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TopProject-Set Teacher Learning Trajectories
The basis of this paper comes from experiences in the National Science Foundation (NSF) funded Project-SET (grant no. 1119016). The four-year project developed two TLTs and designed a professional development program around the TLTs. The goal of Project-SET was to enhance teachers’ content knowledge of two fundamental statistics topics – sampling variability and regression. The construction of the TLTs happened iteratively as the TLTs were refined after each professional development implementations (see Figure 3). Overall, the Project-SET TLTs were designed to conceptualize teacher knowledge in statistics. Project-SET then used these TLTs to design and structure a professional development curriculum.
Project-SET used Clements’ and Sarama’s (2004) conceptualization of a learning trajectory as a “learning goal, developmental progression of thinking and learning, and sequence of instructional tasks” to guide the TLT development. The learning goal of the Project-SET TLTs centered around teachers’ content knowledge of sampling variability and regression. This learning goal was manifested through three design principles: (1) teacher learning should adhere to widely accepted models of statistics practice; (2) teacher learning should progress from informal notions of the content to formal understandings; (3) and teacher learning in statistics should incorporate the use of technology. These design principles guided the development of the progression and the instructional tasks.
Several papers and reports in the statistics education literature discuss models for statistical practice (Bargagliotti & Anderson, 2017; Franklin et al., 2007; Wild and Pfannkuch, 1999). Project-SET used the model presented by Franklin et al. (2007) in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Pre-K12 Report. The GAISE model articulates the statistical process in four components: formulate questions, collect data, analyse data, and interpret data (see Figure 1). The progression of the TLT was then organized around the components of this statistical process.