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

Author:

Rahman Rahayu Abdul,Masrom SurayaORCID,Mohamad MasurahORCID,Sari Eka Nurmala,Saragih Fitriani,Rahman Abdullah Sani Abd

Funder

Universiti Teknologi MARA

Publisher

Elsevier BV

Subject

Medical Laboratory Technology,Clinical Biochemistry

Reference32 articles.

1. A survey of big data and machine learning;Salkuti;Int. J. Electr. Comput. Eng.,2020

2. Detection of academic dishonesty: a perspective of the fraud pentagon model;Achmada;Int. J. Innov. Creat. Change,2020

3. Cheating in online courses: evidence from online proctoring;Dendir;Comput. Hum. Behav. Rep.,2020

4. Predictors of academic dishonesty among undergraduate students in online and face-to-face courses;Peled;Comput. Educ.,2019

5. Remote E-exams during COVID-19 pandemic: a cross-sectional study of students’ preferences and academic dishonesty in faculties of medical sciences;Elsalem;Ann. Med. Surg.,2021

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