{'fraction_non_alphanumeric': 0.016300940438871474, 'fraction_numerical': 0.0, 'mean_word_length': 5.234375, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Monitoring brain waves in an effort to investigate student's cognitive load during a variety of problem solving scenarios

This paper focuses on understanding cognitive load, cognitive flexibility, cognitive efficiency, and their implications for learning. Cognitive load is commonly defined to be the total amount of mental effort that is being used by the working memory at a given time. Recently, researchers have shown that an Electroencephalograph (EEG) can be a reliable tool to measure the cognitive load that one may experience while performing various tasks. We used this knowledge as the basis for a two-phase study, the goal of which is to establish a mapping of cognitive load throughout the engineering design process, so that we may highlight the key differences between novice engineering students and advanced engineering students. During the first phase of this study we evaluated the B-Alert EEG system's ability to measure cognitive load in problem solving situations. Sophomore and senior engineering students solved engineering problems of varying complexity while connected to the B-Alert system. From the collected data, we are able to see a relationship between problem difficulty and cognitive load experienced. Further analysis of the collected data is needed to identify metrics that may be used to evaluate one's cognitive flexibility and cognitive efficiency. The second phase of this study will focus on analyzing the same cognitive functions as both novice and advanced engineering students solve design problems, which tend to be complex and open-ended in nature.