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Feature optimization and machine learning for predicting students’ academic performance in higher education institutions

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Abstract

Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching. ICT-based learning generates a huge amount of data that can be utilized to better understand student behavior and improve students learning. Predicting students’ academic performance is essential to take proactive measures to improve student learning and reduce the risk of student dropout and failure. This study aims to use video learning analysis and data mining approaches to predict student academic achievement and identify the factors affecting their performance. For this purpose, the dataset containing records of 326 students from a higher education institution (HEI) is used which contains records from SIS, Moodle, and eDify. This study advocates the use of a balanced dataset and optimized feature set to obtain better performance for students’ academic performance prediction. Several machine learning and deep learning models are applied to analyze their performance against the original dataset, balanced dataset, and balanced dataset with the optimized feature set. Experimental results demonstrate decision tree classifier outperforms with an accuracy of 99.06% with a balanced dataset and optimized feature set. Further analysis indicates that the video interaction feature has a strong impact on the performance of students.

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Funding

This research was funded by the European University of Atlantic.

Author information

Author notes

  1. Aom Perkash and Qaisar Shaheen contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

    Aom Perkash & Qaisar Shaheen

  2. Department of Education, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan

    Robina Saleem

  3. School of Computing, National College of Ireland, Dublin, D01 K6W2, Ireland

    Furqan Rustam

  4. Universidad Europea del Atlantico, Isabel Torres 21, Santander, 39011, Spain

    Monica Gracia Villar & Eduardo Silva Alvarado

  5. Universidad Internacional Iberoamericana Arecibo, Arecibo, Puerto Rico, 00613, USA

    Monica Gracia Villar

  6. Universidade Internacional do Cuanza, Cuito, Bie, Angola

    Monica Gracia Villar

  7. Universidad Internacional Iberoamericana, Campeche, 24560, Mexico

    Eduardo Silva Alvarado

  8. Universidad de La Romana, La Romana, Dominican Republic

    Eduardo Silva Alvarado

  9. Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belen, 15, Valladolid, 47011, Spain

    Isabel de la Torre Diez

  10. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea

    Imran Ashraf

Authors
  1. Aom Perkash
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  2. Qaisar Shaheen
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  3. Robina Saleem
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  4. Furqan Rustam
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  5. Monica Gracia Villar
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  6. Eduardo Silva Alvarado
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  7. Isabel de la Torre Diez
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  8. Imran Ashraf
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Contributions

AP conceived the idea, performed data analysis and wrote the original draft. QS conceived the idea, performed data curation and wrote the original draft. RS performed data curation, formal analysis, and designed methodology. FR designed methodology, dealt with software and performed visualization. MGV acquired the funding for research, and performed visualization and initial investigation. ESA dealt with software, carried out project administration and performed visualization. IDLTD performed investigation, supervised the study and performed validation. IA supervised the study, performed validation and review and edit the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Imran Ashraf.

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The authors declare no conflict of interests.

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Perkash, A., Shaheen, Q., Saleem, R. et al. Feature optimization and machine learning for predicting students’ academic performance in higher education institutions. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12698-9

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