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Many coding environments for young children involve using navigational arrow codes representing four movements: forward, backwards, rotate left, and rotate right. Children interpreting these four, seemingly simple codes encounter a complex interaction of spatial thinking and semantic meaning. In this study of how children interpret directional arrows, we found that they interpret each of the arrows as encoding many meanings and that the orientation of the agent plays a critical role in children’s interpretations. Through iterative rounds of qualitative coding and drawing on two examples, we unpack some common interpretations. 

In July 2021, Computer Science (CS) standards were officially added as a subject area within the K-12 Montana content standards. However, due to a lack of professional development and pre-service preparation in CS, schools and teachers in Montana are underprepared to implement these standards. Montana is also a unique state, since AmericanIndian education is mandated by the state constitution in what is known as the IndianEducation for All Act. We are developing elementary and middle school units and teacher training materials that simultaneously address CS, Indian Education, and other Montana content standards. In this paper, we present a unit for fourth through sixth grades using a participatory design approach. Through physical computing, students create a visual narrative of their own stories inspired by ledger art, an American Indian art medium for recording lived experiences. We discuss the affordances and challenges of an integrated approach to CS teaching and learning in elementary and middle schools in Montana. 

In this paper, we present work on bringing multimodal interaction to Minecraft. The platform, Multicraft, incorporates speech-based input, eye tracking, and natural language understanding to facilitate more equitable gameplay in Minecraft. We tested the platform with elementary, middle school students and college students through a collection of studies. Students found each of the provided modalities to be a compelling way to play Minecraft. Additionally, we discuss the ways that these different types of multimodal data can be used to identify the meaningful spatial reasoning practices that students demonstrate while playing Minecraft. Collectively, this paper emphasizes the opportunity to bridge a multimodal interface with a means for collecting rich data that can better support diverse learners in non-traditional learning environments. 

Instance segmentation of neural cells plays an important role in brain study. However, this task is challenging due to the special shapes and behaviors of neural cells. Existing methods are not precise enough to capture their tiny structures, e.g., filopodia and lamellipodia, which are critical to the understanding of cell interaction and behavior. To this end, we propose a novel deep multi-task learning model to jointly detect and segment neural cells instance-wise. Our method is built upon SSD, with ResNet101 as the backbone to achieve both high detection accuracy and fast speed. Furthermore, unlike existing works which tend to produce wavy and inaccurate boundaries, we embed a deconvolution module into SSD to better capture details. Experiments on a dataset of neural cell microscopic images show that our method is able to achieve better per- formance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods. 

Our broad research goal is to understand how human societies adapt to natural hazards, such as droughts and floods, and how their social and cultural structures are shaped by these events. Here we develop meteorological data of extreme dry, wet, cold, and warm indices relative to 96 largely nonindustrial societies in the worldwide Standard Cross-Cultural Sample to explore how well the meteorological data can be used to hindcast ethnographically reported drought and flood events and the global patterns of extremes. We find that the drought indices that are best at hindcasting ethnographically reported droughts [precipitation minus evaporation (P − E) measures] also tend to overpredict the number of droughts, and therefore we propose a combination of these two indices plus the PDSI as an optimal approach. Some wet precipitation indices (R10S and R20S) are more effective at hindcasting ethnographically reported floods than others. We also calculate the predictability of those extreme indices and use factor analysis to reduce the number of variables so as to discern global patterns. This work highlights the ability to use extreme meteorological indices to fill in gaps in ethnographic records; in the future, this may help us to determine relationships between extreme events and societal response over longer time scales than are otherwise available.