• 국제교류와 융합교육
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• 제4권1호
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• pp.85-111
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• 2024

핀란드 교육은 2001년 PISA 발표 이래 계속 한국 교육의 경쟁자 또는 지향점이었다. 그러나 최근 우리 교육계에 나타나고 있는 분열과 대립, 그리고 불행의 지표들을 보면, 핀란드의 행복교육과는 거리가 너무도 멀다. 이런 배경에서 본 연구의 목적은 한국 학술지에 나타난 핀란드 교육관련 연구동향을 분석하는 것으로, RISS에서 논문 제목에 핀란드와 교육이 들어간 논문 160편을 대상으로 네트워크 텍스트 분석을 실시하였다. 주요 결과는 다음과 같다. 첫째, 핀란드 교육에 대한 연구는 점진적으로 증가하고 있다. 그러나 최근 감소세를 보였다. 둘째, 연구주제는 대부분 미시적이었으며 연구방법은 문헌연구 위주였다. 셋째, 소수의 연구자가 전체 연구의 1/3을 출판했다. 넷째, 핀란드와 함께 비교된 나라는 일본, 미국, 영국, 호주, 싱가포르 등 주로 신자유주의적 국가였다. 다섯째, 연구 주제와 대상이 주로 초중등, 수학·과학 등 PISA의 영향권에 있었다. 향후 핀란드 교육관련 연구에서는 지엽적이고 부분적인 영역에 대한 연구를 넘어 종합적인 관점에서 핀란드의 행복교육을 이루어온 과정과 역사에 대한 체제적 연구를 통해 우리가 배워야 할 시사점을 도출하는 후속 연구가 필요하다. 특히, 문헌연구 위주의 방법을 벗어나 핀란드 교육 커뮤니티와 온라인으로 연계하여 실제 교사, 학부모, 학생 그리고 지역협의회와 정부 관계자들이 함께 논의하고 연구하는 국제공동논의의 장을 만드는 것도 고려되어야 할 것이다.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.