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How young people navigate multiple roles and identities while rehearsing to teach younger students to program robot Finches 

This article introduces the concept of Youth Pedagogical Development (YPD), defined as an ongoing process of youth learning to teach other youth as they engage with academic knowledge and pedagogical strategies. To conceptualize and provide empirical support for YPD, we look at three case studies with community-based organizations that teach young people how to teach and mentor in STEM. We examine how youth’s teaching, mentoring, and learning from other near-peer youth transforms how youth identify as STEM teachers, learners, and doers. We propose that this development of youth teaching other youth creates more humanizing learning spaces where Black and Brown youth feel supported, cared for, and agentic. 

https://doi.org/10.1002/essoar.10505803.1 

Anomaly detection methods abound and are used extensively in streaming settings in a wide variety of domains. But a strength can also be a weakness; given the vast number of methods, how can one select the best method for their application? Unfortunately, there is no one best way for all domains. Existing literature is focused on creating new anomaly detection methods or creating large frameworks for experimenting with multiple methods at the same time. As the literature continues to grow, extensive evaluation of every available anomaly detection method is not feasible. To reduce this evaluation burden, in this paper we present a framework to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays. We provide a comprehensive experimental validation of multiple anomaly detection methods over different time series characteristics to form guidelines. Applying our framework can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods.