Computer science's multidisciplinary importance is becoming widely recognized, and few fields are seeing this change more rapidly than agriculture. One example is the advent of precision agriculture, which leverages advancements in technology to monitor crops and livestock and precisely apply nutrients, herbicides, etc. for the overall health of the crop. Such innovations will upset the status quo, creating an opportunity for greater equity in emerging Computer Science job markets. However, opportunities in Computer Science are not equitably distributed both socioeconomically and geographically, with most opportunities existing in wealthier metropolitan areas. We surveyed and interviewed K-12 agriculture teachers from Rural Title 1 schools in North Carolina and interviewed them about their experiences, visions of the future of agriculture, emerging AgTech economies, and difficulties they had adjusting to these shifting agricultural domains in their teaching. These teachers form the first phase of the Rural CS+Agriculture Alliance, which will focus on the creation of curriculum to support the integration of computer science topics into rural agriculture classrooms.

Intelligent virtual agents have tremendous potential for facilitating collaborative learning by modeling and reinforcing desirable collaborative practices. Despite recent work in this area, the extent to which intelligent virtual agents can facilitate improvements in the collaborative behavior of children is largely unknown. This study employed a wizard-of-oz study design and investigated elementary children's collaborative behavior after interacting with virtual agents. These agents model exploratory talk for upper elementary school dyads, such as asking higher-order questions and listening to their partners. The findings uncover associations between elementary learner dyads' positive changes in collaboration after agent interventions, the dyads' affective reactions to interventions, and their attentiveness to the agents. Our results also reveal associations between positive changes in collaboration and the timing of interventions: for example, earlier interventions had a higher occurrence of positive changes, and positive changes in collaboration typically happened within five seconds of interventions. The results suggest ways in which intelligent virtual agents may be used to promote effective collaborative learning practices for children.

The COVID-19 pandemic shifted many U.S. schools from in-person to remote instruction. While collaborative CS activities had become increasingly common in classrooms prior to the pandemic, the sudden shift to remote learning presented challenges for both teachers and students in implementing and supporting collaborative learning. Though some research on remote collaborative CS learning has been conducted with adult learners, less has been done with younger learners such as elementary school students. This experience report describes lessons learned from a remote after-school camp with 24 elementary school students who participated in a series of individual and paired learning activities over three weeks. We describe the design of the learning activities, participant recruitment, group formation, and data collection process. We also provide practical implications for implementation such as how to guide facilitators, pair students, and calibrate task difficulty to foster collaboration. This experience report contributes to the understanding of remote CS learning practices, particularly for elementary school students, and we hope it will provoke methodological advancement in this important area.