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International Journal of Engineering and Management Sciences
Regisztráció Aktuális szám Megjelenés előtti cikkek Archívum Indexáltság Beküldés Kapcsolat Keresés
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IJEMS

Évf. 10 szám 3 (2025)
Menedzsmenttudományok

Innovative Strategies and Student Academic Performance: Machine Learning Insights on International Students in Chinese Universities

Megjelent:
2025-07-09
DOI Linkhttps://doi.org/10.21791/IJEMS.2025.14.
Szerzők
Hassan Waseem,
Hassan Mehreen,
Sheraz Maryam,
Zhu Yali,
Samreen,
Ali Mustafa
Megtekintés
PDF (Angol)
Kulcsszavak
COVID-19 Innovative strategies Student Academic Performance Global Environmental Emergency Learning Outcomes Digital Media Technology Machine Learning
Licenc

Copyright (c) 2025 Waseem Hassan, Mehreen Hassan, Maryam Sheraz, Yali Zhu, Samreen, Mustafa Ali

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Hogyan hivatkozzuk
Kiválasztott formátum: APA
Hassan, W., Hassan, M., Sheraz, M., Zhu, Y., Samreen, & Ali, M. (2025). Innovative Strategies and Student Academic Performance: Machine Learning Insights on International Students in Chinese Universities. International Journal of Engineering and Management Sciences, 10(3), 37-60. https://doi.org/10.21791/IJEMS.2025.14.
Beküldött 2025-04-01
Elfogadott 2025-07-08
Publikált 2025-07-09
Absztrakt

The higher education sector in China has faced unprecedented challenges recently due to the global COVID-19 pandemic. The influx of international students, a vital component of the nation's academic landscape, presented distinct challenges, including maintaining academic achievements through various online platforms, which necessitated innovative strategies to ensure that their educational pursuits remained rewarding despite these challenges. This study aims to explore the innovative strategies adopted by Chinese higher education institutions in response to the COVID-19 pandemic and examine their impact on the academic achievements of international students. This study employs a comprehensive approach that incorporates questionnaire surveys and dominant Machine Learning Algorithms, such as Multiple Linear Regression (MLR), Decision Tree Model (DTM), Support Vector Regression Model (SVRM), and K-nearest neighbors (KNN). By employing rigorous data-gathering approaches, our study aimed to address a set of particular questions: How did these innovative strategies improve students' academic performance in the face of environmental emergencies? To what extent did international students benefit from these adaptations? Through investigation of these concerns, our research provides insight into the effectiveness of these strategies and their possible significance for future educational methodologies. Innovative strategies positively correlated with student academic performance during the COVID-19 pandemic in Chinese higher Education. This research highlights how overcoming these challenges can have broader implications for shaping resilient global education systems in future crises. The study accurately predicted academic performance, highlighting the importance of innovative teaching approaches in the context of the COVID-19 pandemic. This study might influence educational policies and practices. Educational institutions can make informed decisions about emergency preparedness and development by assessing results using a creative approach. Our findings bring depth to the global conversation on higher Education under challenging circumstances, showing how Innovation might alleviate the adverse impacts on international students' learning experiences.

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