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An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242

Authors: KUYORO Afolashade

Publication Type: Journal article

Journal: International Journal Of Scientific & Engineering Research

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Abstract

Abstract— The development in Information and Communication Technology and the introduction of Internet enabled examinations, have generated security and reliability issues in e-learning. A lot of academic dishonesty, fraud and identity theft have been constantly reported in literature. Over the years, biometric authentication was used but this was susceptible to fake biometric input and biometric-database modification. Hence, the need to develop a model that will curb the vulnerabilities of e-examination in e-learning environment. This work combined password, biometric authentication (facial detection, recognition and verification) and video monitoring using Supervised Machine Learning Algorithms (SMLA) to address the vulnerabilities of e-examinations. The developed model was tested, using 250 facial images (dataset) acquired from the entire National Diploma students of Computer Science, Yaba College of Technology. For model testing, the Mean Square Error (MSE) and the Root Mean Square Error (RMSE) were calculated to determine the efficiency and validity of the model. A window-based application called E-Learning Authentication and Monitoring System (ELAMS) was developed using Java programming language, PHP, JavaScript, jQuery, CSS and HTML. MySQL was used as the database on Apache Server. The result from system testing showed that MSE and RMSE had values of 1.35 and 1.17 respectively, indicating that the solution was efficient and valid. This implies that it was near impossible for any examination fraudster to match the identity of student in the database. This study provided a new methodology for unbroken e-examination authentication and monitoring system with high reliability.

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KUYORO,A. . (2020). An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242, 11 (), 235-235.

KUYORO,A. . "An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242" 11, no (), (2020): 235-235.

KUYORO,A. and . (2020). An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242, 11 (), pp235-235.

KUYOROA, . An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242. 2020, 11 ():235-235.

KUYORO,Afolashade , . "An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242", 11 . (2020) : 235-235.

K.Afolashade , "An Improved Authentication and Monitoring System for E-Learning Examination Using Supervised Machine Learning Algorithms 11(3): 235-242" vol.11, no., pp. 235-235, 2020.

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