This research focuses on understanding learners more deeply by quantifying the learning process and combining data from multiple sources. It involves analyzing learner characteristics, identifying different aspects of their learning states, and creating personalized learner profiles. The goal is to build stable models that reflect how students learn using a wide range of data. Since learner modeling is a dynamic and probabilistic process, the study follows a structured approach: first analyzing and extracting learner traits, then recognizing multidimensional learning states, followed by the dynamic construction of personalized learner profiles, and finally developing comprehensive profiles and analyzing how they evolve over time.