School of Dentistry in 2017. Second-year pre-dental students were required to enroll in the course and to engage in online learning and in-class discussion. The Kolb's Learning Style Inventory and the Myers-Briggs Type Indicator were conducted to measure non-cognitive student factors. Independent samples t-test and multivariate regression analyses were used to examine the relationships between self-rated measurements and academic achievement. Results: More than half of the students enrolled in the flipped science course had an assimilator learning style (50%), followed by convergers (24%), accommodators (16%), and divergers (10%), and their personality types were dominated by the introverted, sensing, thinking, and judging types, respectively. Examining group differences using the t-test demonstrated a significant relationship between the diverger group and higher academic success. In particular, the multivariate regression analysis indicated that both thinking types and female students performed better in discussion than feeling types and male students. Conclusion: To operate the flipped learning classroom more effectively in medical and dental education, the instructor should carefully develop and apply a more tailored facilitation and relevant assessment by considering student learning styles and personality types.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.59-65
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• 2008

Product usability consists of many attributes such as learnability, efficiency, memorability, and so on. In particular, learnability is one of the most important attributes in product usability. Therefore, many people consider the primary criterion for a good user interface to be the degree to which it is easy to learn. Learnability represents the degree of how much can easily learn the usage of a product. It concerns the features of the interactive system that allow novice users to understand how to use it initially and then how to attain a maximal level of performance. In this study, we studied on the application of learning curve to evaluate product learnability. In order to validate the applicability, we carried out simple experiment using mobile phone. We got task completion times through the experiment and predicted the times using learning curve model. And then, we compared prediction times to task completion times. Finally, we identified that learning curve could apply to predict and compare product learnability.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.192-201
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• 2020

This study commercialization of 5G will enable transformation of university education. This study identifies five attributes (device type, learning place, learning content, learning field and expense payment) and corresponding levels to study the impact of 5G in the future of university education. The attributes and the levels are then combined into few 5G education service alternatives for respondents to rank. 102 students ranked the alternatives based on their preferences and intent to use. Results indicate that the intent to use 5G-based education service was high with 86% and the most important factor was expense payment (37%), followed by learning field (26%), learning content (24%), device type (8%) and learning place (5%). Specifically, students preferred smart device, practical and experiential content, ubiquitous (no limitation of space and time) learning, practical education and free rate when adopting 5G-based education service. These will provide implications to accelerate adoption of and exploitation of 5G for innovating university education.

• Management & Information Systems Review
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• v.27
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• pp.71-99
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• 2008

This study investigates the relationships between three BSC communication attributes(support of organizational culture, message valid, and knowledge sharing) and organizational learning and how that translates into relationship organizational performance in public organization. In this paper, first, past studies on BSC communication and organizational learning that identify the attributes of effective communication and organizational learning in organizational performance are reviewed. Second, a research model, key variables, and three hypotheses tested by PLS(partial least squares) are presented. The data was collected from BSC champions and managers of 53 public organizations in Korea. The results indicate, first, BSC communication (except for support of organizational culture) have not significant related to organizational performance. Therefore, H1 was not supported. Second, the structural path coefficient between support of organizational culture and message valid and organizational learning are statistically significant and in the hypothesized direction. But the knowledge sharing has not significant relationship with organizational learning. Therefore, H2 was partially supported. Third, organizational learning was significantly positively related to organizational performance. H3 was supported. Finally, organizational learning play a significantly positive role in mediating the relationship between BSC communication and organizational performance. The theoretical contributions, limitations, as well as future research directions are discussed at the end of the paper.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• Korean Journal of Organic Agriculture
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• v.21 no.2
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• pp.125-137
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• 2013

The Purpose of this paper is to estimates consumers' willingness to pay for Urban Agriculture Park attributes. we analysis the marginal WTP by choice experiment method. Choice experiment (CE) is designed to elicit the marginal WTP differences among urban agriculture park attributes (garden scales, learning and experience area, leisure and relaxation area and fund). The results of multinomial logit model are meaningful, the total marginal WTP on the urban agriculture park attributes is 18,852 won. gardens scales is 2,949 won, learning and experience area is 11,284 won, leisure and relaxation area is 4,619 won. In the current laws, the facilities related to urban agriculture park is not. Taking advantage of the new urban agriculture park, Amendments of the law is required.