Educators have benefited greatly from the intelligent tutoring method. Student characteristics must be taken into account for the tutoring system to become more effective and personalized. One of important student characteristics is motivation. Consequently, a self-efficacy theory-based motivation assessment model was put forth in this study. According to the hypothesis, motivational factors included performance, perseverance, effort, and activity selection. Additionally, each attribute has parameters that were established for time spent, difficulty level, number of right answers, and number of questions skipped. The CatBoost algorithm's advantages were utilized in the model's design to forecast the motivation level of students. The model can determine the degree of motivation of pupils just like a human tutor can in a typical classroom.