Artificial Intelligence based Student Proctoring in Online Examination and Grade Prediction - D., Preethi; V. K., Reshma; L., Vigneash; Divya, P.; Ganesh R., Senthil; S. A., Sivakumar (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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%.