This paper describes a method for analyzing learning data from video courses. The data comes from an on-demand introductory programming course at a university. The study analyzes differences in note content characteristics across students by exploring patterns of student notes in a video learning environment, combining quantitative analysis with qualitative analysis centered on the vocabulary used in student notes. By deploying a custom designed learning support system, we facilitated the straightforward expression of students’ questions and notes, thereby collecting rich data reflecting their learning process. Initial findings suggest that the frequency and distribution of specific vocabulary used in students’ notes provide insights into the range of learners’ interests and depth of understanding. For example, although different students recorded notes on the same topic at a specific point in a video, there were significant differences in their focus and note content. Some students tended to record how-to’s and specific features, while others focused more on theoretical concepts and programming principles. These findings show that individual student differences significantly influence their learning focus and information-processing style in a video learning environment. This analysis method allows educators to target instructional interventions based on learners’ unique characteristics of engagement and comprehension as demonstrated through vocabulary use. The preliminary results of this study provide educators with insights to help them understand learner diversity in digital learning environments, providing an essential basis for personalized lesson design and improved learning outcomes.