Predicting dropout in Higher Education across borders

References

• Aina, Carmen. 2013. “Parental Background and University Dropout in Italy.” Higher Education 65 (4): 437–56. doi:10.1007/s10734-012-9554-z.
   Web of Science ®Google Scholar
• Allen, Jeff, Steven B. Robbins, Alex Casillas, and In-Sue Oh. 2008. “Third-Year College Retention and Transfer: Effects of Academic Performance, Motivation, and Social Connectedness.” Research in Higher Education 49 (7): 647–64. doi:10.1007/s11162-008-9098-3.
   Web of Science ®Google Scholar
• Aparicio-Chueca, Pilar., et al. 2021. “Beyond University Dropout. An Approach to University Transfer.” Studies in Higher Education 46 (3): 473–84. doi:10.1080/03075079.2019.1640671.
   Web of Science ®Google Scholar
• Avci, Süleyman. 2022. “Investigation of the Individual Characteristics That Predict Academic Resilience.” International Journal of Contemporary Educational Research, doi:10.33200/ijcer.1076091.
   Google Scholar
• Cannistrà, Marta, Chiara Masci, Francesca Ieva, Tommaso Agasisti, and Anna Maria Paganoni. 2022. “Early-Predicting Dropout of University Students: An Application of Innovative Multilevel Machine Learning and Statistical Techniques.” Studies in Higher Education, 1935–56. doi:10.1080/03075079.2021.2018415.
   Web of Science ®Google Scholar
• Cope, Robert G. 1978. “Why Students Stay, Why They Leave.” New Directions for Student Services 1978 (3): 1–11. doi:10.1002/ss.37119780303.
   Google Scholar
• Cunha, Flavio, and James Heckman. 2007. The Technology of Skill Formation, w12840. Cambridge, MA: National Bureau of Economic Research.
   Google Scholar
• Delen, Dursun. 2010. “A Comparative Analysis of Machine Learning Techniques for Student Retention Management.” Decision Support Systems 49 (4): 498–506. doi:10.1016/j.dss.2010.06.003.
   Web of Science ®Google Scholar
• European Commision, Education, Audiovisual and Culture Executive Agency. Eurydice. 2020. The European Higher Education Area in 2020: Bologna Process Implementation Report. Luxembourg: Publications Office. https://doi.org/10.2797/756192.
   Google Scholar
• Fernandez-Sainz, A., J. D. García-Merino, and S. Urionabarrenetxea. 2016. “Has the Bologna Process Been Worthwhile? An Analysis of the Learning Society-Adapted Outcome Index Through Quantile Regression.” Studies in Higher Education 41 (9): 1579–94. doi:10.1080/03075079.2014.988703.
   Web of Science ®Google Scholar
• Greenwell, Brandon M. 2017. “Pdp: An R Package for Constructing Partial Dependence Plots.” The R Journal 9 (1): 421. doi:10.32614/RJ-2017-016.
   Google Scholar
• Guiffrida, Douglas A. 2006. “Toward a Cultural Advancement of Tinto’s Theory.” The Review of Higher Education 29 (4): 451–72. doi:10.1353/rhe.2006.0031.
   Web of Science ®Google Scholar
• Gujarati, Damodar N. 2003. Basic Econometrics. 4th ed. Boston, MA: McGraw Hill.
   Google Scholar
• Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning. In Springer Series in Statistics, 587–604. New York, NY: Springer.
   Google Scholar
• Huang, Shaobo, and Ning Fang. 2013. “Predicting Student Academic Performance in an Engineering Dynamics Course: A Comparison of Four Types of Predictive Mathematical Models.” Computers & Education 61 (February): 133–45. doi:10.1016/j.compedu.2012.08.015.
   Google Scholar
• Ishitani, Terry T., and Lee D. Flood. 2018. “Student Transfer-Out Behavior at Four-Year Institutions.” Research in Higher Education 59 (7): 825–46. doi:10.1007/s11162-017-9489-4.
   Web of Science ®Google Scholar
• Jia, Pengfei, and Tim Maloney. 2015. “Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes.” Higher Education 70 (1): 127–49. doi:10.1007/s10734-014-9829-7.
   Web of Science ®Google Scholar
• Korhonen, Vesa, and Juhani Rautopuro. 2019. “Identifying Problematic Study Progression and “At-Risk” Students in Higher Education in Finland.” Scandinavian Journal of Educational Research 63 (7): 1056–69. doi:10.1080/00313831.2018.1476407.
   Web of Science ®Google Scholar
• Maldonado, Sebastián, Jaime Miranda, Diego Olaya, Jonathan Vásquez, and Wouter Verbeke. 2021. “Redefining Profit Metrics for Boosting Student Retention in Higher Education.” Decision Support Systems 143 (April): 113493. doi:10.1016/j.dss.2021.113493.
   Google Scholar
• Mare, Robert D. 1980. “Social Background and School Continuation Decisions.” Journal of the American Statistical Association 75 (370): 295–305. doi:10.1080/01621459.1980.10477466.
   Web of Science ®Google Scholar
• Mayhew, Matthew J., Alyssa N. Rockenbach, Nicholas A. Bowman, Tricia A. Seifert, Gregory C. Wolniak, and Ernest T. Pascarella. 2016. “21st Century Evidence That Higher Education Works.” In How College Affects Students, edited by Ernest T. Pascarella and Patrick T. Terenzini. 2nd ed., 3, 361–420. San Francisco: Jossey-Bass.
   Google Scholar
• OECD. 2022. “Education at a Glance 2022: OECD Indicators.” Education at a Glance. OECD. doi:10.1787/3197152b-en.
   Google Scholar
• Opazo, Diego, Sebastián Moreno, Eduardo Álvarez-Miranda, and Jordi Pereira. 2021. “Analysis of First-Year University Student Dropout Through Machine Learning Models: A Comparison Between Universities.” Mathematics 9 (20): 2599. doi:10.3390/math9202599.
   Web of Science ®Google Scholar
• Pellagatti, Massimo, Chiara Masci, Francesca Ieva, and Anna M. Paganoni. 2021. “Generalized Mixed-Effects Random Forest: A Flexible Approach to Predict University Student Dropout.” Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (3): 241–57. doi:10.1002/sam.11505.
   Web of Science ®Google Scholar
• Peng, Pu, Winson Fuzun Yang, Yueheng Liu, Shubao Chen, Yunfei Wang, Qian Yang, Xin Wang, et al. 2022. “High Prevalence and Risk Factors of Dropout Intention among Chinese Medical Postgraduates.” Medical Education Online 27 (1): 2058866. doi:10.1080/10872981.2022.2058866.
   PubMed Web of Science ®Google Scholar
• Rodríguez-Gómez, David. 2015. “Understanding Catalan University Dropout from a Cross-National Approach.” Studies in Higher Education 40 (4): 690–703. doi:10.1080/03075079.2013.842966.
   Web of Science ®Google Scholar
• Segura, Marina, Jorge Mello, and Adolfo Hernández. 2022. “Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?” Mathematics 10 (18): 3359. doi:10.3390/math10183359.
   Web of Science ®Google Scholar
• Seidel, Ewa, and Salah Kutieleh. 2017. “Using Predictive Analytics to Target and Improve First Year Student Attrition.” Australian Journal of Education 61 (2): 200–18. doi:10.1177/0004944117712310.
   Web of Science ®Google Scholar
• Sneyers, Eline, and Kristof De Witte. 2017. “The Interaction Between Dropout, Graduation Rates and Quality Ratings in Universities.” Journal of the Operational Research Society 68 (4): 416–30. doi:10.1057/jors.2016.15.
   Web of Science ®Google Scholar
• Sorensen, Lucy C. 2019. “‘Big Data’ in Educational Administration: An Application for Predicting School Dropout Risk.” Educational Administration Quarterly 55 (3): 404–46. doi:10.1177/0013161X18799439.
   Web of Science ®Google Scholar
• Sothan, Seng. 2019. “The Determinants of Academic Performance: Evidence from a Cambodian University.” Studies in Higher Education 44 (11): 2096–111. doi:10.1080/03075079.2018.1496408.
   Web of Science ®Google Scholar
• Spady, William G. 1970. “Dropouts from Higher Education: An Interdisciplinary Review and Synthesis.” Interchange 1 (1): 64–85. doi:10.1007/BF02214313.
   Google Scholar
• Tafreschi, Darjusch, and Petra Thiemann. 2016. “Doing It Twice, Getting It Right? The Effects of Grade Retention and Course Repetition in Higher Education.” Economics of Education Review 55 (December): 198–219. doi:10.1016/j.econedurev.2016.10.003.
   Google Scholar
• Tieben, Nicole. 2020. “Non-Completion, Transfer, and Dropout of Traditional and Non-Traditional Students in Germany.” Research in Higher Education 61 (1): 117–41. doi:10.1007/s11162-019-09553-z.
   Web of Science ®Google Scholar
• Tinto, Vincent. 1975. “Dropout from Higher Education: A Theoretical Synthesis of Recent Research.” Review of Educational Research 45 (1): 89–125. doi:10.3102/00346543045001089.
   Web of Science ®Google Scholar
• Tinto, Vincent. 1993. Leaving College: Rethinking the Causes and Cures of Student Attrition. 2nd ed., 4 vols. Chicago, IL: University of Chicago Press.
   Google Scholar
• Tinto, Vincent. 2017. “Through the Eyes of Students.” Journal of College Student Retention: Research, Theory & Practice 19 (3): 254–69. doi:10.1177/1521025115621917.
   Google Scholar
• Von Hippel, T. Paul, and Alvaro Hofflinger. 2021. “The Data Revolution Comes to Higher Education: Identifying Students at Risk of Dropout in Chile.” Journal of Higher Education Policy and Management 43 (1): 2–23. doi:10.1080/1360080X.2020.1739800.
   PubMed Web of Science ®Google Scholar
• Wainipitapong, Sorawit, and Mayteewat Chiddaycha. 2022. “Assessment of Dropout Rates in the Preclinical Years and Contributing Factors: A Study on One Thai Medical School.” BMC Medical Education 22 (1): 461. doi:10.1186/s12909-022-03527-z.
   PubMed Web of Science ®Google Scholar
• Yair, Gad, Nir Rotem, and Elad Shustak. 2020. “The Riddle of the Existential Dropout: Lessons from an Institutional Study of Student Attrition.” European Journal of Higher Education 10 (4): 436–53. doi:10.1080/21568235.2020.1718518.
   Google Scholar