Existing research in steering often focuses on static paths. However, in real-world systems, such as interfaces for lasso and video games, there are no guarantees that the paths will maintain their initial width and distance during steering. We thus explore steering movements on a dynamically varying path, widening or narrowing. To this end, we empirically studied the impact of several task parameters - including changed path width, path occupancy duration, and position - on steering through the dynamically varying path. As a result, we found different steering movements and performance to the previous steering task in the static path in terms of movement time and error rate. Additionally, we tested various previous extended models and found poor fitness results (mean \(R^2_{adjusted}=0.53\)) compared to the presented models (mean \(R^2_{adjusted}=0.92\)). We believe our model will assist GUI and game designers in evaluating their applications.

Video-based online educational content has been more popular nowadays. However, due to the limited communication and interaction between the learners and instructors, various problems regarding learning performance have occurred. Gaze sharing techniques received much attention as a means to address this problem, however, there still exists a lot of room for improvement. In this work-in-progress paper, we introduce some possible improvement points regarding gaze visualization strategies and report the preliminary results of our first step towards our final goal. Through a user study with 30 university students, we found the feasibility of the prototype system and the future directions of our research.