Search for: All records

The phenomenon of the sea level rise is a pressing environmental and social issue of the present age. Starting with the assumption that mathematics can be utilized to help students explore this phenomenon, we designed a simulation in NetLogo, in which students investigated the relationships between the quantities of temperature rise, height of future sea level, and total land area. In this paper, we present the analysis of a whole-class design experiment in a sixth-grade classroom and discuss how our design helped students to examine sea level rise as both an environmental and a social issue. 

This research study was designed to evaluate the extent and the ways in which sixth-grade students developed their reasoning about the greenhouse effect and covariation as a result of their engagement with an instructional module that seamlessly integrates environmental science, mathematics, and technology. Quantitative and qualitative data were obtained from a design experiment in two sixth-grade classrooms and were compared to the data from a control group of students in a third sixth-grade classroom. The results from the quantitative analysis indicated that students in the treatment group demonstrated a greater development than the control group. The findings from the qualitative analysis illustrated that students developed sophisticated forms of reasoning about the greenhouse effect and covariation through their engagement with dynamic simulations and careful task design that prompted students to explore the covariational relationships underlying the science of the greenhouse effect. We consider the design of this instructional module to be valuable for future efforts to develop integrated science, technology, engineering, and mathematics (STEM) modules. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.