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Predicting Student Retention Among a Homogeneous Population Using Data Mining

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1058))

Abstract

Student retention is one of the biggest challenges facing academic institutions worldwide as it does not only affects the student negatively but also hinders institutional quality and reputation. In this paper, we use classification techniques to predict retention at an academic institution based in the Middle East. Our study relies solely on pre-college and college performance data available in the institutional database to predict dropouts at an early stage. We built a predictive model to study retention until graduation and compare the performance of five standard algorithms and five ensemble algorithms in effectively predicting dropouts as early as possible. The results showed that ensemble predictors outperform standard classification algorithms by effectively predicting dropouts using enrollment data with an Area Under the Curve (AUC) of 88.4%.

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Author information

Authors and Affiliations

  1. Higher Colleges of Technology, Dubai, UAE

    Ghazala Bilquise & Thaeer Kobbaey

  2. British University in Dubai, Dubai, UAE

    Sherief Abdallah

Authors
  1. Ghazala Bilquise
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  2. Sherief Abdallah
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  3. Thaeer Kobbaey
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Corresponding author

Correspondence to Ghazala Bilquise .

Editor information

Editors and Affiliations

  1. Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Ain Shams University, Cairo, Egypt

    Mohamed Fahmy Tolba

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Bilquise, G., Abdallah, S., Kobbaey, T. (2020). Predicting Student Retention Among a Homogeneous Population Using Data Mining. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_4

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