YZR-net : Self-supervised Hidden representations Invariant to Transformations for profanity detection

In the past few years due to the Covid19 pandemic the adoption of e-learning platforms has increased significantly. The widespread restrictions have forced students to continue their education via online means which causes them to spend a significant amount of their time watching videos and attending classes. This sudden change from offline to online learning has affected a lot of students therefore making an attempt to build systems that can accurately simulate the experience of offline learning can help in smoothing out this drastic transition. Live classes is one such way that gives the students a chance to escape the monotony of watching recorded videos on a daily basis. The interaction aspect of such classes allow the students to clarify small scale doubts instantaneously and at the same time gives teachers the opportunity to compliment the students on good behaviour. All these tiny bits significantly affect the learning outcome for a student by making the course content more interesting and thus improving their overall engagement on the platform. In order to mimic this offline style of interaction there can be a multitude of implementations like live polls or quizzes to check whether the student is paying attention, dynamic interactive diagrams that fuel the curiosity of students by giving them a chance to tinker with it, in-session feedback to understand the student's opinions or the in-class chats mechanism between the participants of a given session. Unlike all the other mechanisms, chats are the most open medium of communication and provide the maximum opportunity to interact with each other.