As the students learn an innovative concept, calculating how well they grasp it has mostly relied on traditional pencil and paper tests. Researchers from Dartmouth University have designed a machine learning algorithm that can be utilized to calculate how well a student comprehends a concept on the basis of his or her brain activity outlines. The study was published in Nature Communications. This research is the first one of its type to look at how the knowledge learned in school is characterized in the brain. To check the knowledge of concepts in STEM (Science, Technology, Engineering, and Mathematics), scientists examined how beginners and intermediate learners’ information and brain activity is compared while testing physics and mechanical engineering concepts, and then developed a novel method to test their conceptual understanding.
David Kraemer—Assistant Professor at Dartmouth College—said, “Learning about STEM subjects is thrilling but it can also be very challenging. Yet, through the lessons of learning, students build up a rich understanding of several complex concepts. Most probably, this acquired information must be reflected in innovative patterns of brain activity. Nevertheless, we presently do not have a detailed perceptive of how the brain sustains this kind of abstract and complex knowledge, so that is what we embark to study.”
Recently, Dartmouth University was in news for its study that explored why creative experts might be superior at imagining the future. Apparently, humans utilize imagination a lot and as situations become farther away from realism and more distal, imagining a circumstance becomes quite difficult. The new research from a partnership between Dartmouth College and Princeton University scientists found that creativity might aid us in surmounting these blockades to distal imagination. The results show that people having creative expertise are good at imagining experiences compared to others with similar demographics (education level, age, etc).
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