E-Learning personalization enables system to present learning strategy based on individual learning type. In directing the personalization, the system should identify the learning type of the course participants. The learning type then will become the reference on arranging the appropriate learning strategy. Commonly, the learning type is analyzed once in the beginning of semester. Thus, the learning strategy is given with the assumption that learning type of each learner is constant. In contrast to previous studies, our research considers that learning type may change overtime and learning strategy also need adjustment to the current conditions of the learner. Therefore, the time-based analysis is necessary to acquire the detailed information related to learner's progress over time and learning type changes. This research extracted activity log from e-Learning system to identify learning type of each learner using The Tripe-Factor Approach. Our proposed system integrates automatic scheduled learning type analysis module into existing e-Learning system. Those mechanisms enable system to obtain comprehensive view of learners' behaviour and to readjust the personalized learning strategy according to learning type changes.