July 31, 2025Applied SciencesOpen Access

Predictive Analytics Leveraging a Machine Learning Approach to Identify Students’ Reasons for Dropping out of University

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Identifying reasons for student dropout, the framework predicts dropout rates using machine learning techniques and highlights several significant factors.
Findings indicate that factors such as social background and insufficient professor motivation are critical to understanding dropout rates in students.
The analysis employed machine learning, leveraging data collected from 120 students aged 18–26 across a Moroccan public university between 2022 and 2024.
The implications emphasize the need for universities to integrate AI technologies to address and reduce dropout rates effectively.

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Implication

Quantitative analysis identifies factors affecting student dropout rates in higher education, suggesting AI insights could enhance teaching strategies.

Cite This Study

Mahmoudi et al. (2025) studied this question.

synapsesocial.com/papers/68af2d76cf1dd9ea359e54f1 https://doi.org/10.3390/app15158496

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Implication

Quantitative analysis identifies factors affecting student dropout rates in higher education, suggesting AI insights could enhance teaching strategies.

Predictive Analytics Leveraging a Machine Learning Approach to Identify Students’ Reasons for Dropping out of University

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Discussion

Implication

Cite This Study

Predictive Analytics Leveraging a Machine Learning Approach to Identify Students’ Reasons for Dropping out of University

Key Points

Discussion

Implication

Cite This Study

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