The COVID-19 pandemic and its aftermath have caused most higher educations to choose to implement remote learning as a new method of instruction and assessment. Nevertheless, remote learning has been criticized by having adverse impact on academic integrity. Whistle-blowing has been regarded as an effective mechanism in limiting such unethical behavior. Thus, the main objective of this study is to identify the influence attributes of whistle-blowing intention among university students. The effectiveness of the whistle-blowing attributes was observed in prediction models based on machine learning technique. This paper presents the fundamental knowledge on evaluations of tree-based machine learning algorithms namely decision tree, random forest, to be compared with logistics regression and gradient linear model. A rigorous evaluation reports are provided that includes the area under curve (AUC) as a supplementary metric to measure the model accuracy. Additionally, to provide a clearer insight on the whistle-blowing prediction models, the pattern of influences from the whistle-blowing attributes based on the adoption of theory of planned behavior (TPB) and demography are presented. The findings revealed that both TPB and demography attributes contain some degree of impressive knowledge for the machine learning to generate a good prediction result.

<span lang="EN-US">COVID-19 outbreak has significant impacts on education system as almost all countries shift to new way of teaching and learning; online learning. In this new environment, various innovative teaching methods have been created to deliver educational material in ensuring the learning outcomes such as video content. Thus, this research aims to implement machine learning prediction models for video-based learning in higher education institutions. Using survey data from 103 final year accounting students at Malaysian public university, this paper presents the fundamental frameworks of evaluating three machine learning models namely generalized linear model, random forest and decision tree. Besides demography attributes, the performance of each machine learning algorithm on the video-based learning usage has been observed based on the attributes of technology acceptance model namely perceived ease of use, perceived usefulness and attitude. The findings revealed that the perceived ease of use has given the highest weight of contributions to the generalized linear model and random forest while the major effects in decision tree has been given by the attitude variable. However, generalized linear model outperformed the two algorithms in term of the prediction accuracy.</span>