Project Team
Students
Renusree Bandaru
Computer Science
Penn State Brandywine
Faculty Mentors
Martin Yeh
Penn State Brandywine
Information Sciences and Technology
Hannah Nolte
Penn State University Park
Industrial Engineering
Project
Project Video
Project Abstract
Learning has been described by cognitive scientists as three different stages, i.e. declarative learning, transition, and procedural learning. Identifying the learning stages can be correlated to one’s mastery level. This allows us to determine whether the individual requires more time to practice and train or if the individual is ready to apply their knowledge with greater accuracy. Researchers also identified that we have different brain systems controlling the process of declarative and procedural learning. However, there is limited research on how brain activity registers these learning stages. This research aims to take advantage of various implications of Machine Learning technology to examine and study how people cognitively learn. Because of the amount and the complexity of the EEG data, traditional statistic models may miss subtle but critical signatures. We explored variations of Hidden Markov Models (HMMs) to better understand which parts of the human brain and what characteristics of a brainwave indicate differences in the cognitive learning stages. Currently, we are removing any background noise such as blinking of the eye from various suitable public EEG data sets for our analysis. Ultimately, we will design an experiment to collect data for the project and use the HMMs previously developed to investigate learning stages in coding. We expect to identify cognitive characteristics of the learning stages through the use of the HMMs and deepen our understanding about the areas of the brain that relates to how humans learn a subject repeatedly.
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