Artificial Intelligence based Student Proctoring in Online Examination and Grade Prediction

Authors

  • Preethi D. Associate Professor, Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai
  • Reshma V. K. Associate Professor, Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore
  • Vigneash L. Associate Professor, Electronics and Communication Engineering, Arjun College of Technology, Coimbatore.
  • P. Divya Assistant Professor (SS), Dr NGP Institute of Technology, Coimbatore
  • Senthil Ganesh R. Associate Professor, Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore
  • Sivakumar S. A. Associate Professor, Electronics and Communication Engineering, Dr NGP Institute of Technology, Coimbatore

Keywords:

k-Nearest Neighbor, Lion Optimization, ML algorithms, Naïve Bayes, Proctoring and Pre- assessing, Support Vector Machine

Abstract

Numerous fields are utilizing and getting nurtured by the usage of Machine Learning (ML) algorithms owing to its simplicity in implementation and suitable accuracy. As the various online teaching and learning tools are booming post COVID - 19 to promote remote education, proctoring and assessing students' performance is a major challenge. This situation fits into data mining where students can be categorized based on their level of learning. Hence this promotes early attention for the slow learner students. This paper aims to provide solution to both proctoring and pre-assessing their grades through ML algorithms. The abnormal scores in final exams over internal assessments are compared and identified as outliers and it’s been resolved using neural networks along with anomaly detection algorithms for proctoring. A comparison is made on various existing algorithms such as k-NN, Naive Bayes, SVM and Lion Optimization to explore the data in university records and exploit them for the purpose of judging their grades and ranks in advance. It is observed that the simulation results indicate that the Lion Optimization with anomaly detection performs better when compared with the other ML algorithms with an accuracy of 95.2%.

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Published

24.11.2023

How to Cite

D., P. ., V. K., R. ., L., V. ., Divya, P. ., Ganesh R., S. ., & S. A., S. . (2023). Artificial Intelligence based Student Proctoring in Online Examination and Grade Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 469–476. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3932

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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