It’s quite common for students to not properly grasp the concepts explained by the teachers. A teacher can test their students in multiple ways but sometimes, their explanation can simply not be understood by the students.
It doesn’t mean that either the teacher or the student is at fault every time, but something needs to be done about it and teachers should be aware whether their methods are working well or not. Enter AI, the Dartmouth College researchers created a machine learning algorithm not too long ago to help tackle this issue.
The algorithm analyzes a person’s brain activity to figure out how well they grasp an explained concept.
Initially, testing was done on rookie and intermediate engineering students by having them sit in an fMRI scanner and answer questions related to the pictures shown there. The algorithm evolved with time and “neural scores” were created for predicting the performance of students. Based on how frequently definite parts of brain light up, it is determined whether the students have understood the concepts or not.
However, you shouldn’t be expecting any big change any time soon. There is still a lot of work that needs to be done. The current algorithm caters more towards STEM learning, so it might not have significant effects on literature students. Moreover, the concept of neural scores, at the time being, can only be applied to certain areas of knowledge.
Still, any positive step in the right direction is commendable. Teachers could use this algorithm to learn more about their students’ learning techniques and craft their teaching methods accordingly. Thus, the idea has a lot of potential and only time will tell how effective it turns out to be.