Machine Learning-Driven Analysis of Educational Behavior for Sustainable Development

Abstract

This research employs machine learning and computational psychometrics to investigate educational behavior in relation to the Big Five personality traits. Leveraging a comprehensive dataset of 1,015,342 questionnaire responses collected online, the study delves into understanding cross-cultural variations, gender disparities, age-related dynamics, and interconnections among these traits. By scrutinizing how these personality dimensions’ manifest in learners, the research endeavors to unravel their impact on individual well-being and life satisfaction within educational settings. The exploration of cross-cultural nuances offers insights into how diverse backgrounds influence the expression of these traits, potentially reshaping educational approaches to accommodate multicultural learning environments. Additionally, dissecting gender differences and age-related patterns sheds light on nuanced behavioral variations and developmental aspects crucial for tailored educational interventions.