As OECD has studied and reported, teacher learning needs to a) be evidence based, b) connect preservice with inservice learning, and c) align with school needs. A SSHRC funded two-year mixed methods study of Problem-Based Learning (PBL) as a professional learning model for secondary school mathematics preservice teachers in a mid-sized Ontario university’s teacher preparation program consisted of five data collection phases: a questionnaire with a mathematics beliefs scale, a teacher efficacy scale, and short answer questions, and audio recordings of the PBL team meetings. Statistical significance and qualitative themes indicate greater appreciation that mathematics is not only about drill and mechanical ability, and a greater sense of relevance regarding the mathematics they are teaching for students’ personal contexts. Preservice teachers also articulate the value of PBL as a learning model compared to the more common instructor-led constructivist learning experiences.

Bio:

Jamie S. Pyper is an Associate Professor of Mathematics Education, and the Coordinator of the Mathematics, Science, and Technology Group, at the Faculty of Education, Queen’s University. Prior to his time at Queen’s University, he spent 20 years teaching secondary school mathematics and mathematics education for preservice teachers. His research converges trajectories of teacher efficacy, mathematics discourse and professional literacy, and problem solving into the current project of professional learning models for preservice teacher education. He can be found on Twitter (@314_per) and on the web (mathperceptionproject.ca).

John Bosica, MScH, is a PhD candidate in mathematics education at the Faculty of Education, Queen’s University. John’s focus is primarily in preservice teacher education at the elementary and high school level.

Stephen MacGregor, M.Ed., is a PhD candidate at the Faculty of Education, Queen’s University. His research focuses on how education stakeholders, particularly higher education institutions, can build their capacity in knowledge mobilization to enhance and accelerate research impact. His research draws from the traditions of mixed methods and social network theory in order to model and describe the flows of information and resources in multi-stakeholder networks.