Connect with us

Featured

In online learning era, new method can reduce cheating

Published

on

The era of widespread remote learning brought about by the Covid-19 pandemic requires online testing methods that effectively prevent cheating, especially in the form of collusion among students. With concerns about cheating on the rise, a solution that also maintains student privacy is particularly valuable. 

In research published yesterday in npj Science of Learning, engineers from Rensselaer Polytechnic Institute demonstrate how a testing strategy they call “distanced online testing” can effectively reduce students’ ability to receive help from one another in order to score higher on a test taken at individual homes during social distancing. 

“Often in remote online exams, students can talk over the phone or internet to discuss answers,” says Ge Wang, a chair professor of biomedical engineering at Rensselaer and the corresponding author on this paper. “The key idea of our method is to minimize this chance via discrete optimisation aided by knowledge of a student’s competencies.” 

When a distanced online test is performed, students receive the same questions, but at varying times depending on their skill level. For instance, students of highest mastery levels receive each question after other groups of students have already answered those questions. This approach, Wang says, reduces the incentive for students to receive help from those who have more mastery of the material. In order to determine the order of each student’s questions, their competence levels are estimated using their grade point averages, SAT scores, or midterm scores, depending on what is available at a specific point in the semester.

According to statistical tests and post-exam surveys, this method reduced the points gained through collusion by orders of magnitude when compared to conventional exam methods. As an added benefit, Wang says, when students knew collusion would not be possible, they were more motivated to study class material. Wang and his collaborators hope to share this pedagogical innovation beyond the Rensselaer campus. 

“We plan to develop a good platform so that others can easily use this method,” says Wang. 

Mengzhou Li, a graduate student in biomedical engineering, was first author on this paper. Li and Wang were joined in this work by Hisham Mohamed and Uwe Kruger, also from the Department of Biomedical Engineering, Lirong Xia, a professor of computer science, teaching assistants, and graduate students within their labs.  

Trending

Gadget