Detecting Examinees With Item Preknowledge in Large-Scale Testing Using Extreme Gradient Boosting (XGBoost)

Abstract

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 methods to identify potential testing fraud. In this study, a technical review of a recently developed state-of-the-art algorithm, Extreme Gradient Boosting (XGBoost), is provided and the utility of XGBoost in detecting examinees with potential item preknowledge is investigated using a real data set that includes examinees who engaged in fraudulent testing behavior, such as illegally obtaining live test content before the exam. Four different XGBoost models were trained using different sets of input features based on (a) only dichotomous item responses, (b) only nominal item responses, (c) both dichotomous item responses and response times, and (d) both nominal item responses and response times. The predictive performance of each model was evaluated using the area under the receiving operating characteristic curve and several classification measures such as the false-positive rate, true-positive rate, and precision. For comparison purposes, the results from two person-fit statistics on the same data set were also provided. The results indicated that XGBoost successfully classified the honest test takers and fraudulent test takers with item preknowledge. Particularly, the classification performance of XGBoost was reasonably good when the response time information and item responses were both taken into account.