An Ensemble Learning Approach Based on TabNet and Machine Learning Models for Cheating Detection in Educational Tests

Abstract

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 identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep neural network model, remains uncharted territory. Within this study, a comprehensive evaluation and comparison of 12 base models (naive Bayes, linear discriminant analysis, Gaussian process, support vector machine, decision tree, random forest, Extreme Gradient Boosting (XGBoost), AdaBoost, logistic regression, k-nearest neighbors, multilayer perceptron, and TabNet) was undertaken to scrutinize their predictive capabilities. The area under the receiver operating characteristic curve (AUC) was employed as the performance metric for evaluation. Impressively, the findings underscored the supremacy of TabNet (AUC = 0.85) over its counterparts, signifying the profound aptitude of deep neural network models in tackling tabular tasks, such as the detection of academic dishonesty. Encouraged by these outcomes, we proceeded to synergistically amalgamate the two most efficacious models, TabNet (AUC = 0.85) and AdaBoost (AUC = 0.81), resulting in the creation of an ensemble model christened TabNet-AdaBoost (AUC = 0.92). The emergence of this novel hybrid approach exhibited considerable potential in research endeavors within this domain. Importantly, our investigation has unveiled fresh insights into the utilization of deep neural network models for the purpose of identifying cheating in educational tests.

The Identification of Guessing Patterns in Progress Testing as a Machine Learning Classification Problem

“…The detection of test participants with unusual response patterns can easily be viewed as a supervised machine learning classification problem; one only needs to determine beforehand the exact type of response pattern to be identified. Indeed, some research has been published recently on the performance of machine learning methods in the identification of different groups of non-serious test takers; for example, Zopluoglu [18] has used XGBoost to detect item preknowledge in large-scale tests, while Zhen and Zhu [19] have developed an ensemble learning method to detect cheating in educational tests, a problem which Kamalov et al [20] have approached by training a recurrent neural network with sequential exam data. Of particular interest to us are the results of Nazari et al [21], who have compared the results of random forests and person-fit indices in the detection of careless responders, concluding that random forests predict careless responding more accurately than any person-fit index included in the "PerFit" package of the R programming language.…”

The Identification of Guessing Patterns in Progress Testing as a Machine Learning Classification Problem

“…The detection of test participants with unusual response patterns can easily be viewed as a supervised machine learning classi cation problem; one only needs to determine beforehand the exact type of response pattern to be identi ed. Indeed, some research has been published recently on the performance of machine learning methods in the identi cation of different groups of non-serious test takers; for example, Zopluoglu [18] has used XGBoost to detect item preknowledge in large-scale tests, while Zhen and Zhu [19] have developed an ensemble learning method to detect cheating in educational tests, a problem which Kamalov et al [20] have approached by training a recurrent neural network with sequential exam data. Of particular interest to us are the results of Nazari et al [21], who have compared the results of random forests and person-t indices in the detection of careless responders, concluding that random forests predict careless responding more accurately than any person-t index included in the "PerFit" package of the R programming language.…”