Amateur instructional videos often show a single uninterrupted take of a recorded demonstration without any edits. While easy to produce, such videos are often too long as they include unnecessary or repetitive actions as well as mistakes. We introduce DemoCut, a semi-automatic video editing system that improves the quality of amateur instructional videos for physical tasks. DemoCut asks users to mark key moments in a recorded demonstration using a set of marker types derived from our formative study. Based on these markers, the system uses audio and video analysis to automatically organize the video into meaningful segments and apply appropriate video editing effects. To understand the effectiveness of DemoCut, we report a technical evaluation of seven video tutorials created with DemoCut. In a separate user evaluation, all eight participants successfully created a complete tutorial with a variety of video editing effects using our system.
Peer learning, in which students discuss questions in small groups, has been widely reported to improve learning outcomes in traditional classroom settings. Classroom-based peer learning relies on students being in the same place at the same time to form peer discussion groups, but this is rarely true for online students in MOOCs. We built a software tool that facilitates chat-based peer learning in MOOCs by 1) automatically forming ad-hoc discussion groups and 2) scaffolding the interactions between students in these groups. We report on a pilot deployment of this tool; post-use surveys administered to participants show that the tool was positively received and support the feasibility of synchronous online collaborative learning in MOOCs.
Computational design tools allow the generation of vast numbers of possible designs, entrusting the human designer with describing constraints or specifications to guide exploration of the design space. Designers can have many different decision considerations when conducting this type of exploration, including form, function, users, or context. In this work, we investigate strategies that emerge when people are tasked with exploring a large design space within either a non-immersive (2D) or immersive (VR) interface and equipped with action-based interactions to set or envision specifications related to their considerations. Results from a 28 participant user study uncovers that people have varying strategies to enact their decision considerations that are not unique to the type of interface. However, the interfaces differ in perceptions of enabling breadth or depth of exploration holistically, with preference towards 2D interfaces to compare options, and VR to understand single designs. These results have implications for the user experience of systems that allow designers to explore the outputs of large design spaces, both at the interaction and interface levels.
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