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While learning by teaching is a popular pedagogical technique, it is a learning phenomenon that is difficult to study due to variability in the tutor-tutee pairings and learning environments. In this paper, we introduce the Curiosity Notebook, a web-based research infrastructure for studying learning by teaching via the use of a teachable agent. We describe and provide rationale for the set of features that are essential for such a research infrastructure, outline how these features have evolved over two design iterations of the Curiosity Notebook and through two studies---a 4-week field study with 12 elementary school students interacting with a NAO robot and an hour-long online observational study with 41 university students interacting with an agent---demonstrate the utility of our platform for making observations of learning-by-teaching phenomena in diverse learning environments. Based on these findings, we conclude the paper by reflecting on our design evolution and envisioning future iterations of the Curiosity Notebook.

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With the rapid deployment of Internet of Things (IoT) technologies, it has been essential to address the security and privacy issues through maintaining transparency in data practices, and designing new tools for data protection. To address these challenges, the prior research focused on identifying user's privacy preferences in di↵erent contexts of IoT usage, user's mental model of security threats, and their privacy practices for a specific type of IoT device (e.g., smart speaker). However, there is a dearth in existing literature to understand the mismatch between user's perceptions and the actual data practices of IoT devices. Such mismatches could lead users unknowingly sharing their private information, exposing themselves to unanticipated privacy risks. To address these issues, we conducted a lab study with 42 participants, where we compared the data practices stated in the privacy policy of 28 IoT devices with the participants' perceptions of data collection, sharing, and protection. Our findings provide insights into the mismatched privacy perceptions of users, which lead to our recommendations on designing simplified privacy notice by highlighting the unexpected data practices.