Hanjin Mao

Systematic reviews summarize the progress of studies and pave roads for future research in an academic field. However, conducting a systematic literature review can be burdensome and time-consuming. Computer-assisted methods such as text mining techniques have been increasingly applied to improve systematic reviews in public administration. To test the reliability of using text mining for systematic literature reviews, this study uses clustering, topic modeling, automatic multi-term extraction, and text network to systematically review articles published in Chinese Public Administration Review from 2002 to 2019. By comparing machine-produced topics with existing human-coded themes, findings show that applying text mining methods for systematic reviews can be reliable and effective with cautions. The study also offers practical suggestions for researchers to apply text mining methods for systematic literature reviews.

Scholars have perceived a globalization process in higher education and recognized the influx of international students, especially students from Asia, in the U.S. programs of public affairs as a factual aspect of the globalization of the academic profession. However, the studies that examine international students’ experiences and challenges in the public affairs programs have remained scarce. This article broadens the literature review to multiple disciplines in order to understand the common challenges experienced by Asian international students while it identifies the particular challenges of international students in public affairs programs. Based on the cross-discipline literature review, this article highlights the lessons that American public affairs programs can take in order to provide more accommodating and inclusive educational environments to international students. It also recognizes the missing areas in the literature about the challenges of Asian international students in American public affairs programs that future studies are expected to investigate.