In virtual classrooms across the world, there is high level of student passivity leading to inefficient process of teaching.
Since the Covid-19 pandemic broke, virtual/online classes for school students became normal across the globe replacing real world classrooms.
A key problem faced in such virtual classroom and teaching method is that in a class of 20-30 students, it’s near impossible for a single teacher to look at the face of every student to gauge their attentiveness.
When such classes are held using a mobile phone, the teacher hardly sees the face of any of the students.
My first-hand experience shows that significant number of students exploit this shortcoming to engage in other activities like playing video-games or chatting with friends.
A teacher typically has no way to figure out whether students are attentive or not.
This phenomenon is leading to significant deficiency in the teaching process and would led to students performing poorly when the pandemic is over and full-fledged schooling resumes.
Ways to address this issue by teacher asking random questions to students also has limitations as teacher wouldn’t be able to know whether the student is actually responding after properly listening or looking at the lesson being taught.
A probable solution could be real time analysis of eye movement of the students captured by the mobile/laptop camera and analyzed by a software which might indicate whether the student is attentive or not.