Search for: All records

This presentation shares the ongoing work of the Advancing Culturally-Relevant Computing Project, a researcher-practitioner partnership situated in the diverse multicultural context of Hawai'i. Focused on the topic of culturally-relevant computing, the project aims to better understand how to prepare elementary teachers to integrate Computer Science into their classrooms in ways that are effective, efficient, and meaningful. The presentation introduces culturally-relevant computing, showcases examples of culturally-relevant computing lessons, and shares highlights from a three-day professional development workshop for elementary teachers. 

This presentation reports a mixed-methods study examining how in- service Computer Science (CS) teachers working in Hawai'i perceive the concept of culturally-relevant computing. Data for the study came from a survey sent to CS teachers (n = 19) and focus group interviews with a subset of respondents (n = 10). Analysis of the data revealed a snapshot of teachers’ beliefs about culturally-relevant computing, as well as their current practices related to culturally-relevant pedagogy in the domain of CS. Detailed findings will be presented along with a discussion of the considerations and challenges in-service teachers face when planning and implementing culturally-relevant CS lessons. 

During explosive volcanic eruptions, volcanic ash is ejected into the atmosphere, impacting aircraft safety and downwind communities. These volcanic clouds tend to be dominated by fine ash (<63 μm in diameter), permitting transport over hundreds to thousands of kilometers. However, field observations show that much of this fine ash aggregates into clusters or pellets with faster settling velocities than individual particles. Models of ash transport and deposition require an understanding of aggregation processes, which depend on factors like moisture content and local particle collision rates. In this study, we develop a Plume Model for Aggregate Prediction, a one‐dimensional (1D) volcanic plume model that predicts the plume rise height, concentration of water phases, and size distribution of resulting ash aggregates from a set of eruption source parameters. The plume model uses a control volume approach to solve mass, momentum, and energy equations along the direction of the plume axis. The aggregation equation is solved using a fixed pivot technique and incorporates a sticking efficiency model developed from analog laboratory experiments of particle aggregation within a novel turbulence tower. When applied to the 2009 eruption of Redoubt Volcano, Alaska, the 1D model predicts that the majority of the plume is over‐saturated with water, leading to a high rate of aggregation. Although the mean grain size of the computed Redoubt aggregates is larger than the measured deposits, with a peak at 1 mm rather than 500 μm, the present results provide a quantitative estimate for the magnitude of aggregation in an eruption.