Indicators to prevent university drop-out and delayed graduation: an Italian case

Author:

Bussu AnnaORCID,Detotto ClaudioORCID,Serra Laura

Abstract

Purpose Research on the association between individual characteristics of undergraduate students, drop-out and delayed graduation is still evolving. Therefore, further evidence is required. The paper aims to discuss this issue. Design/methodology/approach This paper reports on an empirical study examining the relationship between students’ individual characteristics and delayed graduation. The analysis is based on a sample of 1,167 students who have registered on and have completed a full-time undergraduate programme in Italy. Using a Probit model, the findings document the individual, background and environmental indicators that play a role in explaining delayed graduation. Findings The study observes that students who commute to university perform better than those residing on campus. Other factors increasing the probability of completing the undergraduate programme on time include individual characteristics (e.g. gender and age), student background (family income, education), institutional environment (teaching and research quality) and student satisfaction. Finally, some policy implications are discussed. Social implications A direct policy implication of these findings is that supporting academic staff in order to enhance their performance in both research and teaching has a positive effect on the performance of the students. Originality/value This paper contributes to the debate on the impact of institutional quality on students’ performance, aiming to address the question of balance between teaching and research orientation.

Publisher

Emerald

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Complexity of the Academic System: Retention and Dropout;Multiple Systems;2024

2. Newly Proposed Student Performance Indicators Based on Learning Analytics for Continuous Monitoring in Learning Management Systems;International Journal of Online and Biomedical Engineering (iJOE);2023-08-16

3. Artificial Neural Network with Learning Analytics for Student Performance Prediction in Online Learning Environment;International Conference on Advanced Intelligent Systems for Sustainable Development;2023

4. Dropout in Higher Education and Determinant Factors;Proceedings of Seventh International Congress on Information and Communication Technology;2022-07-12

5. Drop-Out Decisions in a Cohort of Italian Universities;Teaching, Research and Academic Careers;2022

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