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TRESTLE was a multi-institution, NSF-funded project that studied and implemented a model for improving STEM education at public research universities. Each institution tested a local adaptation of a model with three core components:

  1. Support discipline-based educational experts in departments to catalyze course transformation.
  2. Build intellectual communities around evidence-based educational improvement, within and across departments and institutions
  3. Collect and make visible evidence of the impact on teaching and learning.

TRESTLE is one of several collaborations of the Bay View Alliance. The project embodies the core principles of the BVA, including the idea that the academic department or program is a key unit for implementing and evaluating interventions to improve the culture of teaching and learning.

The TRESTLE project built on the successful Science Education Initiative (SEI) from UBC and CU. The SEI model centered on the development of “embedded expertise,” or postdoctoral Teaching Fellows (TFs), in STEM departments, with a focus on course transformation, to catalyze changes in teaching practices and culture. TRESTLE tested a model initially piloted at the University of Kansas that used significantly fewer resources (including fewer experts in a given department) than the SEI. To amplify the effects of the embedded experts, TRESTLE added an emphasis on building community at multiple levels including the formation of a cross-institutional network, and on making transformed teaching, and improved student learning, visible within the communities. Different campuses also employed varied forms of pedagogical expertise, including department-embedded postdoctoral fellows (KU, QU), faculty leaders (CU, IUB), and partnerships between Education faculty members and STEM faculty members (UTSA).

Project Status

Our current work is studying the impact of our interventions to identify lessons about both cross-cutting strategies and how to adapt strategies to local institutional context.


This material is based upon work supported by the National Science Foundation under Grant Number DUE1525775 (KU), DUE1525331 (CU) and DUE1525345 (UTSA). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.