A comparative study of machine learning and deep learning algorithms for predicting student’s academic performance

Megha Bhushan (), Satyam Vyas, Shrey Mall and Arun Negi
Additional contact information
Megha Bhushan: DIT University
Satyam Vyas: DIT University
Shrey Mall: DIT University
Arun Negi: Deloitte USI

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 48, 2674-2683

Abstract: Abstract Education plays a crucial role in personal development and social change. It provides individuals with the necessary skills to succeed in their careers and serve their communities. The Cumulative Grade Point Average (CGPA) is a valuable metric for assessing student’s academic performance. It is calculated by averaging the performance of a student in each semester, also known as Semester Grade Point Average (SGPA). Its prediction can provide several benefits to both faculties and students. It enables faculties to promptly identify weak students who are facing difficulties and provide them with necessary intervention and support. Additionally, SGPA empowers students to establish realistic future goals and make well-informed decisions based on it. In this study, various classification and regression algorithms are applied to predict student’s SGPA and compared using various evaluation metrics. The results concluded that it is feasible to predict SGPA (of students) using machine learning and deep learning models with low error rate and high accuracy as the predicted results will be equally beneficial for both faculties and students.

Keywords: Machine learning; Prediction; Deep learning; Classification; Regression; Academics (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-02160-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:14:y:2023:i:6:d:10.1007_s13198-023-02160-3

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-023-02160-3

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().