Undergraduate International Student Enrollment Forecasting Model: An Application of Time Series Analysis

Abstract

This study developed statistical models to forecast international undergraduate student enrollment at a Midwest university. The authors constructed a SARIMA (Seasonal Autoregressive Integrated Moving Average) model with input variables to estimate future enrollment. The SARIMA model reflected enrollment patterns by semester through highlighting seasonality. Further, authors added input variables such as visa policy changes, the rapid increase of Chinese undergraduate enrollment, and tuition rate into the model estimation. The visa policy change and the increase of Chinese undergraduate enrollment were significant predictors of international undergraduate enrollment. The effect of tuition rates was significant but minimal in magnitude. Findings of this study generate significant implications for policy, enrollment management, and student services for international students.

The First Year Acculturation: A Longitudinal Study on Acculturative Stress and Adjustment among the First Year International College Students

“…However, we see this as unlikely. The primary changes to U.S. higher education for international students revolve around the 2016 election of Donald Trump and the accompanying increase in visa scrutiny (Chen et al, 2019;Hefner-Babb & Khoshlessan, 2018). Practical challenges (such as securing legal immigration status) have impacted international student recruitment since the post-9/11 period (Johnson, 2018), a trend that has continued since 2016 (Chen et al, 2019;Hefner-Babb & Khoshlessan, 2018).…”

“…The primary changes to U.S. higher education for international students revolve around the 2016 election of Donald Trump and the accompanying increase in visa scrutiny (Chen et al, 2019;Hefner-Babb & Khoshlessan, 2018). Practical challenges (such as securing legal immigration status) have impacted international student recruitment since the post-9/11 period (Johnson, 2018), a trend that has continued since 2016 (Chen et al, 2019;Hefner-Babb & Khoshlessan, 2018). While increasing legal barriers have a serious impact on international higher education, they do not relate directly to our dependent or independent variables; in our review of the literature, we found no evidence that post-2016 visa policy changes impact acculturative stress, self-esteem, English proficiency, social connectedness, or homesickness.…”

The Decision-Making Process in Access Paths to Master’s Degree Studies: The Case of International Students in Spain

“…Chen and Zimitat [54], for instance, found differences among Taiwanese students depending on whether they travelled to universities in the United States or Australia: For those who chose Australia, their perception of the country's higher education was more important, while for those opting for the USA, the influence of family and social context weighed more heavily. The importance of these last two factors in the development of a student's mobility project has been confirmed by many research studies [26,28,49,55].…”

“…Research has shown how the historical link between countries, a colonial past, or a common language [13][14][15][16][17][18][19], a deficit in training provision or the perception of the quality of training in home countries [20][21][22][23][24][25], as well as the prestige and/or reputation of host institutions [13,[26][27][28][29][30] often wrongly represented by international rankings, encourage (or maybe promote) mobility flows and the choice of certain destinations when pursuing a degree abroad.…”

Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs)

“…The model also requires multiple time series variables and can be challenging to estimate. Without the proper expertise to implement such a complex model, it may not be a feasible option (Chen, Li & Hagedorn, 2019). Not only that, but the Seasonal Hybrid also ESD (S-H-ESD) model requires additional features or variables to make accurate predictions.…”