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Related Experiment Video

Updated: Oct 25, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Machine learning based approach to exam cheating detection.

Firuz Kamalov¹, Hana Sulieman², David Santandreu Calonge³

¹Department of Electrical Engineering, Canadian University Dubai, Dubai, UAE.

|August 4, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning approach to detect cheating in online exams by identifying abnormal student scores. The method uses recurrent neural networks and anomaly detection to maintain academic integrity in remote education.

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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

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Published on: June 10, 2021

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Last Updated: Oct 25, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Area of Science:

Computer Science
Education Technology
Artificial Intelligence

Background:

The COVID-19 pandemic necessitated a global shift to remote teaching in educational institutions.
Preserving academic integrity during online assessments presents a significant challenge due to the lack of direct supervision.
Academic misconduct risk increases with remote final examinations.

Purpose of the Study:

To propose a novel machine learning-based method for detecting potential academic dishonesty in online final exams.
To address the challenge of identifying cheating in student assessments within the context of remote education.

Main Methods:

The problem of cheating detection is framed as an outlier detection task.
Student continuous assessment results are utilized to identify anomalous final exam scores.
Recurrent neural networks (RNNs) combined with anomaly detection algorithms are employed to handle the sequential nature of student assessment data.

Main Results:

Numerical experiments demonstrate a high accuracy rate in detecting cheating incidents.
The proposed method effectively identifies abnormal scores indicative of academic misconduct.
The approach proves successful across various datasets.

Conclusions:

The developed machine learning method offers an effective tool for enhancing academic integrity in online assessments.
Academics and administrators can utilize this approach to safeguard the credibility of course evaluations.
The study contributes a robust solution to the persistent issue of cheating in remote learning environments.