| Summary: | 碩士 === 樹德科技大學 === 經營管理研究所 === 100 === In recent years, the declining birth rate has raised concerns in Taiwan. However, factors such as the repeal of martial law, Taiwan’s open policy toward China (and vice versa), rapid changes in society, and liberalization of international trade, have increased the speed of movement for the economic activity population. Furthermore, the deregulation of tourism and international marriage brokers have led to an increase in marriages with foreign spouses and children of mixed descent. All aforementioned factors have contributed to the creation of a new group within Taiwan’s population, thus affecting government policies as well as social, economic, and educational systems in Taiwan and becoming a point of focus in society. Children of marriages including a foreign parent have faced many challenges in adaptation to a new life, education, and cultural and ethnic identity; therefore, appropriate coping or response programs are critical in preventing such children from becoming burdens to society as they progress in age and enter society. Relevant bureaus of the government should place due emphasis on the potential problems of education and life counseling for children of a foreign parent from the perspectives of administrative planning and distribution of educational resources. An accurate and effective forecasting model should be created for arranging annual budgets, effectively employing resources, and planning stipends and counseling. Based on predicting the number of newly enrolling primary school students who are children of a foreign parent, this study proposes a precise forecasting method, leading to greater comprehension of changing trends in student numbers. Thus, the proposed method can assist the government in designing and implementing policies based on the demands of the children of a foreign parent group and expectations from society.
The author used Grey System Theory, an Artificial Neural Network (ANN) approach, and multiple regression analysis as research methods, and Artificial Intelligence (AI) and traditional statistics were adopted for forecasting. Because of a lack of official statistical data for children of a foreign parent and a shortage of relevant literature, choosing variables for this research was especially difficult. The author finalized the research variables by collecting and analyzing data from multiple sources, and then used the three aforementioned forecasting models to compare and analyze the test results and extract the best forecasting model for the changes and trends of enrollment of the targeted group. The result of this study enables administrative and education departments to design policies for children of a foreign parent.
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