I’m a first-year PhD student in human-computer interaction at the HCI research group at the Institute of Media Informatics at Ulm University. My research interests include gaze-assisted systems for teaching and learning, gaze-based interaction, and eye-tracking.
Teaching via lecture video has become the defacto standard for remote education, but videos make it difficult to interpret instructors’ nonverbal referencing to the content. This is problematic, as nonverbal cues are essential for students to follow and understand a lecture. As remedy, we explored different proxies representing instructors’ pointing gestures and gaze to provide students a point of reference in a lecture video: no proxy, gesture proxy, gaze proxy, alternating proxy, and concurrent proxies. In an online study with 100 students, we evaluated the proxies’ effects on mental effort, cognitive load, learning performance, and user experience. Our results show that the proxies had no significant effect on learning-directed aspects and that the gesture and alternating proxy achieved the highest pragmatic quality. Furthermore, we found that alternating between proxies is a promising approach providing students with information about instructors’ pointing and gaze position in a lecture video.
Since the introduction of augmented reality (AR) technology, in-situ instructions for manual tasks have been a central use case for a large body of previous work. However, most implementations provide identical sets of instructions to each user disregarding the user’s current mental load. This is a major issue since previous work has shown the importance and potential of an adapted instruction fidelity for manual tasks such as playing an instrument. To implement a low-cost mental load adaptation for AR instructions, we evaluated a mobile off-the-shelf electroencephalographic (EEG) device for its suitability and feasibility to measure mental load while wearing a video see-through AR head-mounted display (HMD). In a first user experiment (n=12), data of EEG power band values and proprietary performance metrics of the manufacturer were collected and analysed regarding their validity to estimate the user’s mental load. Our results indicate that our setup successfully induced different levels of mental effort. The proprietary performance metrics, however, only partially reflected the participants’ current mental effort and require further analysis.