An Improved Deep Learning Algorithm for Student Achievement Prediction

Authors

  • Sayed Noorusabah  Lecturer in I.T Department, M. H. Saboo Siddik Polytechnic, Mumbai University, Mumbai, Maharashtra, India
  • Dr. Sweta Ramendra Kumar  Assistant Professor, B.Sc-IT, Niranjana Majithia College of Commerce, Mumbai University, Mumbai, Maharashtra, India
  • Mohamed Hasan Phudinawala  Assistant Professor, B.Sc.-IT Department, Niranjana Majithia College of Commerce, Mumbai University, Mumbai, Maharashtra, India

Keywords:

Deep Learning, Optimal Deep learning Algorithms (ODNN), Classification, Students performance Prediction

Abstract

Educational Data Mining (EDM) research has risen to prominence because it aids in the discovery of relevant knowledge from educational data sets that can be used for a variety of reasons, such as forecasting students' academic performance and outcomes. Predicting student accomplishment may be beneficial in the development and implementation of a variety of improvements in education settings as a response to current educational systems. Machine learning has been used to predict students' achievement in a huge amount of existing research, which has taken a variety of factors into account, including family income, students' gender, students' absence, and stage-by-stage characteristics. In this proposal, an attempt is made to investigate the usefulness of applying the Deep Learning Algorithm (DLA), more specifically the Optimal Deep Neural Network (ODNN), to forecast students' progress, which could aid in determining whether or not students would be able to complete their degree. Using experimental data, it was discovered that the suggested framework outperformed the current one that was within reach in terms of accuracy of prediction.

References

  1. Daniel, Ben, "Big Data and analytics in higher education: Opportunities and challenges," British journal of educational technology, vol. 46, no. 5, pp. 904-920, 2015.
  2. Daud, Ali, et al., "Predicting student performance using advanced learning analytics," Proceedings of the 26th international conference on world wide web companion. International World Wide Web Conferences Steering Committee, 2017.
  3. Shahiri, Amirah Mohamed, and Wahidah Husain, "A review on predicting student's performance using data mining techniques," Procedia Computer Science vol. 72, pp.: 414-422, 2015.
  4. Van Barneveld, Angela, Kimberly E. Arnold, and John P. Campbell, "Analytics in higher education: Establishing a common language," EDUCAUSE learning initiative vol. 1, no. 1, pp. l-ll., 2012.
  5. Márquez‐Vera, Carlos, et al., "Early dropout prediction using data mining: a case study with high school students," Expert Systems, vol. 33, no. 1, pp. 107-124, 2016.
  6. Mansur, Andi Besse Firdausiah, Norazah Yusof, and Ahmad Hoirul Basori, "Personalized Learning Model based on Deep Learning Algorithm for Student Behaviour Analytic," Procedia Computer Science vol. 163, pp. 125-133, 2019.
  7. Patil, Akhilesh P., Karthik Ganesan, and Anita Kanavalli, "Effective deep learning model to predict student grade point averages," IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-6, 2017.
  8. Waheed, Hajra, et al., "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human Behavior, vol. 104, pp. 106189, 2020.
  9. Ahmad, Fadhilah, Nur Hafieza Ismail, and Azwa Abdul Aziz, "The prediction of students’ academic performance using classification data mining techniques," Applied Mathematical Sciences, vol. 9, no. 129, pp. 6415-6426, 2015.
  10. Márquez‐Vera, Carlos, et al., "Early dropout prediction using data mining: a case study with high school students," Expert Systems, vol. 33, no. 1, pp. 107-124, 2016.
  11. Kim, Byung-Hak, Ethan Vizitei, and Varun Ganapathi, "GritNet: Student performance prediction with deep learning," arXiv preprint arXiv:1804.07405, 2018.
  12. Tu, Yuwei, Weiyu Chen, and Christopher G. Brinton, "A Deep Learning Approach to Behavior-Based Learner Modeling," arXiv preprint arXiv:2001.08328, 2020.
  13. Hussain, Sadiq, et al., "Prediction model on student performance based on internal assessment using deep learning," International Journal of Emerging Technologies in Learning (iJET), vol. 14, no. 08 pp. 4-22, 2019.
  14. Mansur, Andi Besse Firdausiah, Norazah Yusof, and Ahmad Hoirul Basori, "Personalized Learning Model based on Deep Learning Algorithm for Student Behaviour Analytic," Procedia Computer Science vol. 163, pp. 125-133, 2019.
  15. Patil, Akhilesh P., Karthik Ganesan, and Anita Kanavalli, "Effective deep learning model to predict student grade point averages," IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-6, 2017.
  16. Waheed, Hajra, et al., "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human Behavior, vol. 104, pp. 106189, 2020.
  17. Students' Academic Performance Dataset, Online], available https://www.kaggle.com/aljarah/xAPI-Edu-Data, Access on August 2019.
  18. R. Aruchamy, and V.K. David. "A comparative performance study on hybrid swarm model for micro array data," International Journal of Computer Applications, vol. 975, 2011.
  19. R. Aruchamy. "A hybrid metaheuristic algorithm for classification using micro array data," International Journal of Scientific and Engineering Research, vol. 3, pp.520-528, 2012.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-02-28

Issue

Volume 8, Issue 1, January-February-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sayed Noorusabah, Dr. Sweta Ramendra Kumar, Mohamed Hasan Phudinawala, " An Improved Deep Learning Algorithm for Student Achievement Prediction, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 1, pp.187-192, January-February-2022.