Noyce Microelectronics Master Teacher Fellowship

Photo of teachers at a table making a quiz sheet using circuitry to a light a bulb to show the right answer.

Program Overview

Funded by the National Science Foundation and supported by SCALE K-12, the Noyce Microelectronics (ME) Master Teacher Fellowship at Purdue University was designed to develop K–12 STEM teacher leaders to meet emerging ME workforce needs.

We partnered with Regional Opportunity Initiatives, Naval Surface Warfare Center – Crane Division, and the Silicon Crossroads Microelectronics Commons Hub to serve four school districts: Washington Community Schools, Lafayette School Corporation, Loogootee Community Schools, and Purdue Polytechnic High Schools.

Eighteen teachers were selected as ME Master Teacher Fellows to lead the integration of ME-focused curricula in high-need schools—sparking student interest in STEM learning and preparing the next generation for careers in the growing ME industry.

Meet the ME Master Teacher Fellows

Explore profiles of current and incoming Fellows and their school-based projects.

Photo of a group of teachers planning a presentation using chart paper.
Photo showing a person at a soldering station.

OBJECTIVES & ACTIVITIES

The Noyce ME Master Teacher Fellowship Program aims to meet the following objectives:

  • Enhance teachers’ ME content and integrated STEM pedagogical knowledge.

  • Retain effective elementary and secondary teachers in high-need districts.

  • Equip teachers with the credentials and experiences to serve as teacher leaders.

  • Increase student access to, and preparation for, ME careers.

ME Master Teacher Fellows will meet these objectives by engaging in the following activities:

  • Intensive ME content training

  • Graduate degree completion, if applicable

  • District-wide vertical alignment planning

  • School-based professional development leadership

  • Industry partnership development

  • Annual summits and networking

  • Long-term teacher retention commitments

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This material is based upon work supported by the National Science Foundation under Grant No. 1543175 through the STEM + Computing (STEM+C) program from the DRL division. Any opinions, findings, and conclusions or recommendations are those of the investigators and do not necessarily reflect the views of the National Science Foundation.