J.C. Hapugoda . M.R. Sooriyarachchi

Department of Organizational Studies, The Open University of Sri Lanka, Email: jchap@ou.ac.lk, Department of Statistics, University of Colombo, Email: roshini@stat.cmb.ac.lk

Received in final form on Jan 22, 2022

Abstract
When data with correlated responses are available, joint models may provide interesting and improved results than modelling the responses separately. Such models between those responses can be developed and their applicability in various fields is noteworthy. Though joint mixed models and joint population averaged models are popular and common in statistical literature, Joint Marginalized Multilevel Models (JMMM) are still a developing area. Thus, the main objective of the study is to model survival and count data jointly, utilizing MMM and applying it to the data related to Distance Education in Sri Lanka. The data obtained for this study represents records of the students who have registered for undergraduate study program in Management at a leading higher education institute in Sri Lanka through Open and Distance Learning (ODL), which conducts the program in all the regional/ study centres across the country. As the students are clustered in different regional/ study centres, the clustering effect is also present in the dataset. In this study, completion time of study programs by the students is considered as a survival response and the number of first-time passes by students, which represents the student performance, is considered as the count variable. The findings suggest that the time to completion of study program and gender have a significant impact on study program completion and student performance in the said context.

Keywords
Joint modelling, Marginalized multilevel model, Distance education, Study program completion and student performance.

Cite This Article
J.C. Hapugoda and M.R. Sooriyarachchi, Joint Marginalized Multilevel Model for Study Program Completion and Performance of Students: The Case of Sri Lankan Open and Distance Learners in Management Studies, J. Innovation Sciences and Sustainable Technologies, 2(1)(2022), 41-48. https://doie.org/10.0608/JISST.2022643922

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