# A Machine Learning Framework for Student Retention Policy Development: A Case Study
###### Abstract  
Student attrition at tertiary institutions is a global challenge with significant personal and social consequences. Early identification of students at risk of dropout is crucial for proactive and preventive intervention. This study presents a machine learning framework for predicting and visualizing students at risk of dropping out. While most previous work relies on wide-ranging data from numerous sources such as surveys, enrolment, and learning management systems, making the process complex and time-consuming, the current study uses minimal data that are readily available in any registration system. The use of minimal data simplifies the process and ensures broad applicability. Unlike most similar research, the proposed framework provides a comprehensive system that not only identifies students at risk of dropout but also groups them into meaningful clusters, enabling tailored policy generation for each cluster through digital technologies. The proposed framework comprises two stages where the first stage identifies at-risk students using a machine learning classifier, and the second stage uses interpretable AI techniques to cluster and visualize similar students for policy-making purposes. For the case study, various machine learning algorithms--including Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, Naive Bayes, Artificial Neural Network, Random Forest, Classification and Regression Trees, and Categorical Boosting--were trained for dropout prediction using data available at the end of the students' second semester. The experimental results indicated that Categorical Boosting with an F1-score of 82% is the most effective classifier for the dataset. The students identified as at risk of dropout were then clustered and a decision tree was used to visualize each cluster, enabling tailored policy-making. s 2025  
student success and reducing attrition. Student attrition can be considered at primary and secondary education levels, tertiary or higher education levels, or even in specific courses. Specifically, due to age ranges and governing regulations, dropout phenomena in tertiary education differ significantly from foundational or secondary education, both in terms of contributing factors and the available data sources. This study focuses on the prediction of student dropouts from tertiary institutions, which is defined as one of the most complex and detrimental events for both students and institutions [1]. The term dropout is used to describe the act of leaving a university without earning a degree, which is considered a type of failure. This aligns with the dropout definition by [PERSON] et al. as the withdrawal from a degree course before it is completed [2]. Hence, dropout does not include the extension of studies or temporary leave due to financial or health issues. Dropping out of school is a potentially devastating event in a student's life and also negatively impacts the university from an economic perspective [3]. According to the Education at a Glance 2022 OECD Indicators Report, only 39% of full-time students in bachelor's programs graduate within the normal duration of their degree program, 12% of students drop out prior to the second year, and 21% drop out by the end of the degree program duration [4]. Therefore, developing strategies to reduce school dropouts is globally an important goal for education systems. Student dropout is a crucial concern not only for individuals and educational institutions but also for the social and economic development of communities and countries. [PERSON] et al. identified dropouts as one of the biggest problems universities currently face [5]; [PERSON] et al. asserted that university students dropping out of school pose a significant risk for local societies and countries [6].