A Comprehensive Analysis of Relationship among Cognitive Traits, Learning Styles, and Academic Performance in Computer Programming


  • C P Pavan Kumar Hota Research Scholar, Department of CSE, Annamalai University, Tamilnadu.
  • V.Asanambigai Assistant Professor, Department of CSE, Annamalai University, Tamilnadu, India.
  • D. Lakshmi Senior Associate Professor, School of Computing Science and Engineering (SCSE), VIT Bhopal University, Madhya Pradesh, India


Brain Dominance, Chi-Square test, Cognitive, Full battery assessment,Higher education, Learning style, Multiple intelligence, Thinking skill.


Rapid changes in the field of design engineering education, driven by factors such as fast-paced technological advancements, increasing globalization, and a diverse student demographic, have prompted a need for innovative teaching and learning strategies. This research employs psychometric analysis to investigate the factors contributing to student failure. Using established and reliable assessments, including the personality traits from Big-5 assessment, BTSA for understanding Brain Dominance, Learning Style using Kolb’s assessment, Multiple Intelligence given by Howard Gardner, and Kolb's Learning Style Inventory, this study examines 1145 engineering students to uncover their cognitive, learning style, and personality traits. This study simplifies the process of transforming education to meet the needs of each student by analyzing the findings and facilitating a deeper comprehension of each student's intellectual and academic potential. Chi-square tests are employed to explore the associations between bimodal cognitive traits within the dataset. Furthermore, a comprehensive battery assessment is applied to identify learning disabilities and behavioral disorders by considering students' cognitive profiles and academic performance. According to their levels of cognitive development and academic success, the study's results categorize students into 16 different groups, offering important new information about the connections between cognitive characteristics, learning preferences, and academic success in engineering education.




How to Cite

C P Pavan Kumar Hota, V.Asanambigai, & D. Lakshmi. (2023). A Comprehensive Analysis of Relationship among Cognitive Traits, Learning Styles, and Academic Performance in Computer Programming. Chinese Journal of Computational Mechanics, (5), 424–430. Retrieved from http://jslxxb.cn/index.php/jslxxb/article/view/4378