• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.765-780
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• 2018

The purpose of this study is to synthesize the theoretical perspectives, research methods, and research results of teachers' professional vision by reviewing and analyzing previous research papers and to suggest implications for science teacher education and research. Three databases were used to search peer reviewed journal articles published between 1997-2017, which include 'teachers' and 'professional vision' explicitly in abstracts and empirical studies only. 21 articles in total were analyzed and review results are as follows. First, researchers regarded professional vision as a new concept of teacher professionalism. Previous research viewed professional vision as integrated structure of teachers' knowledge or ability activated at specific moment. Second, the analytical framework of professional vision included two aspects; 'selective attention' and 'reasoning'. Several aspects of lessons or the desirable teaching and learning factors are suggested as the subcategories of selective attention. Hierarchical levels or independent reasoning ability factors are suggested as the subcategories of reasoning process. Third, research on teachers' professional vision focused more on middle school teachers than elementary teachers and on various subject areas. Most studies used video clips and more cases of using videos of non-participants were found. In case of measurement of professional vision, most quantitative scoring methods were whether the responses of experts and teachers on video clips were consistent. Last, most studies examined or assessed teachers' professional vision. It is reported that in-service teachers' professional vision was evaluated higher than novice teachers' and using video clips were effective to examine and improve teachers' professional vision.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.