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Mining Student Information System Records to Predict Students’ Academic Performance

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and IT, The British University in Dubai, Dubai, UAE

    Amjad Abu Saa & Khaled Shaalan

  2. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Gambang, Malaysia

    Mostafa Al-Emran

Authors
  1. Amjad Abu Saa
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  2. Mostafa Al-Emran
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  3. Khaled Shaalan
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Corresponding author

Correspondence to Mostafa Al-Emran .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Saa, A.A., Al-Emran, M., Shaalan, K. (2020). Mining Student Information System Records to Predict Students’ Academic Performance. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_23

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