Predicting Student Retention and Success in Higher Education

About this Research Topic

Background

Student retention and success remain persistent global challenges in higher education, with direct implications for institutional quality, resource allocation, and social equity. While many factors influence dropout or progression, quantitative approaches have enabled deeper insights into patterns of academic persistence and completion. Over the past two decades, predictive analytics - fueled by advances in educational data mining and statistical modeling - have become increasingly prominent in identifying at-risk students and forecasting academic outcomes. However, much of the literature remains context-specific, methodologically diverse, and fragmented across disciplines. This Research Topic seeks to consolidate quantitative research efforts that examine and predict retention (continuous enrolment) and student success (course, program, or qualification completion) through robust, generalizable methodologies.

This Research Topic aims to advance the theoretical and empirical understanding of student retention and success in higher education by focusing on rigorous, data-driven approaches. Despite institutional efforts to enhance throughput and reduce attrition, many interventions remain reactive or insufficiently informed by predictive evidence. This gap underscores the need for robust statistical and machine learning models that can identify significant predictors of persistence and success early in the academic journey. We aim to gather high-quality contributions that develop or validate predictive models using diverse quantitative methods—ranging from logistic regression, structural equation modeling, and survival analysis, to more contemporary approaches such as artificial intelligence and learning analytics. The goal is to encourage comparative, cross-institutional, and context-sensitive research that not only enhances predictive accuracy but also informs early warning systems, targeted interventions, and inclusive retention policies. This research topic will support the development of replicable, ethically sound, and policy-relevant knowledge to improve student outcomes globally.

We invite original quantitative research articles, systematic reviews, data reports, and methodological papers focused on predicting student retention (defined as re-registration across academic periods) and student success (defined as completion of courses, programs, or qualifications). Submissions may address, but are not limited to:

• Development and validation of predictive models;
• Identification of key academic, behavioral, psychosocial, or demographic predictors;
• Institutional applications of predictive analytics;
• Comparative studies across higher education systems;
• Evaluation of early-warning systems and predictive dashboards;
• Methodological innovations in modeling retention/success.

Priority will be given to contributions that employ rigorous quantitative techniques, are grounded in sound theoretical frameworks, and demonstrate practical implications for higher education policy and practice. Manuscripts should provide sufficient detail to ensure reproducibility and transparency in methods and findings.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Registered Report
• Review
• Study Protocol
• Systematic Review

Keywords: Student retention, Student success, Predictive analytics, Quantitative, Higher Education

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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