Identifying Problem Solving Strategies for Learning Styles in Engineering Students Subjected to Intelligence Test and EEG Monitoring

The cognitive approach of learning agrees in that behavior of the individual correlates with certain complex and dynamic mental processing operations modulated by certain internal mechanisms learned or improved during life. Applied to university learning, the demand towards students no longer focuses only in the results of the process but, ideally, in the development of individual styles based on cognitive preferences and learning strategies. Several tests such as Kolb and VAK or Hermann dominances allow distinguishing some learning styles such as: Divergent, Assimilator, Convergent and Accommodator; Visual; Kinesthetic; and Auditory. Meanwhile, by using electrophysiological tools like an electroencephalographic (EEG) recording it is now possible to measure individual differences in the varying electrical activity present in the cerebral cortex while a student face a cognitive problem or performs a test of intelligence. In this study, we tested 20 students from their early years of engineering to be classified in three systems of learning styles. The students were then subjected to a test of intelligence (Raven test, abbreviated version of 15 questions) with increasing levels of complexity. The time length to solve the test taken for students previously classified in four main styles yielded by the Kolb test, were analyzed. Then we grouped them by correspondences with the visual, auditory and kinesthetic predominance yielded by the VAK test. The response time was measured and the absolute frequency of response time for each question was calculated. During the Raven test execution an electroencephalogram with Emotiv Epoc Brain-Computer Interface of 14 channels was recorded. After subtracting baseline and eliminating common artifacts with the help of EEGLAB toolbox under Matlab, we obtained clean signals for theta, alpha, beta and gamma bands. Using the set of learning styles classificatory tools and the electrophysiological analysis we started to look for variability and consistencies that support or rise new questions about adequate usefulness of common used psycho-cognitive and behavioral learning styles tests. In our sample we found relatively low discriminative resolution from all learning styles test applied, but a promising research field to study electrical brain activity phenomenology associated with learning and solving problem strategies of the brain.