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Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic d...

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Published in:Sensors (Basel, Switzerland) Switzerland), 2023-04, Vol.23 (8), p.4149
Main Author: Alsabhan, Waleed
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Language:English
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description Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model's superior performance.
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subjects Academic achievement
Academic misconduct
Analysis
Cheating
Critical thinking
Datasets
Deep learning
Dishonesty
Education
Education, Higher
exploratory data analysis
Higher education
Literature reviews
Machine learning
Methods
Neural networks
Online education
online examination
Plagiarism
Software
student assessment
student cheating detection
Students
Well being
title Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques
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