Summative EEG-based Assessment of the Relations between Learning Styles and Personality Traits of Openness

AbstractLearning styles (LS), being one of the important attributes of a learner's profile, are relevant to different aspects of teaching and learning such as the learner's achievement and motivation. Equally important is the personality traits of ‘Openness’, which relate positively to knowledge and skill acquisition, thus making them relevant to learning and learners differences. Recognizing the importance of LS and Openness in profiling learners, the researchers carried out this study to examine the relationship between these two factors using a novel method based on Electroencephalogram (EEG) technology. In this research, Kolb's Learning Style Inventory (KLSI) was used to determine 131 participants’ LS: Diverger, Assimilator, Converger or Accommodator. The EEG technology was used to record the participants’ brain signals (with their eyes closed) to generate the dataset of EEG Beta band of baseline condition. Later, the dataset was processed and classified based on the LS using the 2-Step Cluster Analysis. The result showed that the brain signals could be processed effectively to classify the participants’ LS. More importantly, among the LS studied, convergers and assimilators were observed to have positive and strong relation with Openness. Between the two learning styles, assimilators were found to have stronger relation with Openness than convergers

Development of EEG-based stress index

This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%

Learners' Learning Style classification related to IQ and Stress based on EEG

The importance to recognize a learner's Learning Style (LS) is ever-essential as to substantiate success in a teaching and learning process. At the same time, the learner's IQ and personality traits such as Stress also being actively investigated in educational research as educationists consistently attempted to understand learners in a more adept way. Nevertheless, the effort was usually confined to psychoanalysis test. With the emergence of Electroencephalography (EEG) technology, learner's brain characteristics could be accessed directly and the outcome may well hand-in-hand supported the conventional test. In this study, the participants (n= 80) are grouped to the LS of Diverger, Assimilator, Converger or Accommodator using the Kolb's Learning Style Inventory (KLSI). Subsequently, their brain signals were then recorded using EEG at resting baseline state of Open Eyes and Closed Eyes. A statistical tool of SPSS 16 was used for data analysis purposes. Using the Two Step Cluster analysis, the participants’ EEG datasets were 100% classified to the corresponding LS. Then, EEG Alpha band was selected to link between LS, IQ and Stress. The study concluded that Diverger is the LS with highest IQ while Converger and Diverger are the LS that prone to Stress

Cisco Packet Tracer Simulation as Effective Pedagogy in Computer Networking Course

The Computer Networking course commonly taught in mixed mode involving lecture and practical session whereas beside face-to-face theory session, students need to experience hands-on activities in order to appreciate the technology and contents. Nevertheless the abstraction in Computer Networking course such as the complexity in TCP/IP network layering, the connection and configuration of client and server’s framework, differences in static and dynamic IP address configuration had imposed a great challenge for students to understand and grab the main concept of computer networking technology. As such, an approach of using computer network simulation and visualization tool in teaching and learning Computer Networking course is seen beneficial for educators and students. In this research, computer network simulation software of CISCO Packet Tracer was utilized in Computer Networking (MTN3023) course. Students (N=55) were exposed to CISCO Packet Tracer on which they developed Wide Area Network (WAN) that consists of configuration activities of Personal Computer (PC), Servers and Switches according to CISCO standard. Subsequently, student’s feedback and their insight on the effectiveness of CISCO packet Tracer in learning computer networking were probed using questionnaire. All the feedbacks were investigating statistically using SPSS 16.0. From the analysis, the descriptive results shown that all students were agreed (N=32 : Strongly Agree; N=23 : Agree) that CISCO Packet Tracer had successfully help them to understand several key concepts of computer networking and at the same quash some abstractions they faced in the course. In a nutshell, CISCO Packet Tracer as a simulation and visualization tool had been proven to be an effective software in supporting the teaching and learning of computer networking course

Cisco Packet Tracer Simulation as Effective Pedagogy in Computer Networking Course

The Computer Networking course commonly taught in mixed mode involving lecture and practical session whereas beside face-to-face theory session, students need to experience hands-on activities in order to appreciate the technology and contents. Nevertheless the abstraction in Computer Networking course such as the complexity in TCP/IP network layering, the connection and configuration of client and server’s framework, differences in static and dynamic IP address configuration had imposed a great challenge for students to understand and grab the main concept of computer networking technology. As such, an approach of using computer network simulation and visualization tool in teaching and learning Computer Networking course is seen beneficial for educators and students. In this research, computer network simulation software of CISCO Packet Tracer was utilized in Computer Networking (MTN3023) course. Students (N=55) were exposed to CISCO Packet Tracer on which they developed Wide Area Network (WAN) that consists of configuration activities of Personal Computer (PC), Servers and Switches according to CISCO standard. Subsequently, student’s feedback and their insight on the effectiveness of CISCO packet Tracer in learning computer networking were probed using questionnaire. All the feedbacks were investigating statistically using SPSS 16.0. From the analysis, the descriptive results shown that all students were agreed (N=32 : Strongly Agree; N=23 : Agree) that CISCO Packet Tracer had successfully help them to understand several key concepts of computer networking and at the same quash some abstractions they faced in the course. In a nutshell, CISCO Packet Tracer as a simulation and visualization tool had been proven to be an effective software in supporting the teaching and learning of computer networking course

Electroencephalogram-Based Stress Index

Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able to indicate the stress level of healthy human from their brain electrical activity; Electroencephalogram (EEG) signals. This study proposes stress index as an indicator of stress level using EEG signals. The study employs nonparametric method to extract stress features from EEG signals after performing two tasks; do nothing and answer Intelligence Quotient (IQ) test questions. The k-Nearest Neighbor (k-NN) classifier is used to identify the stressed group using the extracted stress features. The results of the study established 3 type of indexes which represent the stress levels (Low Stress, Moderate Stress, High Stress) with 88.89% overall classification accuracy, 86.67% classification sensitivity and 100% classification specificity. The 10-fold and leave-one-out cross validation of the classifier produced classification accuracy of 78.89% and 83.50% respectively