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Using Problem-Based Learning Analytics to Investigate Individual and Collaborative Mathematics Learning in a Digital Environment Over Time

Principal Investigators:

  • Elizabeth Phillips (Michigan State University),
  • AJ Edson (Michigan State University),
  • Kristen Bieda (Michigan State University),
  • Chad Dorsey (Concord Consortium), and
  • Nathan Kimball (Concord Consortium).

Funding Source: National Science Foundation

Award Number: DRL-2200763

Dates: September 1, 2022 to August 31, 2026 (estimated)

Amount: $2.99 million

Project Partners: Concord Consortium

This project will explore two key questions related to student learning and collaboration in the technological platform: how does the platform enhance students' individual and collaborative engagement, and how do student learning outcomes, engagement, and attitudes develop over an academic year in which the platform is used repeatedly. The project will use the platform to collect data related to student collaboration, engagement, instructional practices, classroom artifacts, and written and spoken mathematical reflections from students and teachers. The research outcomes will include guidelines for a research-based approach to the use of learning analytics in technology-enhanced mathematics education, an understanding of design features that support meaningful STEM learning using digital notebooks, and the nature of the teacher-student boundaries in using digital learning resources to support students' individual needs. The project will focus on a deep examination of a key mathematical idea in the seventh-grade curriculum, proportional reasoning, to understand how students' understandings are influenced over time using the platform. Project activities will include development and testing of the platform with students, pilot and field testing with teachers, and professional learning opportunities for teachers to ensure thoughtful implementation of the platform in practice.


NSF LogoThis work was supported by the National Science Foundation grant DRL-2200763. Any opinions, findings, and conclusions or recommendations expressed in the material are those of the authors and do not necessarily reflect the views of the National Science Foundation.