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Engaged Learning: Using Neurotechnology to Assess and Improve STEM Outcomes

In engineering education, the level of attention and engagement is one of the most important factors affecting student success. To date, factors affecting engineering education are not well understood, and it is not clear what makes some students better focused than others. It is hypothesized that quantitative identification of learning styles and teaching methods can be achieved through brain activity monitoring. Quantitatively evaluating learning and teaching outcome using brain wave monitoring will revolutionize the current practice in engineering education. The proposed approach uses special algorithms to determine attention levels and interest levels of engineering students while they are engaged in classes of various styles, such as being abstract, math-based, chemistry, design, or graphic-based. This will help determine interest areas of the engineering student as an individual. It will also assist the instructors in their approaches to teaching by allowing them to see where they actually engage the student or fail to deliver curriculum material in a way that engages the student.


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