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Smart home technologies are making their way into families. Parents' and children's shared use of smart home technologies has received growing attention in CSCW and related research communities. Families and children are also frequently featured as target audiences in smart home product marketing. However, there is limited knowledge of how exactly children and family interactions are portrayed in smart home product marketing, and to what extent those portrayals align with the actual consideration of children and families in product features and resources for child safety and privacy. We conducted a content analysis of product websites and online resources of 102 smart home products, as these materials constitute a main marketing channel and information source about products for consumers. We found that despite featuring children in smart home marketing, most analyzed product websites did not mention child safety features and lacked sufficient information on how children's data is collected and used. Specifically, our findings highlight misalignments in three aspects: (1) children are depicted as users of smart home products but there are insufficient child-friendly product features; (2) harmonious child-product co-presence is portrayed but potential child safety issues are neglected; and (3) children are shown as the subject of monitoring and datafication but there is limited information on child data collection and use. We discuss how parent-child relationships and parenting may be negatively impacted by such marketing depictions, and we provide design and policy recommendations for better incorporating child safety and privacy considerations into smart home products. 

Sunflowers are famous for their ability to track the sun throughout the day and then reorient at night to face east the following morning. This occurs by differential growth patterns, with the east sides of stems growing more during the day and the west sides of stems growing more at night. This process, termed heliotropism, is generally believed to be a specialized form of phototropism; however, the underlying mechanism is unknown. To better understand heliotropism, we compared gene expression patterns in plants undergoing phototropism in a controlled environment and in plants initiating and maintaining heliotropic growth in the field. We found the expected transcriptome signatures of phototropin-mediated phototropism in sunflower stems bending towards monochromatic blue light. Surprisingly, the expression patterns of these phototropism-regulated genes are quite different in heliotropic plants. Most genes rapidly induced during phototropism display only minor differences in expression across solar tracking stems. However, some genes that are both rapidly induced during phototropism and are implicated in growth responses to foliar shade are rapidly induced on the west sides of stems at the onset of heliotropism, suggesting a possible role for red light photoreceptors in solar tracking. To test the involvement of different photoreceptor signaling pathways in heliotropism, we modulated the light environment of plants initiating solar tracking. We found that depletion of either red and far-red light or blue light did not hinder the initiation or maintenance of heliotropism in the field. Together, our results suggest that the transcriptional regulation of heliotropism is distinct from phototropin-mediated phototropism and likely involves inputs from multiple light signaling pathways. 

Peer assessment, as a form of collaborative learning, can engage students in active learning and improve their learning gains. However, current teaching platforms and programming environments provide little support to integrate peer assessment for in-class programming exercises. We identified challenges in conducting such exercises and adopting peer assessment through formative interviews with instructors of introductory programming courses. To address these challenges, we introduce PuzzleMe, a tool to help Computer Science instructors to conduct engaging in-class programming exercises. PuzzleMe leverages peer assessment to support a collaboration model where students provide timely feedback on their peers' work. We propose two assessment techniques tailored to in-class programming exercises: live peer testing and live peer code review. Live peer testing can improve students' code robustness by allowing them to create and share lightweight tests with peers. Live peer code review can improve code understanding by intelligently grouping students to maximize meaningful code reviews. A two-week deployment study revealed that PuzzleMe encourages students to write useful test cases, identify code problems, correct misunderstandings, and learn a diverse set of problem-solving approaches from peers.