Psychometric Evaluation of Learners’ Personality According to the Big Five Model

Abstract

Our thesis traits the relationship between students’ personality traits and their behaviour in digital learning environments. The massive volume of pedagogical and interaction data generated by online platforms o ers new opportunities to better understand learners and o er them more personalised educational experiences. This research aims to design and develop Educonnect an integrated educational web platform, combining social, pedagogical and analytical tools. We then analysed how academic performance and behavioural data help identify students’ OCEAN personality traits. Our methodology integrates three sources of information: educational outcomes (quizzes, activities), psychometric assessment (IPIP-50 question naire) and behavioural data automatically recorded from the platform. The system was tested with third-year computer science undergraduates enrolled in the Semi-Structured Data module, primarily through activities related to XML/XSLT. After preparing the collected data, we used mixed-methods factor analysis (FAMD) and multinomial logistic regression to the behavioural and psycho metric data, in order to investigate the relationship between learning be haviour and personality factors. Our results indicate that interaction behaviours and pedagogical activ ities provide information for characterise students’ personalities. Despite limitations, particularly regarding sample size, this study demonstrates that combining psychometric and behavioural data can inform future adaptive pedagogical approaches and student grouping strategies