Predicting Students' Academic Performance with Conditional Generative Adversarial Network and Deep SVM.

Journal: Sensors (Basel, Switzerland)
PMID:

Abstract

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students' performance, thus mitigating the probability of student failures. Predicting students' academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students' performance through school and home tutoring. Students' educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students' performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.

Authors

  • Samina Sarwat
    Department of Humanities and Social Sciences, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
  • Naeem Ullah
    Department of Humanities and Social Sciences, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
  • Saima Sadiq
    Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan. Electronic address: s.kamrran@gmail.com.
  • Robina Saleem
    Department of Humanities and Social Sciences, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
  • Muhammad Umer
    Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
  • Ala' Abdulmajid Eshmawi
    Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia.
  • Abdullah Mohamed
    Research Center, Future University in Egypt, New Cairo 11845, Egypt.
  • Imran Ashraf
    Information and Communication Engineering, Yeungnam University, Gyeongsan si, Daegu, South Korea.