Cross-cultural Adaptation Rule Extraction Of Foreign Students

In order to explore the relationship between the foreign students’cross-cultural adaptation influencing factors and adaptive level in China, getting the information about foreign students’demographic factors, adaptive influencing factors and adaptive level through literature review, interviews and questionnaires. Using all model structural equation models, association rules, and classification and regression tree method, to analyze cross-cultural adaptation of foreign students from more than one angle. Findings enriched current international students’adaptability theory, and firstly successful applied the data mining method to the study of foreign students’ adaptability. Methodology, it’s not only a breakthrough in the field of foreign students, but also a new psychometric data analysis methods. The main conclusions are the following:1) After pre-tested, through exploratory factor analyzed and confirmatory factor analyzed to determine the various parts of the formal questionnaire including the influencing factors of adaptability and adaptive level structure. Variance analyzed of demographic factors on foreign students, the results showed as follows:Foreign students’countries cultural and economic circumstances impacted on adaptability, and the gender of the students, the time length in China and the situation of understanding China before coming to China are the main factors to affect adaptability. Studying factors also is a great impact on adaptability, main performance in getting scholarships, low tuition, liking Chinese, and want to learn Chinese.2) Used structural equation modeling to establish the model structure between influencing factors of adaptability and adaptive level of foreign students, the results showed as follows:The whole model fitting degree and explanations of the ability was meted the basic requirements. Content validity and structural reliability was relatively high, more comprehensive and systematic response relationship between the level of foreign students adaptability influencing factors and adaptability.3) Association rules introduced in the study of foreign students’cross-cultural adaptation. Used Visual C++development environment to achieve the Apriori algorithm, mining2-item sets frequent rules. Setting the minsup and the mine of as0.1and0.6, the according to the different degrees of support and confidence, carried out a total of17experiments, and the experimental results obtained524association rules. 4) Compared results of the frequent2-item sets Rules with traditional analysis methods, the results showed as follows:Frequent2-item sets association rules can be a good expression of the traditional analysis, and can even get interrelation which traditional analysis cannot show. Traditional analysis can be seen as part of the association analysis. Association rules can be effectively applied to the analysis of the psychological measurement data relationship.5) Using WEKA software to mining frequent a number of sets of rules, carried out6experiments. According to the different degrees of support and confidence, obtained different numbers of association rules. The study found that frequent a number of sets are better than the frequent2-item sets, it can get more information.6) Using the decision tree method for the study of foreign students’cross-cultural adaptation, selected the CART algorithm to classify the foreign students’adaptability. It established four CART trees model named a general adaptation, social and cultural adaptation, psychological adaptation and campus adaptation, and extracted twenty-nine classification rules.7) Compared the CART classification rules with traditional binary logistic regression analysis, and verify the performance of the two classification model through correct classification rate and ROC curve. The results showed that:Two classification models had their pros and cons, the test parameters were also very similar. The CART classification got the same factors with Logistic regression in mining the main effect factors and getting rules. They mutual authenticated that service mode, extraversion, image of teachers and learning conditions were the factor s with the best predictive effect. So the decision tree method was effective for the psychometric data classification, and the CART classification can get more variables than Logistic regression, it had great relationship with more sophisticated of the decision tree mining, which can avoid argument collinearity.