The project aims to understand how engagement in learning mathematics is enhanced by a digital collaborative platform with an embedded problem-based curriculum and a digital mathematics notebook. We investigate how engagement and learning is affected over a year-long seventh grade course. Using digital notebooks, platform analytics, and classroom artifacts, we report on a set of initial design characteristics for digital platforms that support student collaboration in learning mathematics.

NLP-TIPS takes advantage of natural language processing (NLP) methods to detect students’ ideas in written science explanations. We design adaptive guidance that supports each student to consider their own ideas and pursue deeper understanding of phenomena. This work continues a successful partnership between University of California, Berkeley, Educational Testing Service (ETS), and science teachers from schools enrolling students from diverse racial, ethnic, and linguistic groups whose cultural experiences may be neglected in science instruction.

The purpose of the Animated Contrasting Cases in Geometry (ACinG) research is two-fold: To design animated mathematics curricular materials for geometry that focus on directly comparing different approaches to solving the same geometry problem, and to examine the effects of these materials on student learning of geometry.

Socioscientific Issues (SSI) are ill-defined problems and debatable issues that can enhance student learning of scientific knowledge and literacy skills (Zeidler et al., 2005). However, most teachers are unfamiliar with planning and implementation of SSI (Macalalag et. al, 2019). Our project aimed to support the development of teachers’ pedagogical content knowledge through planning and implementation of SSI lessons in their classrooms.

Improving the quality of teaching is essential to improving student outcomes. BSCS Science Learning and the University of Minnesota STEM Education Program Area adapted an effective in-person professional learning model to utilize an online approach. The adaptation of STeLLA—Science Teachers Learning from Lesson Analysis—is important because it can reach a broader audience. To further promote the reach of STeLLA, the online version prepared local leaders to lead the program.

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.

This project aims to support teachers to engage their students in mathematical problem posing (problem-posing-based learning, or P-PBL). P-PBL is a powerful approach to the teaching and learning of mathematics, and provides students with opportunities to engage in authentic mathematical practices.

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 is conducting research on the modes of interaction that effectively prepare K-2 teachers to engage in reflective inquiry about their students’ emergent use of computational thinking strategies, and embed those learnings within a CoP platform to support scalable teacher professional development.