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Teachers today have increasing access to professional learning communities (PLCs) through a rapidly expanding menu of online professional development offerings. While a valued opportunity for growth, online PLCs can limit opportunities for co-teaching, pedagogical practice, and experiential learning. This paper examines a teacher professional development program implemented in 2022, where 14 middle school teachers joined either an online or an in-person version of a summer practicum in which PLCs were fostered. In both versions of the PD, teachers worked in small teams of co-teachers to learn and practice teaching middle school students about Artificial Intelligence (AI), a topic in which teachers were non-experts. Findings from qualitative analysis of teacher interviews suggest affordances and barriers to teacher learning online as compared to in-person PLCs. The paper offers recommendations for online PLC structure and co-teaching to enhance teacher learning. 

Teachers today have increasing access to professional learning communities (PLCs) through a rapidly expanding menu of online professional development offerings. While a valued opportunity for growth, online PLCs can limit opportunities for co-teaching, pedagogical practice, and experiential learning. This paper examines a teacher professional development program implemented in 2022, where 14 middle school teachers joined either an online or an in-person version of a summer practicum in which PLCs were fostered. In both versions of the PD, teachers worked in small teams of co-teachers to learn and practice teaching middle school students about Artificial Intelligence (AI), a topic in which teachers were non-experts. Findings from qualitative analysis of teacher interviews suggest affordances and barriers to teacher learning online as compared to in-person PLCs. The paper offers recommendations for online PLC structure and co-teaching to enhance teacher learning. 

This paper proposes SmartBench, a benchmark focusing on queries resulting from (near) real-time applications and longer-term analysis of IoT data. SmartBench, derived from a deployed smart building monitoring system, is comprised of: 1) An extensible schema that captures the fundamentals of an IoT smart space; 2) A set of representative queries focusing on analytical tasks; and 3) A data generation tool that generates large amounts of synthetic sensor and semantic data based on seed data collected from a real system. We present an evaluation of seven representative database sys- tems and highlight some interesting findings that can be considered when deciding what database technologies to use under different types of IoT query workloads.