Search for: All records

Artificial Intelligence (AI) enhanced systems are widely adopted in post-secondary education, however, tools and activities have only recently become accessible for teaching AI and machine learning (ML) concepts to K-12 students. Research on K-12 AI education has largely included student attitudes toward AI careers, AI ethics, and student use of various existing AI agents such as voice assistants; most of which has focused on high school and middle school. There is no consensus on which AI and Machine Learning concepts are grade-appropriate for elementary-aged students or how elementary students explore and make sense of AI and ML tools. AI is a rapidly evolving technology and as future decision-makers, children will need to be AI literate[1]. In this paper, we will present elementary students’ sense-making of simple machine-learning concepts. Through this project, we hope to generate a new model for introducing AI concepts to elementary students into school curricula and provide tangible, trainable representations of ML for students to explore in the physical world. In our first year, our focus has been on simpler machine learning algorithms. Our desire is to empower students to not only use AI tools but also to understand how they operate. We believe that appropriate activities can help late elementary-aged students develop foundational AI knowledge namely (1) how a robot senses the world, and (2) how a robot represents data for making decisions. Educational robotics programs have been repeatedly shown to result in positive learning impacts and increased interest[2]. In this pilot study, we leveraged the LEGO® Education SPIKE™ Prime for introducing ML concepts to upper elementary students. Through pilot testing in three one-week summer programs, we iteratively developed a limited display interface for supervised learning using the nearest neighbor algorithm. We collected videos to perform a qualitative evaluation. Based on analyzing student behavior and the process of students trained in robotics, we found some students show interest in exploring pre-trained ML models and training new models while building personally relevant robotic creations and developing solutions to engineering tasks. While students were interested in using the ML tools for complex tasks, they seemed to prefer to use block programming or manual motor controls where they felt it was practical. 

Abstract The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed‐watershed, perennially ice‐covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. We apply a physically based hydrological model that includes a surface energy balance model to estimate glacial melt and lake sublimation to constrain mass fluxes to and from the lakes. Results show that lake levels are very sensitive to small changes in glacier albedo, air temperature, and wind speed. We were able to balance the hydrologic budget in two watersheds using meltwater inflow and sublimation loss from the ice‐covered lake alone. A third watershed, closest to the coast, required additional inflow beyond model uncertainties. We hypothesize a shallow groundwater system within the active layer, fed by dispersed snow patches, contributes 23% of the inflow to this watershed. The lakes are out of equilibrium with the current climate. If the climate of our study period (1996–2013) persists into the future, the lakes will reach equilibrium starting in 2300, with levels 2–17 m higher, depending on the lake, relative to the 2020 level. 

By combining two surveys covering a large fraction of the molecular material in the Galactic disc, we investigate the role spiral arms play in the star formation process. We have matched clumps identified by APEX Telescope Large Area Survey of the Galaxy (ATLASGAL) with their parental giant molecular clouds (GMCs) as identified by SEDIGISM, and use these GMC masses, the bolometric luminosities, and integrated clump masses obtained in a concurrent paper to estimate the dense gas fractions (DGFgmc = ∑Mclump/Mgmc) and the instantaneous star formation efficiencies (i.e. SFEgmc = ∑Lclump/Mgmc). We find that the molecular material associated with ATLASGAL clumps is concentrated in the spiral arms (∼60 per cent found within ±10 kms−1 of an arm). We have searched for variations in the values of these physical parameters with respect to their proximity to the spiral arms, but find no evidence for any enhancement that might be attributable to the spiral arms. The combined results from a number of similar studies based on different surveys indicate that, while spiral-arm location plays a role in cloud formation and H I to H2 conversion, the subsequent star formation processes appear to depend more on local environment effects. This leads us to conclude that the enhanced star formation activity seen towards the spiral arms is the result of source crowding rather than the consequence of any physical process.