UTeach faculty are experts in STEM and STEM teacher preparation. Their work in the field is published in peer-reviewed journals.

Alegre, F., Moreno, J., Dawson,T., Tanjong, E., & Kirshner, D. (2020). Computational thinking for STEM teacher leadership training at Louisiana State University. RESPECT 2020: Research on Equity and Sustained Participation in Engineering, Computing, and Technology. Portland, OR, United States, March 10–11, 2020.

Alegre, F., Moreno, J., Weltman, J., Kirshner, D., Underwood, J., Neubrander, F., & Madden, J. (2020). Introduction to Computational Thinking: a new high school curriculum using CodeWorld. In J. E. Goodell & S. Koc (Eds.), *Preparing STEM Teachers: A replication model*. Charlotte, NC: Information Age Publishing.

Ares, N., Stroup, W. M., & Schademan, A. R. (2009). The power of mediating artifacts in group-level development of mathematical discourses. *Cognition and Instruction*, *27*(1), 1–24.

Bendinelli, A. J., & Marder, M. (2012). Visualization of longitudinal student data. *Physical Review Special Topics–Physics Education Research,8*(2), 1–15.

Carrejo, D. J., & Marshall, J. (2007) What is mathematical modelling? Exploring prospective teachers' use of experiments to connect mathematics to the study of motion. *Mathematics Education Research Journal,* *19*(1), 45–76.

Confrey, J., & Makar, K. (2005). Critiquing and improving the use of data from high-stakes tests with the aid of dynamic statistics software. In C. Dede, J. Honan, & L. Peters (Eds.), *Scaling up for success: Lessons from technology-based educational improvement* (pp. 198–226). San Francisco: Jossey-Bass.

Confrey, J., Makar, K., & Kazak, S. (2004). Undertaking data analysis of student outcomes as professional development for teachers. *ZDM: International Reviews on Mathematics Education, 36*(1), 32–40.

Craig, C., Verma, R., Stokes, D., Evans, P., & Abrol, B. (2018). The influence of parents on undergraduate and graduate students’ entering the STEM disciplines and STEM careers, *International Journal of Science Education*,*40*(6), 621-643.

Craig, C., Evans, P., Stokes, D., Bott, S. (2017). Attracting, preparing and retaining teachers in areas of high need: A teaching science as inquiry model. In M. Peters, B. Cowie & I. Menter (Eds.), *A companion to research in teacher education*. New York, NY: Springer Publishing.

Dickinson, G., Summers, E. J., & Jackson, J. (2010). Developing expertise in project-based science: A longitudinal study of teacher development and student perceptions. In R. E. Yager (Ed.), *Exemplary* *science for* *resolving* *societal challenges* (pp. 1–18). Arlington, VA: National Science Teachers Association.

Dickinson, G., & Summers, E. J. (2010). (Re)Anchored, video-centered engagement: The transferability of preservice training to practice. *Contemporary Issues in Technology and Teacher Education, 10*(1)*, *106–118.

Enderle, P., Dentzau, M., Roseler, K., Southerland, S. A., Granger, E., & Hughes, R. (2014). Examining the influence of RET's on science teachers' beliefs and practice. *Science Education*, *98*, 1077–1108.

Evans, P. K., Dillard, K. C., Rodriguez-Wilhelm, D., & McAlister-Shields, L. (2019). Like-minded people: University-based interdisciplinary collaborations in STEM teacher preparation programs. *Journal for STEM Education Research, 2*(1), 35–54.

Farrell, I. K., & Hamed, K. M. (2017). Examining the relationship between technological pedagogical content knowledge (TPACK) and student achievement utilizing the Florida value-added model. *Journal of Research on Technology in Education*, *49*(3-4), 161–181.

Farrell, I. & Hamed, K. (2016). Teaching with soap: Examples of project-based units for students and future educators. *Science activities: Classroom projects and curriculum ideas, 53*(2) 74–86.

Fletcher, C. L., Warner, J. R., Garbrecht, L., & Ramsey, C. (2018). Lessons learned from developing a framework for evaluating the impact of CS teacher professional development on CS for All outcomes. Paper presented at 2018 AERA Annual Meeting, New York, NY.

Granger, E. M., Bevis, T., Saka, Y., Southerland, S. A., Sampson, V., & Tate, R. (2012). Efficacy of student-centered instruction in supporting student science learning. *Science*, *338*(6103), 105–108.

Hamilton, R. (2011, May 2). Is poverty, not teacher quality or charters, key to student outcomes? Interview with Michael Marder. *The Texas Tribune*.

Harron, J. (2018). Introducing preservice STEM teachers to computer science: A narrative of theoretically oriented design. *Texas Education Review, 6*(1), 1733. doi:10.15781/T20R9MM9X

Harron, J., Langdon, J., Gonzalez, J., & Cater, S. (2017) Digital forensics. *The Science Teacher,* *84(8), *32–36.

Horvath, M., Goodell, J., & Kosteas, V. (2018). Decisions to enter and continue in the teaching profession: Evidence from a sample of U.S. secondary STEM teacher candidates. *Teaching and Teacher Education, 71*, 57–65.

Houle, F., Kirby, K., & Marder, M. (2023). Ethics in physics: The need for culture change. *Physics Today*, *76*(1), 28.

Jett, C. C., Stinson, D. W., & Williams, B. A. (2015). Communities for and with Black male students: Four strategies can be effective in creating supportive learning environments. *Mathematics Teacher*, *109*(4), 284–289.

Kirshner, D. (2016). Configuring learning theory to support teaching. In L. English & D. Kirshner (Eds.), *Handbook of international research in mathematics education (3 ^{rd} Ed) *(pp. 98–149). New York: Taylor & Francis.

Lowery, K., Rodriquez, S., & Benfield, P. (2019). Making in the middle: Making as a performance task. *Science Scope*, *042*(07).

Ludwig, R., & Chimonidou, A. (2013). Hands-on-science: Hands-on, integrated natural sciences for pre-service elementary teachers*. *Paper presented at 2013 NARST Annual International Conference, Rio Grande, Puerto Rico.

Marder, M., Horn, C., Stephens, S., & Rhodes, A. (2022). Student learning and teacher retention for graduates of Texas Noyce programs. *Education Policy Analysis Archives*, *30*(147).

Marder, M., David, B., & Hamrock, C. (2020). Math and science outcomes for students of teachers from standard and alternative pathways in Texas. *Education Policy Analysis Archives, 28*(27).

Marder, M. (2019). Can we inspire every high-school student to take physics? Texas nearly did. *APS News*, 28(10).

Marder, M. (2018, March). *Rise and fall of Texas STEM education: College readiness and course-taking since House Bill 5 of 2013**.* [White paper].

Marder, M., Brown, C. R., & Plisch, M. (2017). Recruiting Teachers in High-Needs STEM Fields: A survey of current majors and recent STEM grads. *The Physics Teacher*, *55*(5), 318–318. doi: 10.1119/1.4981053

Marder, M., Patzek, T, & Tinker, S. (2016). Physics, fracking, fuel, and the future. In *Physics Today* *69*(7), 46–52.

Marder, M., & Walkington, C. (2014). Classroom observation and value-added models give complementary information about quality of mathematics teaching. In T. Kane, K. Kerr, & R. Pianta (Eds.), *Designing teacher evaluation systems: New guidance from the Measuring Effective Teaching project* (pp. 234–277). New York: John Wiley & Sons.

Marder, M. (2013). A problem with STEM. *CBE Life Sciences Education*, *12*(2), 148–150.

Marder, M. (2012). Failure of U.S. public secondary schools in mathematics. *AASA Journal of Scholarship and Practice, 9*(1), 8–24.

Marder, M. (2012). Measuring teacher quality with value-added modeling. *Kappa Delta Pi Record, 48*, 156–161.

Marder, M. (2011). *Research methods for science**. *Cambridge, England: Cambridge University Press.

Marder, M., & Bansal, D. (2009). *Flow and diffusion of high-stakes test scores*. Proceedings of the National Academies of Science, 106, 17267–17270.

Marshall, J., & Harron, J. R. (2018). Making learners: A framework for evaluating making in STEM education. *Interdisciplinary Journal of Problem-Based Learning, 12*(2). doi: 10.7771/1541-5015.1749

Marshall, J., & Young, E. S. (2006). Preservice teachers' theory development in physical and simulated environments. *Journal of Research in Science Teaching*,* 43*(9), 907–937.

Rodriguez, S., Allen, K., Harron, J., & Qadri, S. A. (2019). Making and the 5E learning cycle. *The Science Teacher*, *86*(5). doi: 10.2505/4/tst18_086_05_48

Rodriguez, S. R., Fletcher, S. S., & Harron, J. R. (2019). Introducing ‘making’ to elementary and secondary preservice science teachers across two university settings. *Innovations in Science Teacher Education, 4*(4).

Rodriguez, S., Morrison, A., & Benfield, P. (2019). Pulley islands: Third graders conquer a tinkering challenge. *Science and Children*, *56*(8).

Rodriguez, S., Harron, J., Benfield, P., & Reyes, M. (2018). Illuminating food webs: A maker jigsaw. *Science Scope, 42*(1), 54–64.

Rodriguez, S., Harron, J., Fletcher, S., & Spock, H. (2018). Elements of making: A framework to support making in the science classroom. *The Science Teacher, 85*(2), 24–30.

Siegel, L. M., Dickinson, G., Hooper, E. J., & Daniels, M. (2008). Teaching algebra and geometry concepts by modeling telescope optics. *Mathematics Teacher*, *101*(7), 490–497.

Smith, J., & Nadelson, L. (2016). Learning for you and learning for me: Mentoring as professional development for mentor teachers. *Mentoring & Tutoring: Partnership in Learning,* *24*(1), 59–72.

Sparks, D. (2018). The process of becoming: Identity development of African American female science and mathematics preservice teachers. *Journal of Science Teacher Education, 29*(3), 243–261.

Stroup, W., Hills, T., & Carmona, G. (2011). Computing the average square: An agent-based introduction to aspects of psychometric practice. *Technology, Knowledge and Learning*, *16*(3), 199–220.

Stroup, W. M. (2005). Learning the basics with calculus. *Journal of Computers in Mathematics and Science Teaching*, *24*(2), 179–196.

Stroup, W. M., Ares, N. M., & Hurford, A. C. (2005). A dialectic analysis of generativity: Issues of network-supported design in mathematics and science. *Mathematical Thinking and Learning*, *7*(3), 181–206.

Walkington, C., Sherman, M., & Petrosino, A. (2012). ‘Playing the game' of story problems: coordinating situation-based reasoning with algebraic representation. *The Journal of Mathematical Behavior*, 31, 174–195.