学生学业成绩与大学预科考试成绩的关系分析

Chong Qi, Sabariah Binti Saharan
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摘要

关于影响学生毕业年级成绩的因素,其中包括学生的入学资格,还存在很多不确定性。本文研究了入学资格和大学前 CGPA 对大学阶段学生成绩的影响。入学资格对于教育机构或教育提供者确保毕业生质量至关重要。本研究旨在分析和比较理学士(工业统计学)和荣誉学士(BWQ)学生的成绩。本研究选取了马来西亚敦侯赛因大学应用科学与技术学院(FAST)的 54 名学生。这些学生来自马来西亚中学毕业会考(STPM)和马来西亚大学预科课程。通过配对 t 检验和 Z 检验来分析大学前 CGPA 和每学期 GPA 的影响,以及入学资格对最后一年成绩的影响。使用分类和回归树(CART)、K-最近邻和 Naïve Bayes 来开发和预测学生的成绩。研究结果表明,上一学期的成绩与下一学期的成绩没有关系。同时,在每学期平均积点(GPA)、大学前平均积点(CGPA)和最终平均积点(CGPA)方面,大马高等教育文凭考试(STPM)的学生优于预科生。K-Nearest Neighbors 和 Naïve Bayes 模型是预测学生成绩最有效的数据挖掘技术,准确率高达 100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis on Students’ Academic Performance in Relation to the Results of Pre-University Examination
There is a great deal of uncertainty regarding the factors that influence their final year grade, which includes their entry qualification. This paper investigates the impact of entry qualification and pre-university CGPA on student performance at the university level. Entry qualifications are critical for educational institutions or educational providers to ensure the quality of the graduates. The goal of this study is to analyze and compare performance of Bachelor of Science (Industrial Statistics) with Honours (BWQ) students. Total of 54 students were selected form the Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia (UTHM). The students are coming from Malaysian Higher School Certificate (STPM) and Malaysian Matriculation Programme. Paired t test and Z test were carried out to analyze the impact of pre-university’s CGPA and each semester’s GPA as well as impact of entry qualification towards their final year grade. Classification and Regression Tree (CART), K-Nearest Neighbors and Naïve Bayes were used to develop and predict the students’ performance. The findings show that there is no relation between the result obtained from previous semester towards the next semester. Meanwhile, students from STPM outperform Matriculation in terms of their GPA per semester, pre-university CGPA as well as their final CGPA. The K-Nearest Neighbors and Naïve Bayes models have been documented as the most efficient data mining techniques in predicting student performance with the highest percentage of accuracy of 100%.
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