Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

본 연구의 목적은 지구과학 교사 연구 모임에 참여하고 있는 교사의 야외 지질 학습에 관한 인식과 실행을 조사하는 것이다. 연구 참여자는 은대리 지역의 야외 지질 학습에 지도 교사로 참여한 지구과학 교사 4명이다. 자료 수집은 야외 지질 학습 수업 관찰 및 녹화, 학생들과 교사들을 대상으로 한 반구조화된 심층면담, 야외 지질 학습 수업 자료 및 학생 보고서 등을 통해 이루어졌으며, 야외 지질 학습 수업 및 면담 자료를 전사한 후 질적 분석을 하였다. 연구 결과에 의하면 연구 참여 교사들은 대체로 야외 지질 학습의 목적으로 심미적인 측면을 중요시하고 있었고, 실제 연구 참여 교사들의 야외 수업에서 학생들이 과학적 탐구에 참여할 수 있는 다양한 교수 방법과 전략이 활용되고 있었지만, 각 관찰 지점에서의 제한된 시간 등으로 인하여 학생들의 활동보다는 교사의 설명이 더 많은 야외 강의의 측면이 보였다. 연구 참여 교사들은 교사 연구 모임과 야외 지질 답사 참가, 대학원 진학 등을 통해 야외 지질 학습 지도를 위한 전문성 개발에 계속적인 노력을 해오고 있었다.