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We are often hit with situations in which we have to explain ourselves the concept that we are faced with. In such situations, what often happens is the fact that we fail to recognize the overall ordeal. Have you ever wondered why your brain activity isn’t aligning well to make you understand the concept that you were just told of?
A new study (R) conducted by the researchers from the Dartmouth College which makes use of machine learning to gauge how well the students grasp any new concept that’s shared with them on the basis of their basic brain activity. This is the first of the study of its kind which explains how the knowledge learned around in the school is represented inside the brain of the students.
In order to go forth with the study, the researchers wanted to test the knowledge of the concept represented in STEM. The researchers tested out how both groups of novices and the intermediate learners have their knowledge as well as the brain activity compared when they conducted everything with the mechanical engineering along with the physics concepts. Following that, they also developed a new method to be able to assess the conceptual understanding altogether.
David Kraemer, an assistant professor of education at Dartmouth College, who is also the senior author of the study suggested saying while learning about the STEM is quite interesting on its own, it is also quite challenging. Through the entirety of the course, the students do develop a proper rich understanding of several of the complex concepts altogether. All of these acquired knowledge is often reflected on the brain activity of the individual.
Trailing all of these, Kraemer even further suggested saying that they don’t have a proper understanding behind how the brain functions and supports this form of complex and abstract knowledge which is what the primary focus of the study was in the first place.
To conduct the study, the researchers acquired 28 students from Darmouth who were divided into two groups – novices and the engineering students. They ensured that the engineering students have taken at least one mechanical engineering course along with advanced physics while the novice students haven’t had any exposure to any kind of college routine or studies at all.
The entirety of the study was based around three tests which predominantly focused on the process by which the structures are built and also tested out the participants understanding of the Newton’s third law of motion. This law is primarily subjected to the objects in motion but the same can also been theorized for the objects which are static and non-moving. This is the principle theory which suggests whether or not a specific object will collapse under its own weight or it is capable of supporting more weight.
Preceding the study, the participants were given a brief rundown of all the involved forces in the mechanical engineering. Following that, in an fMRI scanner, they were displayed with some of the real life structures and were then asked to explain how the individual forces would apply to the same to keep the structure in equilibrium.
Then, the participants were again prompted with the same image but with arrows overlaying on the structure. They were then asked to identify whether or not the Newtonian forces were labeled correctly or not. The researchers found that 75% of the engineering students were able to answer the same correctly while only 53.6% of the novice students answered them correctly.
Keeping aside the fMRI session, the participants were also given two standarised multiple choice questions to complete which were based around the knowledge about mechanical engineering and physics knowledge. For both of these conducted tests, the engineering students again outnumbered the novice students by a high margin.
For this specific study, the researchers wanted to opt for a different route than what is generally used in the field of neuroscience. They wanted to devise a data drive method which would be capable of culminating the individual neural score based off of the brain activity itself.
They created a new method of evaluation known as the informational network analysis which is a machine learning algorithm which was responsible for providing with neural scores based off of the differences in the performances of the individual. To reevaluate and cross check the prospect of efficacy of this machine learning program, the researchers also compared the results of the participants from the three conducted studies. The researchers found that the participants with the high neural score were more likely to score more on the conducted tests.
Shedding some light on everything, Kraemer suggested saying that the main conclusion that they drew from the study is the fact that the engineering students are more likely going to apply their knowledge when they see a real life image, thus assessing the difference between the structures.
On the basis of the similarity in the brain patterns, the main aim of the researchers was to be able to distinguish between the mechanical categories along with the neural score based off of the underlying knowledge. The primary idea behind was to deduce the fact that the engineer as well as the novice are going to view something different when they look at the photograph and this is what they researchers wanted to focus on.
The final conclusion to the study was the fact that even though both the engineering and the novice students use their visual cortex while applying the concept knowledge, they use their brain in a very different way while processing the same visual image. The outcomes from the same found that the engineering students did have their patterns in the brain activity in several of the different regions including the dorsal frontopariental network which is associated with the spatial cognition.
There needs to be more studies conducted with the concept learning and the impacts of the same on the brain activity for better understanding but this study was quite self explanatory on its own explaining the process effectively.
