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Shared gaze, where collaborators can see each other's point of gaze visualized on their screen in real time, is a novel non-verbal mechanism that augments remote collaborations and increases shared awareness and common grounding. While past studies have focused on well-structured tasks and analyzed task performance and efficiency, our study explores the domain of collaborative drawing for recreational purposes and focuses on collaborators' own perceptions. We surveyed 75 users of online collaborative drawing platforms who mostly drew collaboratively for recreation and artistic growth; they reported the importance of communication but also of retaining individual space despite the collaborative setting. Informed by this and prior research on shared gaze, we evaluate collaboration by allowing two collaborators to draw synchronously on a shared canvas and share their point of gaze. We conducted a study with 24 pairs that drew collaboratively under all combinations of shared gaze and voice communication. Combining voice and shared gaze was perceived to reach the best balance between tightly coupled collaboration and parallel individual execution. Shared gaze led to higher spatial awareness and less turn-taking was observed in conditions that shared gaze was present. Surprisingly, many participants found the lack of any communication medium to afford the highest degree of divergent thinking. Our findings provide guidelines for adaptive tools that consider individual preferences as well as the nature of the task to better support remote collaborations that are open-ended and prize creativity. 

As machine intelligence is increasingly incorporated into educational technologies, it becomes imperative for instructors and students to understand the potential flaws of the algorithms on which their systems rely. This paper describes the design and implementation of an interactive post-hoc explanation of the Bayesian Knowledge Tracing algorithm which is implemented in learning analytics systems used across the United States. After a user-centered design process to smooth out interaction design difficulties, we ran a controlled experiment to evaluate whether the interactive or static version of the explainable led to increased learning. Our results reveal that learning about an algorithm through an explainable depends on users' educational background. For other contexts, designers of post-hoc explainables must consider their users' educational background to best determine how to empower more informed decision-making with AI-enhanced systems.