This paper proposes a learning support system that can analyse the user’s attention in real-time and provide review suggestions during video-based learning. By utilizing the front camera of the personal computer to detect users’ pupils, this study achieves a real-time analysis of users’ attention and is able to match their attentional state with the timeline of the learning video. With the visualization of attentional state along with the timeline of the learning video, users can readily grasp the parts of learning video they should review to achieve an improved review outcome. We evaluated the review effectiveness of the proposed system by measuring the percentage of correct answers of 20 video learners’ answers to the test questions. As the result, the percentage of correct answers of the inattentive parts of the experimental group was 93.7%, while the control group was 67%. It was observed that through the proposed system, the participants experienced an increase in the percentage of correct answers of their answer in 26.7%. It suggests that the proposed system has the ability to support users’ video-based learning by improving the efficiency of reviewing.