Supporting students throughout their academic trajectory is widely regarded as a core responsibility of higher education institutions. Despite growing diversity in student backgrounds, employment patterns, and life circumstances, many European higher education systems continue to operate under a model in which academic progression is treated predominantly as the responsibility of the individual student [1]. Students who deviate from linear study trajectories are frequently left to their own devices, particularly in the later phases of their studies. Institutional support structures are usually concentrated in the first semesters [2]. However, little attention has been paid to disengagement processes occurring in the late phases of study. This study reports first findings on an understudied student population: Near-completion students. These students have successfully completed the majority of a bachelor’s curriculum (150 ECTS or more), but have disengaged from university, and are typically inactive over several semesters up to years and even decades. At the same time, they continue to re-enrol each semester, despite incurring a tuition fee, indicating an intention to graduate, but not taking the final steps to do so. Because they do not officially drop out, near-completion students usually remain invisible to institutions. This raises important questions about how universities can counteract disengagement at late stages of study. Losing these students this close to graduation is certainly a net loss, as both, the concerned students as well as the universities have invested considerable time, effort, and resources for them to reach this point of their studies [3] The research is done as part of Project 150+, which aims to understand the situation of near-completion students and develop evidence-based approaches to support them. This study addresses two central research questions: RQ1. What reasons do near-completion students report for their prolonged inactivity? RQ2. Do these reasons differ between study programmes? Students at five different faculties of a technical university indicated reasons for their prolonged inactivity in a survey. A total of 158 respondents selected multiple responses from a set of reasons derived from the literature, as well as the options “Other reason” and “Degree no longer needed.” As the selected reasons are nested within participants and thus not statistically independent, generalised estimating equations (GEE; [4) were used to model whether specific reasons were selected more frequently by students from different study programmes. The results show that some reasons for prolonged near-completion are significantly more prevalent than others, most notably employment. However, the interaction terms between selected reasons and study programme were not statistically significant. This indicates that the reasons underlying prolonged near-completion are not specific to particular study programmes, but are largely shared across faculties at this technical university. These findings carry important implications for universities seeking to re-engage students late in the student life cycle. Rather than requiring interventions that are tailor-cut to specific study programmes, the results suggest that broad support measures may be suitable across different technical study programmes. By shedding light on reasons for disengagement late in the student lifecycle, this study contributes to discussions on institutional responsibility, and support in higher education. If higher education institutions aim to foster trust and meaningful engagement in their students, support must extend beyond the entry phase and address the realities of non-linear student trajectories. [1] Haas, C., & Hadjar, A. (2024). Social Inequalities in Study Trajectories: A Comparison of the United States and Germany. Sociology of Education, 97(3), 276–296. https://doi.org/10.1177/00380407241228553 [2] Eather, N., Mavilidi, M. F., Sharp, H., & Parkes, R. (2022). Programmes targeting student retention/success and satisfaction/experience in higher education: A systematic review. Journal of Higher Education Policy and Management, 44(3), 223–239. https://doi.org/10.1080/1360080X.2021.2021600 [3] Ragni, A., Masci, C., & Paganoni, A. M. (2025). Analysis ofHigher Education Dropouts Dynamics Through Functional Decomposition ofRecurrent Events onTime-to-Event Processes. In A. Pollice & P. Mariani (Eds), Methodological and Applied Statistics and Demography IV (pp. 461–466). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64447-4_78 [4] Højsgaard, S. ., Halekoh, U., & Yan, J. (2005). The R Package geepack for Generalized Estimating Equations. Journal of Statistical Software, 15(2), 1–11. https://doi.org/10.18637/jss.v015.i02