Résumé:
This thesis is part of an effort to improve online learning (e-learning) by forming learner groups that are balanced from a psychological perspective. The core idea is based on the use of psychometrics, a discipline that evaluates personal characteristics such as personality traits, attitudes, or behaviors using psychological tests. Although these data are originally subjective, they can be transformed into objective numerical measurements to better understand learners’ profiles. The main objective of this work is to design a web application that not only evaluates certain psychological attributes in students but also automatically groups them based on the complementarity of their profiles. This aims to foster collaborative work and enhance group performance in a digital environment. The adopted approach involves several steps: first, a literature review to better understand existing psychometric tools and their use in education; second, the selection of appropriate measurement instruments; then, the design and development of a software tool capable of managing test administration, analyzing results, and automatically creating groups; and finally, a field test with students to evaluate the effectiveness of the proposed system. The system was tested with third-year undergraduate computer science students. The experiment conducted with this sample showed that groups formed automatically based on complementary learning styles achieved better average performance than those formed based on personality traits.
