The Application of Neural Network Forecasting on the Scores of College Entrance Exam – A Study of a Kaohsiung High School

碩士 === 義守大學 === 工業管理學系 === 103 === The primary goal of most students who have chosen attending the high school is to find an ideal university for their continuous studies after graduation. Students who are graduating from public and private senior high schools or those who have equivalent qualificat...

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Bibliographic Details
Main Authors: Chao-Ai Yang, 楊朝艾
Other Authors: Pei-Tsang Wu
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/2ts8p3
Description
Summary:碩士 === 義守大學 === 工業管理學系 === 103 === The primary goal of most students who have chosen attending the high school is to find an ideal university for their continuous studies after graduation. Students who are graduating from public and private senior high schools or those who have equivalent qualification can take either one or both the college entrance exams including "subject ability exam” and “designated course exam" organized by the college entrance exam center. The allocation of scores obtained from these two exams is usually hard to allocate. Therefore, building a forecasting model with good prediction is helpful for these students. The purpose of this study is to investigate the relationship between the scores obtained from three simulation tests of senior high school students and the scores obtained from college entrance exam of such students. By using the back propagation neural network forecasting, the objective of this study is to find out the suitable score allocations of the college entrance exam results for the senior high school students. In this study, a collection of three senior simulation tests results and corresponding subjective ability tests from a public high school in Kaohsiung for academic years 2013, 2014 and 2015 was investigated. The test results included the total scores for each simulation test and five indivisual scores for different courses such as Chinese literature, English, mathematics, nature science and social science. These tests results were designed to be the inputs of the neural network training. Another set of collections were the results for the subjevt ability exam from the same students. These tests results were desigeded to be the outputs of the neural network training. After training the back propagation neural networks, the best prediction of corresponding network was selected to be the prediction model for our research. The performances of both total scores of three simulation tests and individual scores of three simulation tests were investigated. From the results, we can conclude that (1) the performance of using total scores of the simulation tests results to predict the subject ability exam scores is much better than the performace of using individual scores of the simulation tests results, (2) the forecasting performance of using individual tests scores for social science is the most accuracte among the five courses, (3) the forecasting performance of using individual tests scores for mathematics is worse than other courses.