Cheating detection in large-scale assessment received considerable attention in the extant
literature. However, none of the previous studies in this line of research investigated the …

Researchers frequently use machine-learning methods in many fields. In the area of
detecting fraud in testing, there have been relatively few studies that have used these …

In this paper, we compare the performance of 18 indicators of cheating on e-exams in higher
education. Basis of the study was a field experiment. The experimental setting was a …

The traditional linear regression in applied linguistics (AL) suffers from the drawbacks
arising from the strict assumptions namely: linearity, and normality, etc. More advanced …

The COVID-19 pandemic has accelerated the implementation and acceptance of remotely
proctored high-stake assessments. While the flexible administration of the tests brings forth …

The remarkable strides in artificial intelligence (AI), exemplified by ChatGPT, have unveiled
a wealth of opportunities and challenges in assessment. Applying cutting‐edge large …

The pervasive issue of cheating in educational tests has emerged as a paramount concern
within the realm of education, prompting scholars to explore diverse methodologies for …

As technologies have been improved, item preknowledge has become a common concern
in the test security area. The present study proposes an unsupervised‐learning‐based …

Recent approaches to the detection of cheaters in tests employ detectors from the field of
machine learning. Detectors based on supervised learning algorithms achieve high …

There is a growing interest in approaches based on latent-variable models for detecting
fraudulent behavior on educational tests. Wollack and Schoenig (2018) noted the presence …