• Journal of Christian Education in Korea
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• v.77
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• pp.63-84
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• 2024

Research Objective : The study aimed to investigate the satisfaction with the operation of theological university curricula from the perspective of learners experiencing the theological education curriculum in the field. The goal is to provide a basis for reflective introspection on the current theological education curriculum and for restructuring it to have influential impacts within the church and society. Content and Methodology : A survey was conducted with 80 learners currently enrolled in undergraduate, graduate, master's, and doctoral programs at a theological university to analyze satisfaction with current theological education programs. To interpret the survey results progressively, in-depth interviews were conducted with a randomly selected group of 6 participants. Survey Results : First, the satisfaction with the current theological education programs was found to be 60%, indicating a high level of satisfaction. Second, while 77.5% recognized the need for practical pastoral education, only 45.5% reported that practical pastoral education is currently provided in theological education programs, indicating a lower percentage than the perceived need. Third, 73.7% responded negatively regarding whether the current theological education programs can enhance pastoral competence for future society. Lastly, the areas identified as urgently requiring change for the restructuring of theological education programs were theological education content, methodology, and objectives, in that order. Conclusion and Recommendations : In an era of great transformation, our society is changing rapidly. In the face of this wave of change, the theological education curriculum also requires adaptation to suit the new era. Traditional theological education courses have primarily focused on imparting theory-centered knowledge. However, theological education in the new era necessitates a curriculum that enhances the pastoral capacity of churches and pastors to dynamically navigate through this era of significant transition. To achieve this, it is imperative to restructure the curriculum to one that is more closely related to the pastoral field. This involves offering a variety of constructivist-based, learner-centered teaching and learning methods within a theory-centered curriculum and methodology. Additionally, it entails establishing a practice-oriented theological school that can actively address the evolving pastoral landscape in this era of great transition. Restructuring of the process is essential to meet these goals.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.