• Title/Summary/Keyword: Game for learning

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An Analysis of the Writing Types Elementary School Students Presented in Mathematics Journal (초등학생의 수학 일기 쓰기 유형 분석)

  • Choi-Koh, Sang Sook;Park, Man Goo;Kim, Jeong Hyeon
    • Communications of Mathematical Education
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    • v.37 no.1
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    • pp.85-104
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    • 2023
  • The purpose of this study is to analyze the types of mathematics journals of elementary school students and to understand how they change in mathematics journals as the grade goes up, and to obtain implications in mathematics education. To this end, 170 of the 222 parish mathematics data submitted to the "Math Journal Contest" were analyzed with the consent of both minors and their parents. As for the framework for analyzing math journal types, 12 types were derived through independent analysis between three researchers. The research results showed that first, the type of math journal written by elementary school students is a variety of journals, such as observation, problem making, concept organization, and review. In addition, as a learning area, it was found that math journal showed a noticeable increase in experimental observation, problem making, and concept journal as the grades progressed, while a small number of idea journal and explanatory journals appeared. However, game (winning) strategy building and types declined. It can be seen that this is evolving from a type that requires activity-oriented or simple descriptions to a type that actively applies mathematical concepts. As such, there are 12-type of math journals, but it is necessary to actively use the teaching materials in writing that can be freely expressed in the school setting.

Analysis on the Characteristics of the IT Science tilted Students Toward Computer Learning (정보과학영재의 학습 특성 분석)

  • Kim, Eui-Jeong;Seo, Seong-Won;Baek, Soon-Heum
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2008.10a
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    • pp.491-495
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    • 2008
  • The purpose of this study is to analyze the characteristics of IT gifted middle school students. The researcher analyzed the observational assessments of 16 gifted noddle school students through 19 education programs conducted from During the latest 3 years at the Science Gifted Children Education Center. The researcher hypothesized that IT gifted children would be outstanding in computer skills and information processing abilities. But they were not much different from gifted children in the other areas. Therefore there are two suggestions resulted from the study. First, it might not be meaningful to sub-categorize the subjects because of their developmental stages. The tenth grade students observed in this study were in their formal operational period by Piaget. Therefore, it would be desirable to teach them integrated areas rather than separated areas. Second, gifted children could be excellent in most areas. Due to their curiosity, task tenacity, and intellectual abilities, they could show excellence in any areas. Therefore, it is important to elaborate the educational programs so that the gifted children can develop their abilities in each areas.

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A Methodology of Customer Churn Prediction based on Two-Dimensional Loyalty Segmentation (이차원 고객충성도 세그먼트 기반의 고객이탈예측 방법론)

  • Kim, Hyung Su;Hong, Seung Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.111-126
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    • 2020
  • Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.