Design of Fast Mining Algorithm for Educational Sport Course Data Based on Cluster Analysis

Q4 Engineering
Jing Lin, Dan Li
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引用次数: 0

Abstract

In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.
基于聚类分析的教育体育课程数据快速挖掘算法设计
在这个大数据时代,教育研究人员正在重新认识和评估教育数据的价值。因此,我们需要利用教育数据挖掘方法进行数据分析,更好地指导教学。高校的信息化水平逐年提高,学生从入学到毕业的整个培养数据都被存储起来。这些数据集由不同部门收集、存储和保管,包含大量规律性的相关信息,真实记录了学生的成长足迹。传统的教育决策尚未充分挖掘和利用数据资源中蕴藏的宝贵信息。虽然现阶段已有部分学者开展了校园数据挖掘的相关研究,但在高校决策应用中仍有许多问题尚未解决。本文基于数据驱动决策的思想,结合校园大数据的数据特点,应用多种机器学习算法,建立了学生行为分析与行为预测的模型方案。在多类教育数据背景下分析学生学业行为表现的基础上,提出了处理多类型校园大数据的聚类分析框架,并阐述了聚类结果的群体特征。通过引入K-原型算法,有效解决了传统聚类算法(如K-Means等)无法适应教育数据属性的多类别问题。研究成果表明,基于 "数据-预测-决策 "思想创新教育决策模型和方法,推动了大数据科学在教育领域的应用研究。
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来源期刊
International Journal of High Speed Electronics and Systems
International Journal of High Speed Electronics and Systems Engineering-Electrical and Electronic Engineering
CiteScore
0.60
自引率
0.00%
发文量
22
期刊介绍: Launched in 1990, the International Journal of High Speed Electronics and Systems (IJHSES) has served graduate students and those in R&D, managerial and marketing positions by giving state-of-the-art data, and the latest research trends. Its main charter is to promote engineering education by advancing interdisciplinary science between electronics and systems and to explore high speed technology in photonics and electronics. IJHSES, a quarterly journal, continues to feature a broad coverage of topics relating to high speed or high performance devices, circuits and systems.
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