Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior

Rahayu Abdul Rahman; Suraya Masrom; Masurah Mohamad; Eka Nurmala Sari; Fitriani Saragih; Abdullah Sani Abd Rahman

doi:10.1016/j.mex.2023.102364

Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior

MethodsX. 2023 Sep 7:11:102364. doi: 10.1016/j.mex.2023.102364. eCollection 2023 Dec.

Authors

Rahayu Abdul Rahman¹, Suraya Masrom², Masurah Mohamad², Eka Nurmala Sari³, Fitriani Saragih³, Abdullah Sani Abd Rahman⁴

Affiliations

¹ Faculty of Accountancy, Univesiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
² College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
³ Department of Accounting, Universitias Muhammadiyah Sumatera Utara, Indonesia.
⁴ Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Perak, Malaysia.

Abstract

Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70-93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields.•AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing.•Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing.•AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem.

Keywords: Academic dishonesty; AutoML; Automated Machine Learning (AutoML); Demographic; Theory of planned behavior; Whistleblowing.