Science

The purpose of this project is to gather, analyze, and synthesize research studies that have investigated different approaches to supporting students in grades 6-14 in learning to analyze, interpret, and reason about data with a focus on variation and covariation. We will use Robust Variance Estimation (RVE) to examine how effect size estimates depend on intervention characteristics, study design, outcomes of interest, and demographic characteristics of participants in the studies.

We iteratively designed and tested a research-based professional learning (PL) approach to help middle school science teachers effectively support and sustain students’ motivational competencies during ambitious science instruction. A team of researchers and middle school science teachers co-designed a PL approach called M-PLANS (Motivation - Planning Lessons to Activate eNgagement in Science). Early testing suggests promise for impacts on teacher beliefs and behavior as well as students’ motivation, engagement, and science performance.

STRIDES supports science teachers to rapidly respond to the diverse students in their classrooms. Leveraging advances in natural language processing, the project analyzes student written explanations of scientific phenomena to provide fine-grained summaries to teachers about student knowledge integration across NGSS dimensions. STRIDES suggests learning science-based customizations and studies how teachers use the summaries and customization suggestions to improve student progress. The researchers study how well the customizations address the learning needs of diverse students.

This project includes research examining teachers’ customization processes and the development of tools to support teachers in adapting curriculum materials for their specific school context to facilitate equitable science sensemaking for all students. The research program includes: (1) Empirical study of teachers’ customization processes; (2) Theoretical model of teacher thinking that underlies customization; (3) Tools to support principled customization consistent with the goals of the reform; and (4) Empirical study of how tools influence teachers.

The LaCuKnoS project promotes a justice-centered approach to broadening participation in STEM by engaging teachers, students and families across Oregon in work to support language development for science meaning making, mapping cultural and community connections to science, and building knowledge for informed decision making.

This project seeks to support students to creatively combine science, engineering and social or community knowledge to address Earth Science problems in their local communities. Through a collaborative project based learning platform with built in scaffolding and an AI design mentor, students will engage in front-end design practices to understand and frame problems and explore solutions. We examine how this impacts science conceptual knowledge, design skills and creativity for middle and high school students.

Written science explanations are central to learning and practicing science. However, there are two main challenges: first, students struggle to explain their ideas and use supporting data appropriately. Second, it is not realistic for teachers to provide real-time, comprehensive feedback to each student. We will present how PyrEval, a natural language processing (NLP) technology, provided students with timely, personalized, automated feedback. We will discuss opportunities and challenges for using automated feedback in classrooms.

This project supports students’ writing of science explanations. One main goal is to develop novel, automated natural language processing (NLP) technology to provide middle school students and teachers with real-time feedback on their science essays, to help them reflect on the quality of their explanations, and to foster their ability to write scientific explanations grounded in scientific evidence. A science unit on roller coasters is presented to students in a web-based environment that includes a roller coaster simulation to conduct experiments.

This project studied the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions based on automatically generated student reports (AutoRs).

This project is developing and testing a high school climate change unit and professional learning experience in which teachers localize part of the unit to make it relevant to their students and community context. The study is using a cohort-control quasi-experimental design to examine the impact of the unit and professional learning experience on dimensions of teacher practice and students' sense of agency with environmental science.