“At the same time, these questions have started to increase in volume with the timing being exactly alongside the move to online teaching, the move to online exams and assessments, often in a completely unsupervised environment,” Lancaster continued. “From the point of view of Chegg, they are not promoting themselves as a service designed to help students to cheat, but they do offer a facility where you can get your answers completed quickly by a tutor, and the answers are delivered within the short time frame which matches an exam.” “From my experience as somebody who has set exams, marked exams, read exams, seen too many exams, these things look like exam questions,” said Thomas Lancaster, the lead author of the study and senior teaching fellow in computing at Imperial College London, where he researches issues related to academic integrity and contract cheating. “Given the number of exam style questions, it appears highly likely that students are using this site as an easy way to breach academic integrity by obtaining outside help,” the authors write in the article titled “Contract cheating by STEM students through a file sharing website: a COVID-19 pandemic perspective.”
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You can also use this spectacular option of Image Upscale here.The number of questions asked and answered on the “homework help” website Chegg has skyrocketed since classes migrated online due to the pandemic, an increase that authors of a new study published in the International Journal for Educational Integrity link to a likely increase in cheating.Ĭhegg, which has an honor code prohibiting cheating and which promotes itself as a site where students can get help on their homework, allows users to post a question to the site and receive an answer from a Chegg-identified expert “in as little as 30 minutes.” (The site’s posted average response time is 46 minutes.) The authors of the new study found that the number of questions posted on the site in five different science, technology, engineering and mathematics disciplines increased by 196.25 percent in April to August of 2020 compared to the same period in 2019. This way, GAN deblur photos making its look close to the original. It simply compares the textures of both photo states and then the service recreates it per user’s request. This algorithm had been trained on a huge set of photos both sharp and blurred. Well, all the magic is possible with Generative Adversarial Networks (GAN). You might be wondering how it works on the backend. Just check your image properties and you may start deblurring the photo online in a few seconds.
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