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The Influence of Refugee Students’ Personal Characteristics on Study Success in Online Education

Author

Listed:
  • F. Reinhardt

    (Johannes Gutenberg University)

  • T. Deribo

    (Johannes Gutenberg University)

  • O. Zlatkin-Troitschanskaia

    (Johannes Gutenberg University)

  • R. Happ

    (Johannes Gutenberg University)

  • S. Nell-Müller

    (Johannes Gutenberg University)

Abstract

There is little research on the study success factors of refugee students in higher education. One approach to meeting the growing global demands is to provide online education specifically for refugees. This study examines specific personal characteristics of refugee students and their influence on success and retention in online education. Individual factors such as intrinsic motivation and language proficiency, cognitive functioning, and sociodemographic factors such as gender and country of residence influence retention of refugee students during online studies. The results indicate that sociodemographic factors (e.g., gender), cognitive factors (e.g., English proficiency), and external factors (e.g., country of residence) have a significant influence on study retention on refugee students.

Suggested Citation

  • F. Reinhardt & T. Deribo & O. Zlatkin-Troitschanskaia & R. Happ & S. Nell-Müller, 2021. "The Influence of Refugee Students’ Personal Characteristics on Study Success in Online Education," Journal of International Migration and Integration, Springer, vol. 22(3), pages 987-1008, September.
    • Handle: RePEc:spr:joimai:v:22:y:2021:i:3:d:10.1007_s12134-020-00775-0
    DOI: 10.1007/s12134-020-00775-0
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    1. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    2. ., 2007. "Bilateralism and intellectual property rights," Chapters, in: International Economic Law and the Digital Divide, chapter 9, Edward Elgar Publishing.
    3. Datzberger, Simone & Le Mat, Marielle L.J., 2018. "Just add women and stir?," International Journal of Educational Development, Elsevier, vol. 59(C), pages 61-69.
    4. Thierry Volery & Deborah Lord, 2000. "Critical success factors in online education," Post-Print hal-02311851, HAL.
    5. Charissa W. S. Wong & Robert D. Schweitzer & Nigar G. Khawaja, 2018. "Individual, Pre-Migration, and Post-Settlement Factors in Predicting Academic Success of Adolescents from Refugee Backgrounds: a 12-Month Follow-Up," Journal of International Migration and Integration, Springer, vol. 19(4), pages 1095-1117, November.
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