Using Survival Analysis to Identify Populations of Learners at Risk of Withdrawal: Conceptualization and Impact of Demographics




course withdrawal, demographics, distance education and online learning, dropout, intervention design, survival analysis


High dropout rates constitute a major concern for higher education institutions, due to their economic and academic impact. The problem is particularly relevant for institutions offering online courses, where withdrawal ratios are reported to be higher. Both the impact and these high rates motivate the implementation of interventions oriented to reduce course withdrawal and overall institutional dropout. In this paper, we address the identification of populations of learners at risk of withdrawing from higher education online courses. This identification is oriented to design interventions and is carried out using survival analysis. We demonstrate that the method’s longitudinal approach is particularly suited for this purpose and provides a clear view of risk differences among learner populations. Additionally, the method quantifies the impact of underlying factors, either alone or in combination. Our practical implementation used an open dataset provided by The Open University. It includes data from more than 30,000 students enrolled in different courses. We conclude that low-income students and those who report a disability comprise risk groups and are thus feasible intervention targets. The survival curves also reveal differences among courses and show the detrimental effect of early dropout on low-income students, worsened throughout the course for disabled students. Intervention strategies are proposed as a result of these findings. Extending the entire refund period and giving greater academic support to students who report disability are two proposed strategies for reducing course withdrawal.

Author Biographies

Juan Antonio Martínez-Carrascal, Universitat Oberta de Catalunya

Juan Antonio Martínez-Carrascal is a PhD candidate at the Universitat Oberta de Catalunya (UOC, Barcelona, Spain). He carries out his research within the Learning Analytics for Innovation and Knowledge Application in Higher Education (LAIKA) group. He focuses on the impact of technology in education and in particular on the influence of students’ characteristics and their activity on academic performance. He is also an associate professor at the Universitat Autònoma de Barcelona (UAB, Barcelona, Spain).

Martin Hlosta, Institute for Research in Open, Distance and eLearning, Swiss Distance University of Applied Sciences

Martin Hlosta is a research fellow at the Institute for Distance Learning and eLearning Research (IFeL), working on projects for adaptive learning in education. Before that, he led OUAnalyse at the Open University, a project improving student retention via machine learning, which was selected in 2020 by UNESCO as one of the four best projects using artificial intelligence in education.

Research focus: predictive learning analytics, learning analytics for equity in education, scaling up and impact of learning analytics.

Teresa Sancho-Vinuesa, Universitat Oberta de Catalunya

Teresa Sancho is full professor at Universitat Oberta de Catalunya (UOC) in Barcelona, Spain, where she teaches mathematics for engineers and conducts research on e-assessment, feedback and learning analytics as head of the LAIKA (Learning Analytics for Innovation and Knowledge Application in Higher Education) Group. Currently, she is the academic director of the Applied Data Science Degree. She was a visiting professor at the Open University UK (2015), University of Southampton (2018) and Cardiff University (2019).
Born in Barcelona, Spain, Dr. Sancho received the Mathematics degree from Universitat de Barcelona (UB) in 1990 and a Ph.D. degree in Electronic Engineering from Universitat Ramon Llull (URL) in 1995.
Dr. Sancho taught numerical analysis and the theory of probabilities and stochastic processes at the La Salle School of Engineering, where for six years she co-ordinated a research group on numerical methods to solve problems in fluid mechanics and electromagnetism. She was a member of the pedagogical and editorial team of the department of didactic material at Enciclopedia Catalana, S.A., Barcelona, Spain, before joining Universitat Oberta de Catalunya in 1998 where she has been involved in several positions: Academic coordinator of the Ph. D. Programme in Information and Knowledge Society, Research Director and Vicerector in Research and Innovation.
Teresa Sancho has been involved in several research and innovation projects concerning the Internet and Higher Education. In particular, she has been involved in the Catalonia Internet Project, an interdisciplinary research project on the information society in Catalonia, co-directed by professors Manuel Castells and Imma Tubella. She has been the coordinator of the MOOC Programme UCATx and CIRAX Programme, both launched by the Catalan Government in 2013. She is currently concentrating her research efforts in the use of learning analytics for the improvement of online education and learning, and more particularly in the evaluation and feedback processes related to mathematics subjects.
She has participated in over 15 technical programme committees and has been reviewer in several academic journals. Dr. Sancho has authored over 75 academic journal and conference papers, as well as writing two books and several chapters of books.


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How to Cite

Martínez-Carrascal, J. A., Hlosta, M., & Sancho-Vinuesa, T. (2023). Using Survival Analysis to Identify Populations of Learners at Risk of Withdrawal: Conceptualization and Impact of Demographics. The International Review of Research in Open and Distributed Learning, 24(1), 1–21.



Research Articles