Technology 3 min read

AI Tool to Measure Human Conceptual Understanding Developed

A new machine learning algorithm tracks brain activity patterns to measure the conceptual understanding of learners and assign a “neural score.”

Image courtesy of Shutterstock

Image courtesy of Shutterstock

Humans are not born with conceptual understanding. We develop it over the years as we move forward on our learning journey.

The ability to internalize concepts help us succeed academically and professionally.

As learners pick up new ideas, regardless of the domain, they also develop sets of skills and experiences. With time, learners gain the ability to express and transfer this knowledge to other individuals.

It’s more than just knowing “isolated facts” because it requires the connection between concepts and the contexts in which they should be applied.

How to teach new ideas is another topic that gets its share of focus. Here, it’s more about the evaluation methods used to assess students’ understanding of concepts.

Conceptual Understanding Footprint in the Brain: Neural Score

How well do you think you can grasp new concepts?

Forget paper-and-pencil assessment and traditional tests. They were both deemed to be inefficient, or let’s say insufficient, especially with the development of educational technologies and the rapid progress of sciences.

A research team from Dartmouth College has devised a machine learning algorithm based on students’ brain activities that can measure how well they understand a concept.

The study, published in Nature Communications, is one of the first studies to investigate how knowledge acquired in school is reflected in the brain.

Researchers tested twenty-eight novice and intermediate learners at Dartmouth on their understanding of some mechanical engineering and physics concepts before they assessed their conceptual understanding of Newton’s third law.

While inside an fMRI scanner, participants were shown images of bridges, buildings, and other structures, so they can think about the balance forces that keep the structures in place. Then, they were asked whether the Newtonian forces were labeled correctly in other images representing the same structures.

Outperforming the novices who answered 53.6 percent of the diagrams correctly, intermediate students were correct 75 percent of the time.

This machine learning method, called informational network analysis, is an innovative assessment tool that generates neural scores to predict the differences in performance between learners.

To validate their results, the team also conducted two traditional multiple-choice tests, and results showed that the higher the neural score, the higher a student would perform on the conventional tests.

Senior author David Kraemer, an assistant professor of education at Dartmouth, explains their approach:

“When engineering students looked at images of real-world structures, the students would automatically apply their engineering knowledge, and would see the differences between structures such as whether it was a cantilever, truss or vertical load. Based on the similarities in brain activity patterns, our machine learning algorithm method was able to distinguish the differences between these mechanical categories and generate a neural score that reflected this underlying knowledge.”

Before practicing it with students, maybe teachers themselves could benefit from such a method to assess their own conceptual understanding. Because if teachers struggle with a given concept, they will transfer a shaky knowledge to their students.

Read More: New Study: Eating Nuts In Early Pregnancy Boosts Children’s Intelligence

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Zayan Guedim

Trilingual poet, investigative journalist, and novelist. Zed loves tackling the big existential questions and all-things quantum.

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