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Information seeking is crucial for people's self-care and wellbeing in times of public crises. Extensive research has investigated empirical understandings as well as technical solutions to facilitate information seeking by domestic citizens of affected regions. However, limited knowledge is established to support international migrants who need to survive a crisis in their host countries. The current paper presents an interview study with two cohorts of Chinese migrants living in Japan (N=14) and the United States (N=14). Participants reflected on their information seeking experiences during the COVID pandemic. The reflection was supplemented by two weeks of self-tracking where participants maintained records of their COVID-related information seeking practice. Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries. They also made strategic use of the Mandarin information to perform selective reading, cross-checking, and contextualized interpretation of COVID-related information in Japanese or English. While such practices enhanced participants' perceived effectiveness of COVID-related information gathering and sensemaking, they disadvantaged people through sometimes incognizant ways. Further, participants lacked the awareness or preference to review migrant-oriented information that was issued by the host country's public authorities despite its availability. Building upon these findings, we discussed solutions to improve international migrants' COVID-related information seeking in their non-native language and cultural environment. We advocated inclusive crisis infrastructures that would engage people with diverse levels of local language fluency, information literacy, and experience in leveraging public services. 

Global teams frequently consist of language-based subgroups who put together complementary information to achieve common goals. Previous research outlines a two-step work communication flow in these teams. There are team meetings using a required common language (i.e., English); in preparation for those meetings, people have subgroup conversations in their native languages. Work communication at team meetings is often less effective than in subgroup conversations. In the current study, we investigate the idea of leveraging machine translation (MT) to facilitate global team meetings. We hypothesize that exchanging subgroup conversation logs before a team meeting offers contextual information that benefits teamwork at the meeting. MT can translate these logs, which enables comprehension at a low cost. To test our hypothesis, we conducted a between-subjects experiment where twenty quartets of participants performed a personnel selection task. Each quartet included two English native speakers (NS) and two non-native speakers (NNS) whose native language was Mandarin. All participants began the task with subgroup conversations in their native languages, then proceeded to team meetings in English. We manipulated the exchange of subgroup conversation logs prior to team meetings: with MT-mediated exchanges versus without. Analysis of participants' subjective experience, task performance, and depth of discussions as reflected through their conversational moves jointly indicates that team meeting quality improved when there were MT-mediated exchanges of subgroup conversation logs as opposed to no exchanges. We conclude with reflections on when and how MT could be applied to enhance global teamwork across a language barrier. 

We propose a five-step computational framing analysis framework that researchers can use to analyze multilingual news data. The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual deep learning model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Most importantly, anyone can perform the proposed computational framing analysis using a free, open-sourced system, created by a team of communication scholars, computer scientists, web designers and web developers. Making advanced computational analysis available to researchers without a programming background to some degree bridges the digital divide within the communication research discipline in particular and the academic community in general. 

Flood mitigation governance is critical for coastal regions where flooding has caused considerable damage. Raising the First-Floor Elevation (FFE) above the base flood elevation (BFE) is an effective mitigation measure for buildings with a high risk of flooding. In the U.S., measuring FFE is necessary to obtain an Elevation Certificate (E.C.) for the National Flood Insurance Program (NFIP) and has traditionally required labor-consuming field surveys. However, the advances in computer vision technology have facilitated the handling of large image datasets, leading to new FFE measurement approaches. Taking Galveston Island (including the cities of Galveston and Jamaica Beach) in Coastal Texas as a case study, we explore how these new approaches may inform flood risk management and governance, including how FFE estimates may be combined with BFE estimates from flood inundation probability mapping to model the predicted cost of raising buildings’ FFE above their BFE. After establishing the FFE model’s accuracy by comparing its results with previously validated FFE estimates in three districts of Galveston, we generalize the workflow to building footprints across Galveston Island. By combining the FFE data derived from our workflow with multidimensional building information, we further analyze the future flood control and post-disaster maintenance strategies. Our findings present valuable data collection paradigms and methodological concepts that inform flood governance for Galveston Island. The proposed workflow can be extended to flood management and research for other vulnerable coastal communities.

 

A plant can be thought of as a colony comprising numerous growth buds, each developing to its own rhythm. Such lack of synchrony impedes efforts to describe core principles of plant morphogenesis, dissect the underlying mechanisms, and identify regulators. Here, we use the minimalist known angiosperm to overcome this challenge and provide a model system for plant morphogenesis. We present a detailed morphological description of the monocot Wolffia australiana, as well as high-quality genome information. Further, we developed the plant-on-chip culture system and demonstrate the application of advanced technologies such as single-nucleus RNA-sequencing, protein structure prediction, and gene editing. We provide proof-of-concept examples that illustrate how W. australiana can decipher the core regulatory mechanisms of plant morphogenesis.

 

Students often get stuck when programming independently, and need help to progress. Existing, automated feedback can help students progress, but it is unclear whether it ultimately leads to learning. We present Step Tutor, which helps struggling students during programming by presenting them with relevant, step-by-step examples. The goal of Step Tutor is to help students progress, and engage them in comparison, reflection, and learning. When a student requests help, Step Tutor adaptively selects an example to demonstrate the next meaningful step in the solution. It engages the student in comparing "before" and "after" code snapshots, and their corresponding visual output, and guides them to reflect on the changes. Step Tutor is a novel form of help that combines effective aspects of existing support features, such as hints and Worked Examples, to help students both progress and learn. To understand how students use Step Tutor, we asked nine undergraduate students to complete two programming tasks, with its help, and interviewed them about their experience. We present our qualitative analysis of students' experience, which shows us why and how they seek help from Step Tutor, and Step Tutor's affordances. These initial results suggest that students perceived that Step Tutor accomplished its goals of helping them to progress and learn.