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Abstract Despite a growing movement towards expanding computer science education in PreK‐12, gaps in computing opportunities along lines of race, ethnicity, class, and gender have widened. Emergent theories and practices related to culturally responsive computing show promise in addressing this gap; however, little is known about engaging Black, Latinx, and Indigenous preschoolers in computer science. In this paper, we utilized qualitative content analyses to explore how an early childhood computer science curriculum created opportunities for young Black and Latinx preschoolers to develop computational thinking skills while engaging in culturally responsive computing. Overwhelmingly, we found the curriculum, co‐developed with educators and caregivers, emphasized unplugged tools and coding activities to support computational thinking. These unplugged opportunities positioned children as innovators with technology and technosocial change agents, in developmentally‐appropriate, play‐based ways. Findings demonstrate a need to emphasize the value of unplugged tools and coding activities in order to support computational thinking and align the goals of culturally responsive computing with the unique needs of young children. We discuss implications for a theory of culturally responsive computing specifically for early childhood education. 

Background Large (>1 Mb), polymorphic inversions have substantial impacts on population structure and maintenance of genotypes. These large inversions can be detected from single nucleotide polymorphism (SNP) data using unsupervised learning techniques like PCA. Construction and analysis of a feature matrix from millions of SNPs requires large amount of memory and limits the sizes of data sets that can be analyzed. Methods We propose using feature hashing construct a feature matrix from a VCF file of SNPs for reducing memory usage. The matrix is constructed in a streaming fashion such that the entire VCF file is never loaded into memory at one time. Results When evaluated on Anopheles mosquito and Drosophila fly data sets, our approach reduced memory usage by 97% with minimal reductions in accuracy for inversion detection and localization tasks. Conclusion With these changes, inversions in larger data sets can be analyzed easily and efficiently on common laptop and desktop computers. Our method is publicly available through our open-source inversion analysis software, Asaph.