skip to main content

Title: Science in action or science inaction? Evaluating the implementation of "best available science” in hydropower relicensing
Award ID(s):
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Energy Policy
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Facility with foundational practices in computer science (CS) is increasingly recognized as critical for the 21st century workforce. Developing this capacity and broadening participation in CS disciplines will require learning experiences that can engage a larger and more diverse student population (Margolis et al., 2008). One promising approach involves including CS concepts and practices in required subjects like science. Yet, research on the scalability of educational innovations consistently demonstrates that their successful uptake in formal classrooms depends on teachers’ perceived alignment of the innovations with their goals and expectations for student learning, as well as with the specific needs of their school context and culture (Blumenfeld et al., 2000; Penuel et al., 2007; Bernstein et al., 2016). Research is nascent, however, about how exactly to achieve this alignment and thereby position integrated instructional models for uptake at scale. To contribute to this understanding, we are developing and studying two units for core middle school science classrooms, known as Coding Science Internships. The units are designed to support broader participation in CS, with a particular emphasis on females, by expanding students’ perception of the nature and value of coding. CS and science learning are integrated through a simulated internship model, in which students, as interns, apply science knowledge and use computer programming as a tool to address real-world problems. In one unit, students gain first-hand experience with sequences, loops, and conditionals as they program and debug an interactive scientific model of a coral reef ecosystem under threat. The second unit engages students in learning concepts related to data analysis and visualization, abstraction, and modularity as they code data visualizations using real EPA air quality data. A core goal for both units is to provide students experience with some of the increasingly prevalent ways that computer science is integrated into the work of scientists. 
    more » « less
  2. null (Ed.)
    Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations. 
    more » « less
  3. null (Ed.)
  4. null (Ed.)
    We will present emerging findings from an ongoing study of instruction at the intersection of science and computer science for middle school science classrooms. This paper focuses on student knowledge and dispositional outcomes in relation to a 2 week/10-lesson learning sequence. Instruction aims to broaden participation in STEM pathways through a virtual simulated internship in which students inhabit the role of interns working to develop a restoration plan to improve the health of coral reef populations. Through this collaborative work, students construct understanding of biotic and abiotic interactions within the reef and develop a computational model of the ecosystem. Analysis of pre/post surveys for n=381 students revealed that students who participated in the 2 week/10 lesson integrated computational thinking in science learning sequence demonstrated significant learning gains on an external measure of CT (0.522***; effect size=0.32). Drawing on scales from the Activation Lab suite of measures, pre/post surveys revealed increased competency beliefs about computer programming (mean difference =1.13***; effect size=1.01), and increased value assigned to STEM (0.78***; effect size=0.945). We also discuss the design of the instructional sequence and the theoretical framework for its development. 
    more » « less