Using Artificial Intelligence to Predict Class Loyalty and Plagiarism in Students in an Online Blended Programming Course during the COVID-19 Pandemic

Abstract

During the COVID-19 epidemic, most programming courses were revised to distance learning. However, many problems occurred, such as students pretending to be actively learning while actually being absent and students engaging in plagiarism. In most existing systems, obtaining status updates on the progress of a student’s learning is hard. In this paper, we first define the term “class loyalty”, which means that a student studies hard and is willing to learn without using any tricks. Then, we propose a novel method combined with the parsing trees of program codes and the fuzzy membership function to detect plagiarism. Additionally, the fuzzy membership functions combined with a convolution neural network (CNN) are used to predict which students obtain high scores and high class loyalty. Two hundred and twenty-six students were involved in the experiments. The dataset was randomly separated into the training datasets and the test datasets for twenty runs. The average accuracies of the experiment in predicting which students obtain high scores using the fuzzy membership function combined with a CNN and using the duration and number of actions are 93.34% and 92.62%. The average accuracies of the experiment in predicting which students have high class loyalty are 95.00% and 92.74%. Both experiments show that our proposed method not only can detect plagiarism but also can be used to detect which students are diligent.

Interpretive analysis of online teaching labs constructed using moodle during the pandemic period

“…Certainly, the courses taught do not meet the same requirements, and teachers must renew/review their techniques on a regular schedule. In addition, during times of crisis, teachers/assessors [ 14 ] should be concerned about plagiarism and should focus on engaging students in their studies rather than on ratings. The alternative would be to teach students to accept the values of academic culture, which do not permit plagiarism.…”

Academic dishonesty by students of bioethics at a tertiary institution in Australia: an exploratory study

“…While plagiarism is still problematic in institutions of higher learning, there generally, has been a downward trend in its prevalence in the 30 years from 1990 to 2020 (Curtis 2022 ). The COVID 19 pandemic, might also have brought an increase in plagiarism and cheating by students (Comas-Forgas et al 2021 ; Kratovil 2021 ; Wu et al 2021 ). Similar concerns of cheating have been reported in the United States (cheating and plagiarism) (Perez-Pena 2012 ), Australia (Belot, 2016 ) and South Africa (News24, 2014 ).…”

Personalización de tareas como metodología de aprendizaje para evitar la deshonestidad en educación superior

“…El fácil acceso a la información, el insuficiente control por parte de los docentes y las débiles sanciones por parte de las instituciones educativas facilitan el proceso. Cuando los estudiantes normalizan estas actitudes, se fomenta una cultura de aprobación que desplaza el interés por aprender (Comas et al, 2021;Selemani et al, 2018;Wu et al, 2021).…”