What type of study is this?

This is a Experimental Study study.

September 12, 2025PeerJ Computer ScienceOpen Access

A machine learning assistant for detecting fraudulent activities in synchronous online programming exams

View Full Paper

Ask AI

Bookmark

Authors

FOFrancisco OrtínUniversidad de Oviedo ÁGÁ Díaz GagoUniversidad de Oviedo JQJosé QuirogaUniversidad de Oviedo

Discussion

Member takes

Implication

This article presents a machine learning solution to detect fraudulent activities in online exams, indicating its effectiveness in maintaining academic integrity.

Key Points

The machine learning system detects fraudulent activities during online programming exams with an accuracy of 95.18%.
Utilizing a convolutional neural network and recurrent neural network, the system prioritizes recall to minimize false positives.
Data augmentation and class-weight adjustments during training led to substantial improvements in detection performance.
Instructors reported high satisfaction with the system's real-time monitoring capabilities for large-scale online exams.

Cite This Study

Ortín et al. (2025) studied this question.

synapsesocial.com/papers/68d44c4d31b076d99fa55d4f https://doi.org/10.7717/peerj-cs.3159

View Full Paper

Ask AI

Bookmark

Implication

This article presents a machine learning solution to detect fraudulent activities in online exams, indicating its effectiveness in maintaining academic integrity.

A machine learning assistant for detecting fraudulent activities in synchronous online programming exams

Discussion

Member takes

Implication

Key Points

Cite This Study

A machine learning assistant for detecting fraudulent activities in synchronous online programming exams

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider