Keywords
E-exam
BERT
LSTM
RNN
GRU
Essay
E-learning
machine learning
cheating
education
BERT
LSTM
RNN
GRU
Essay
E-learning
machine learning
cheating
education
How to Cite
Joseph Kombe Samuel, Dr. Peter Ochieng, & Dr. Solomon Mwanjele. (2023). Machine Learning Algorithm to Detect Impersonation in an Essay-Based E-Exam. Global Journal of Computer Science and Technology, 23(D1), 15–26. Retrieved from https://gjcst.com/index.php/gjcst/article/view/36
Abstract
Essay-based E-exams require answers to be written out at some length in an E- learning platform The questions require a response with multiple paragraphs and should be logical and well-structured These type of examinations are increasingly becoming popular in academic institutions of higher learning based on the experience of COVID-19 pandemic Since the exam is mainly done virtually with reduced supervision the risk of impersonation and stolen content from other sources increases Due to this there is need to design cost effective and accurate techniques that are able to detect cheating in an essay based E- exam
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