Exploration of the Stacking Ensemble Machine Learning Algorithm for Cheating Detection in Large-Scale Assessment

Abstract

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 stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study explored the application of the stacking ensemble machine learning algorithm to analyze the item response, response time, and augmented data of test-takers to detect cheating behaviors. The performance of the stacking method was compared with that of two other ensemble methods (bagging and boosting) as well as six base non-ensemble machine learning algorithms. Issues related to class imbalance and input features were addressed. The study results indicated that stacking, resampling, and feature sets including augmented summary data generally performed better than its counterparts in cheating detection. Compared with other competing machine learning algorithms investigated in this study, the meta-model from stacking using discriminant analysis based on the top two base models—Gradient Boosting and Random Forest—generally performed the best when item responses and the augmented summary statistics were used as the input features with an under-sampling ratio of 10:1 among all the study conditions.