基于深度学习卷积神经网络(CNN)的学生毕业预测模型

A. Salam, Junta Zeniarja, Dhevan Muhamad Anthareza
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引用次数: 4

摘要

学生是大学生命周期中最重要的一部分。与同一学年获得的学生人数相比,一所大学毕业的学生人数往往占很小的比例。这种低学生毕业率可以归因于多种因素,包括丰富的学生活动,以及经济和其他方面的考虑。这就需要一个模型的存在,这个模型可以决定学生是否能够按时毕业。学生按时毕业是决定一所大学的最重要因素之一。在相同的比例下,一所大学的新生水平越高,按时毕业的学生就越多。如果在所有注册学生中有许多学生没有按时毕业,那么学生数据和学术数据的数量就会增加。因此,大学的形象和声誉将受到影响,潜在地危及大学的认证价值。为了解决这个问题,我们需要一个可以预测学生毕业的模型,然后用来为政策决策提供信息。本研究的目标是提出一个使用卷积神经网络(CNN)算法来预测学生毕业的深度学习分类模型。采用CNN算法建立的分类模型准确率高达87.44%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Student Graduation Prediction Model using Deep Learning Convolutional Neural Network (CNN)
Students are the most important part of a university's life cycle. When compared to the number of students obtained in the same academic year, the number of students graduating from a university often has a small ratio. This low student graduation rate can be attributed to a variety of factors, including the abundance of student activities, as well as economic and other considerations. This necessitates the existence of a model that can determine whether or not a student will be able to graduate on time. Student graduation on time is one of the most important factors in determining a university. With the same ratio, the higher the level of new students at a university, the more students who graduate on time. If many students do not graduate on time from all registered students, the number of student data and academic data increases. As a result, the university's profile and reputation will suffer, potentially jeopardizing the university's accreditation value. To address this, we need a model that can predict student graduation and then be used to inform policy decisions. The goal of this research is to propose a Deep Learning classification model that uses the Convolutional Neural Network (CNN) algorithm to predict student graduation. The classification model with the CNN algorithm produced a high accuracy value of 87.44 %.
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