Predicting the Enrollments in Humanities and STEM Programs in Higher Education Using ARIMAX Models

IF 0.3 Q4 EDUCATION & EDUCATIONAL RESEARCH

International Journal of Online Pedagogy and Course Design Pub Date : 2022-10-01 DOI:10.4018/ijopcd.311435

Dian-Fu Chang, Wenhau Zhu, Shu-Jing Wu

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引用次数: 0

Abstract

Traditionally, the participation patterns in the humanities and STEM (science, technology, engineering, and mathematics) programs in higher education differ. This study aimed to tackle this issue using concurrent time series data sets in the expanding higher education system. Authors selected the higher education system in Taiwan as an example. The participation in the humanities and STEM programs, covering 71 periods from 1950-2020, were collected from the Ministry of Education in Taiwan. The authors applied CCF (cross-correlation function) and ARIMAX (multivariable autoregressive integrated moving average) models to select the fittest model to predict the future trend. The humanities was the input variable and STEM was the output variable in the model. The findings revealed that ARIMAX (1,2,1) works well for these target data sets. According to the findings, enrollment in STEM programs will decrease with the decline in humanities programs in the future. This finding may provide useful information for related policy makers.

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利用ARIMAX模型预测高等教育人文学科和STEM项目的入学人数

传统上，高等教育中人文学科和STEM(科学、技术、工程和数学)课程的参与模式是不同的。本研究旨在利用并行时间序列数据集在不断扩大的高等教育系统中解决这一问题。笔者以台湾高等教育体制为例。从1950年到2020年的71个时期，人文和STEM项目的参与情况由台湾教育部收集。采用交叉相关函数(cross-correlation function, CCF)和多变量自回归综合移动平均(ARIMAX, multi - variable autoregressive integrated moving average)模型选择最适合的模型来预测未来趋势。人文学科是模型的输入变量，STEM是输出变量。结果表明，ARIMAX(1,2,1)对这些目标数据集效果良好。根据调查结果，未来STEM课程的入学人数将随着人文课程的减少而减少。这一发现可能为相关决策者提供有用的信息。

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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来源期刊

International Journal of Online Pedagogy and Course Design EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

1.40

自引率

0.00%

发文量

期刊介绍： The mission of the International Journal of Online Pedagogy and Course Design (IJOPCD) is to provide a platform for the latest research, analysis, and development of online education, effective online teaching methods, and course design. IJOPCD covers the pedagogical design aspects of science education and computing education, as well as courses supported by educational technologies. Targeting academic researchers and educators who work in the field, this journal focuses on the importance of developments in online course design and teaching methods to improve teachers’ teaching and students’ learning. Researchers are encouraged to submit cross-disciplinary, high-quality syntheses that are interesting, beneficial, and apprehensible to all those interested in or teaching science and related disciplines. Topics to be discussed in this journal include (but are not limited to) the following: -Adoption of e-learning -Best practices in computing education -Best practices in science education -Blended learning -Computer-mediated communication -E-learning -Emerging technologies -Evaluation of learning technology systems -Evaluation of online learning effects -Learning management systems -Multimedia and interactive learning systems -Online course design -Online learners’ behavior -Pedagogy and teaching with technology -Virtual reality environments -Web-based teaching methods