Cultural assimilator as a technology for preventing maladaptation of foreign students

Panich, Olga; Tkachenko, Nadejda; Khudaeva, M Y; Ovsyanikova, Yelena; Sckilev, Sergey; Doronina, Natalia

doi:10.1051/shsconf/20219701034

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gu and Usinger (2021) focused on international Chinese graduate students in the United States and designed experiments to explore their intercultural adaptation. Panich et al (2021) explored the maladaptation phenomenon of international students from a cultural perspective and attempted to solve this problem through cultural assimilator. Oh and Butler (2019) focused on the adaptation of international college freshmen in a foreign university from the perspective of information behavior.…”

Section: Related Workmentioning

confidence: 99%

Lost at starting line: Predicting maladaptation of university freshmen based on educational big data

Guo

Bai

Zhen

et al. 2022

Asso for Info Science & Tech

View full text Add to dashboard Cite

The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of "at-risk" students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%