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Cheating Detection in Online Exams During Covid-19 Pandemic Using Data Mining Techniques

EasyChair Preprint no. 5917

13 pagesDate: June 27, 2021


Face-to-face learning has been replaced by e-learning due to the closing of academic institutions in the world during the covid-19 pandemic. Educational institutions faced many challenges in the online platforms and the most important of which was assessing students' performance in the online exams. E-learning has grown significantly every day over the last decade with the growth of the internet and technology. Therefore, an online examination can be beneficial for people to take the exam, but cheating in tests is a common phenomenon around the world. As a consequence, the prevention of cheating can no longer be completely effective. This paper proposed a recommendation system to detect cheating during the online exam using statistical methods, similarity measures, and clustering algorithms by presenting a set of features extracted from the online exam based on Moodle platform. The results show that the proposed online examination system effectively reduces cases of cheating and provides a reliable online exam.

Keyphrases: Cheating Detection, clustering algorithms, e-learning, Moodle, online exam, similarity measures

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ali M. Duhaim and Safaa O. Al-Mamory and Mohammed Salih Mahdi},
  title = {Cheating Detection in Online Exams During Covid-19 Pandemic Using Data Mining Techniques},
  howpublished = {EasyChair Preprint no. 5917},

  year = {EasyChair, 2021}}
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