What question did this study set out to answer?

This research aims to develop a machine learning-based system to identify undergraduate students at academic risk and provide personalized intervention strategies.

May 10, 2026PeerJ Computer ScienceOpen Access

Early warning and recommendation system for undergraduate students at higher education institutions based on machine learning methods

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Authors

STSameh TaqatqehCyprus International University MAMary AgoyiCyprus International University

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Implication

Randomized trial demonstrates improved academic outcomes for undergraduate students using a machine learning system, suggesting an effective pathway for timely interventions.

Key Points

This research aims to develop a machine learning-based system to identify undergraduate students at academic risk and provide personalized intervention strategies.
Utilized a comprehensive dataset with institutional, academic, and demographic variables.
Trained and evaluated fifteen machine learning classifiers to predict academic warning status.
Implemented feature selection techniques to identify key predictors of academic performance.
Boosting with Grid Search achieved an accuracy of 89.7%, recall of 0.92, and AUC of 0.92.
Identified academic performance indicators such as SCGPA, GPA, and CGPA as the most predictive features.
The recommendation module effectively addressed root causes of academic risk and provided tailored solutions.

Cite This Study

Taqatqeh et al. (2026) studied this question.

synapsesocial.com/papers/6a00205ec8f74e3340f9b533 https://doi.org/10.7717/peerj-cs.3792

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Implication

Randomized trial demonstrates improved academic outcomes for undergraduate students using a machine learning system, suggesting an effective pathway for timely interventions.

Early warning and recommendation system for undergraduate students at higher education institutions based on machine learning methods

Discussion

Member takes

Implication

Key Points

Cite This Study

Early warning and recommendation system for undergraduate students at higher education institutions based on machine learning methods

Discussion

Member takes

Implication

Key Points

Cite This Study

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