ECR Projects

Explore past and current fundamental STEM education research projects across the three research areas that NSF's EDU Core Research (ECR) program funds, as well as across ECR funding types. Other search filters draw from both NSF's data and the ECR Hub's hand coding of award abstracts.

Ninth-grade biology students create cell models using clay.

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STEM Learning and Learning Environments STEM Learning and Learning Environments  

Grasping Understandings of Students Mathematical and Perceptual Strategies Using Real-Time Teacher Orchestration Tools

Effective Years: 2022-2027

Many middle and high school students in the United States do not reach proficiency in algebra. When solving algebraic expressions and equations, students not only need to perform procedures, but also identify mathematical structure, attend to important perceptual cues, and make decisions about which steps are most appropriate or productive in a particular problem context. Math teachers are critical to supporting and improving students’ math achievement by providing high-quality feedback, instruction, discourse, and opportunities to their students. However, many teachers struggle to find algebra-based teaching tools that efficiently provide a means to challenge students to think conceptually, keep their students engaged, review student work efficiently in real-time, and better support their instruction. This project focuses on the design, development, and use of new algebra-focused teacher tools that use artificial intelligence (AI) to efficiently provide teachers with detailed information about their students’ math problem solving steps, behaviors, errors, and learning in real-time. The underlying hypothesis is that if teachers are given detailed information and feedback about their students’ perceptual and mathematical processes using real-time analytics, teachers will better notice and interpret student struggles. In turn, teachers will be able to make better decisions and differentiate their instruction for a broader range of students.

The main research question is to determine whether teachers are better able to detect, attend to, interpret, and make actionable decisions when using the AI-supported tool. Researchers will conduct a sequence of activities during this five-year project. First, to determine what behaviors best predict learning, a database of log files generated from students solving problems will be analyzed using statistical and learning analytics methods. Next, researchers will utilize machine learning approaches to create automated detectors that capture the use of effective math strategies, errors, and focus that has led to improved learning. Third, the project will use design-based research alongside teachers to co-design, develop, and prototype AI-supported teacher tools. The tools provide critical information about students’ mathematical and perceptual processes and help teachers quickly identify what gaps students have in their math knowledge. The researchers will conduct classroom-based observations and interviews to examine how teachers’ instruction and students’ understanding might be altered with the real-time tools and feedback. The outcome of the project will advance theories and foundational research in the fields of learning science, computational data science, human-computer interaction, and math education, as well as offer new insights into automatic detection of mathematical strategies and classroom orchestration. The technical and educational agendas also provide opportunities for interdisciplinary research and practical training and collaboration between graduate students, postdocs, teachers, and students. This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.