Article detail · 2011
Predicting Student Attrition with Data Mining Methods
Journal
Journal of College Student Retention Research Theory & Practice- Year
- 2011
- Type
- article
Data source split
- YÖKSİS venue Journal of College Student Retention Research Theory & Practice
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using 8 years of institutional data along with three popular data mining techniques, we developed analytical models to predict freshmen student attrition. Of the three model types (artificial neural networks, decision trees, and logistic regression), artificial neural networks performed the best, with an 81% overall prediction accuracy on the holdout sample. The variable importance analysis of the models revealed that the educational and financial variables are the most important among the predictors used in this study.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
125 citations
OpenAlex cited_by_count (cache / database)
10 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition 2014
- A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition 2013
- Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition 2020
- Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition 2020
- Eğitimde Yeni Bir Paradigma: “YükseköğretimdeYapay Zekâ” 2020
- Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework 2023
- Generative AI-enhanced interventions: a novel framework for predicting and mitigating freshman student attrition 2025
- Generative AI-enhanced interventions: a novel framework for predicting and mitigating freshman student attrition 2025
- Science Mapping the Knowledge Base on Student Retention in Higher Education: A Bibliometric Review of Research Papers from 1914-2022 2024
- Science Mapping the Knowledge Base on Student Retention in Higher Education: A Bibliometric Review of Research Papers from 1914-2022 2024