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The widespread adoption of smart home devices (SHD) has increased privacy concerns among users, yet user-friendly controls are lacking. While there is a large body of research focused on understanding privacy concerns and threat models of SHD users, there is limited research so far aimed at informing the development of privacy controls in SHDs. This paper presents the results of 25 interviews focused on characterizing the users' needs for privacy controls. Through qualitative analysis of interview data, we present 7 design factors and 32 sub-factors for the design of privacy controls in SHDs. The interview findings inform the design of a survey that was deployed to 440 adult SHD users to gain quantitative insights on privacy control requirements and to complement the interview findings. Based on the findings, we discuss a privacy control framework that guides designers towards user-centric privacy controls.

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The world was hit by the Covid-19 pandemic in the beginning of 2020, which forced many educational institutions to shift their classroom-based or face-to-face courses to remote or online mode. Students, instructors and institutions had little time to prepare for this sudden, unanticipated shift. Students who prefer learning face-to-face with instructor in a classroom are more likely to face challenges in learning remotely. To understand the experiences of such students, the author conducted a pilot survey of students whose face-to-face courses were moved to remote learning. This paper reports on the results of the analysis of participant responses. Participants experienced a mix of benefits and challenges due to the unanticipated move to remote learning. Based on the findings, this paper makes some suggestions on the design of remote learning.

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