学术潜能测试中线性同余方法与随机数乘法的实现

Akbar Idaman, Roslina, Rika Rosnelly
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引用次数: 2

摘要

APT(学术潜力测试)是一项旨在衡量一个人在学术领域的总体能力的测试。在APT考试的实施中,它是在新生的录取和在线申请中进行的,使用基于网站的申请,每个准新生将获得一个登录帐户,可以在预定的时间同时参加APT考试。而这个过程可以通过互联网在任何地方访问。APT考试的实施并不总是一帆风顺,事实上,几乎每次进行APT考试都会出现问题,问题的出现是因为所给出的问题在做工上没有差异,从而导致APT考试结果不纯粹和准确。为了克服APT考试实施中不断出现的问题,需要一种算法或方法来随机化APT考试中的问题。在本研究中,线性同余(LCM)和乘法随机数生成器(Multiplicative Random Number Generator, Multiplicative RNG)方法是用于随机化APT考试问题的随机方法,以便APT考试问题包可以有不同的问题位置和问题包之间,并且将比较应用该方法的结果,以衡量每种方法的随机化的复杂程度。通过使用LCM模型,问题的复杂程度增加到100%,而使用MRNG方法,问题的复杂程度增加到50%。
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Implementation of Linear Congruent Methods and Multiplication Random Numbers for Academic Potential Tests
APT (Academic Potential Test) is a test that aims to measure a person's ability in the academic field in general. In the implementation of the APT exam, it is carried out in the admission of new students and its application online, using a website-based application, each prospective new student will be given a login account to take the APT exam simultaneously and at a predetermined time. While the process can be accessed anywhere with an internet network. The implementation of the APT exam does not always run smoothly or well, in fact almost every time the APT exam is carried out there are problems, problems that arise because the questions given do not have differences in workmanship which causes the APT exam results to be impure and accurate. To overcome the problems that continue to occur in the implementation of the APT exam, an algorithm or method is needed that can randomize the questions in the APT exam. In this study, the Linear Congruent (LCM) and Multiplicative Random Number Generator (Multiplicative RNG) methods are random methods that are applied to randomize the APT exam questions so that the APT exam question packages can have different question positions and between question packages and the results of the application of this method will be compared to measures how complex the randomization is for each method. By using the LCM model the level of complexity of the questions increases to 100% while by using the MRNG method the level of complexity of the questions increases to 50%.  
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