The application of data mining techniques for predicting education to new undergraduate students at Chiang Mai Rajabhat University

2023 IEEE International Conference on Cybernetics and Innovations (ICCI) Pub Date : 2023-03-30 DOI:10.1109/ICCI57424.2023.10112233

Sancha Panpaeng, Phattharamon Phanphaeng, Jirang Kumnuanta, Phuttachat Yommakit, Khomson Kocento, Primprai Wongchompoo

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Abstract

This study aimed to predict the number of new students enrolled in bachelor's degree programs using data mining techniques. The population and sample consisted of 31,918 applicants for undergraduate studies at Chiang Mai Rajabhat University only for the regular semester in the quota applied and screened for the year 2014-2019. There were two components in the tool used in this study: hardware and software. that uses RapidMiner Studio 9 to analyze data with data mining. The association rules technique of the Apriori model was used to identify the association rules of recruitment behavior in each field of applicants in each group. both general and quota The results of the analysis evaluated the support-based correlation rule model. confidence and an increase from a total of 340 correlation rules considering support = 0.01, confidence = 95%, and a significant addition of more than 1 correlation rule 89.

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数据挖掘技术在清迈拉贾哈特大学本科新生教育预测中的应用

本研究旨在利用数据挖掘技术预测新入学的学士学位课程的学生数量。总体和样本包括31918名申请清迈拉贾哈特大学本科学习的申请人，仅在2014-2019年申请和筛选的配额中申请常规学期。本研究中使用的工具有两个组成部分:硬件和软件。它使用RapidMiner Studio 9来分析数据挖掘。利用Apriori模型的关联规则技术，识别出每组应聘者在各个领域的招聘行为关联规则。分析结果对基于支持的关联规则模型进行了评价。考虑支持度= 0.01，置信度= 95%，显著增加1条以上相关规则89条，置信度和增加总数为340条相关规则。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2023 IEEE International Conference on Cybernetics and Innovations (ICCI)

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