Hierarchical Agglomerative Clustering to Detect Test Collusion on Computer-Based Tests

There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering (HAC) for detecting aberrant test takers on Computer-Based Testing (CBT) is explored. Random forest ensembles are used to evaluate the accuracy of the clustering and find the important features to classify the aberrant test takers. Testing data from a certification exam is used. The level of overlap between the exact response matches on incorrectly keyed items in the exam preparation material and HAC are compared. Integrating HAC as an investigation mean is promising in this field to improve the accuracy of classification of aberrant test takers.