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1. ”What you bring matters”: A Comparative Case Study of Middle School Engineering Teachers’ Pedagogical Content Knowledge (Fundamental)

   Gale, J; Baptiste_Porter, D; Alemdar, M; Newton, S; Rehmat, A; Choi, J (June 2025, American Society for Engineering Education)

   Pre-college engineering teachers bring unique backgrounds to their teaching practice. Many engineering teachers follow a non-traditional route to teaching engineering, often coming to engineering from teaching other subjects or from careers in other fields. Among the many variations influencing engineering teaching practices is pedagogical content knowledge (PCK), defined as the “the knowledge of, reasoning behind, and enactment of the teaching of particular topics in a particular way with particular students for particular reasons for enhanced student outcomes [1]”. This multiple case study explores the PCK of five middle school engineering teachers implementing the same middle school engineering curriculum, STEM-ID. The 18- week STEM-ID curriculum engages students in contextualized challenges that incorporate foundational mathematics and science practices and advanced manufacturing tools such as computer aided design (CAD) and 3D printing, while introducing engineering concepts like pneumatics, aeronautics, and robotics. Drawing on observation and interview data collected over the course of two semester-long implementations of STEM-ID, the study addresses the research question: What variations in PCK are evident among engineering teachers with different professional backgrounds and levels of experience? Five teachers were purposively selected from a larger group of teachers implementing the curriculum because they represent a range of professional backgrounds: one veteran engineering teacher, one former Math teacher, one former Science teacher, one former English/Language Arts teacher, and one novice teacher with a background in the software industry. The study utilizes the Refined Consensus Model of PCK to investigate connections between teacher backgrounds, personal PCK (pPCK), the personalized professional knowledge held by teachers, and enacted PCK (ePCK), the knowledge teachers draw on to engage in pedagogical reasoning while planning, teaching, and reflecting on their practice. Observation, interview, and survey data were triangulated to develop narrative case summaries describing each teacher’s PCK, which were then subjected to cross-case analysis to identify patterns and themes across teachers. Findings describe how teachers’ backgrounds translated into diverse forms of pPCK that informed the pedagogical moves and decisions teachers made as they implemented the curriculum (ePCK). Regardless of the previous subject taught (math, science, or ELA), teachers routinely drew upon their pPCK in other subjects as they facilitated the engineering design process. Teachers with previous experience teaching math or science tended to be more likely than others to foreground the integration of math or science within the curriculum. Comparison of ePCK observed as teachers implemented the curriculum revealed that, in spite of having a more fully developed pPCK in teaching engineering, the veteran engineering teacher did not exhibit more sophisticated ePCK than novice engineering teachers. In addition to contributing to the field’s understanding of engineering teachers’ PCK, these findings hold implications for the recruitment, retention, and professional development of engineering teachers. 

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2. Just keep swimming: Increasing resilience of STEM preservice teachers during COVID-19

   Catherine M. Lussier University of California, Riverside Ashley M. (March 2023, Journal of instructional pedagogies)

   A substantial achievement gap between K-12 English learners (ELs) and non-ELs in science, technology, engineering, and mathematics (STEM) content areas exists, as indicated by national assessments of student outcomes. Considering the expected steady increase in students who are ELs in the U.S., determining methods for addressing this achievement gap is of immediate concern. Research has indicated this gap may be exacerbated by lack of adequate teacher preparation, specifically in STEM fields, to effectively teach students who are culturally and linguistically diverse (CLD). Founded in previous research about effective teacher preparation, the current case study pilots and reports on a model of early STEM preservice teacher training that integrates: knowledge of language development for ELs, early experiences with CLD learners, and professional development activities that guide the implementation of STEM pedagogical methods. Five STEM preservice teachers participated in a year-long supplemental training program focused on adapting STEM instruction for ELs. Components of the supplemental program included: (a) coursework extending teacher knowledge of EL language development, (b) fieldwork providing early exposure to research-based teaching experiences with EL students, and (c) professional development guiding the creation of hands-on STEM curriculum for diverse learners. Five secondary preservice teachers experienced increases in self-efficacy, growth in STEM instructional practices, and greater motivation for teaching in high-need schools. Results will inform educational models for improving STEM-EL teaching, thereby addressing a crucial need to serve the growing national population of underserved students. 

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3. Quest: Human Learning - A Framework for Incorporating Generative-AI Teaching and Learning Instruction in STEM Pre-Service Teacher Preparation (Evaluation). Paper presented at the 2025 ASEE Annual Conference & Exposition, Montreal, Canada.

   Liebe, C; Schill, S; Smith, CE; Ahammed_Ovi, J (June 2025, 2025 ASEE Annual Conference & Exposition)

   Generative Artificial Intelligence (GenAI) offers tools to transform K-12 science, engineering, technology, and mathematics (STEM) education. Pre-service teachers are in a prime position to learn about and engage with this emergent technology, preparing them to enter the workforce with knowledge on how GenAI can impact K-12 learning outcomes. This paper examines the implementation of GenAI teaching and learning instruction in a computer science (CS) pre-service teacher preparation program at a small engineering-focused R1 institution. Applying the Quest: Human Learning model, pre-service teachers learned prompt engineering and tested AI output. Quest stands for Question + Test. The pre-service teachers attended the Teach@Mines K-12 STEM teacher preparation program. Teach@Mines offers pathways for undergraduate and graduate students to obtain licensure for teaching in K-12 science, math, and computer science. Students were both undergraduates and graduates. Given the recent advances in GenAI, Teach@Mines asks STEM students to develop innovative future-oriented STEM curriculum and instruction focused on the future of human and GenAI interaction. This paper uses a case study approach to review curriculum and student artifacts related to GenAI teaching and learning instruction using new AI Education model, Quest: Human Learning (QHL). Case study examples of CS pre-service teachers evaluating, developing, creating, and testing AI-assisted curriculum provide insight into how the QHL model can be applied for other STEM disciplines and pre-service teacher education broadly. Teach@Mines’ implementation of GenAI teaching and learning education in pre-service teacher education empowers future K-12 educators to thoughtfully and responsibly integrate GenAI into the classroom to enhance learning experiences, support diverse learners, and prepare K-12 students for the future. As GenAI advances, applying the QHL model with a focus on teaching future generations provides a novel pathway for STEM majors to reconnect with and define human talents and abilities to solve human problems and develop technological solutions. 

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4. Enhancing Engineering Education for Students with Intellectual Disabilities

   Jimenez, B; Courtade, G (March 2026, Journal of science education for students with disabilities)

   This study explores the impact of a Universal Design for Learning (UDL) aligned engineering curriculum on teacher confidence and student engagement in special education classrooms, specifically focusing on students with intellectual and developmental disabilities (IDD). The research investigates how the curriculum, combined with targeted professional development (PD), supports teachers in delivering high-quality engineering instruction. Through a mixed methods approach, including surveys, interviews, focus groups, and classroom observations, data revealed significant improvements in teacher self-efficacy, particularly in areas such as scaffolding, differentiation, and accessibility. Teachers reported increased confidence in their ability to implement engineering lessons and foster engineering mindsets in their students. Furthermore, the study highlights the effectiveness of UDL principles in promoting student collaboration, problem-solving, and independent learning. The findings emphasize the importance of data-informed PD, which not only enhances teacher practice but also provides equitable STEM learning opportunities for students with ID, contributing to the broader goal of advancing inclusive STEM education. 

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5. Long-Term Outcomes of RET Programs on Female and Minority Student High School Graduation Rates and Undergraduate STEM Major Rates (Fundamental)

   Nichol, C.; Crawford, C. A.; Barr, C.; Cerda, I. (January 2021, ASEE Annual Conference proceedings)

   null (Ed.)

   Research Experience for Teachers (RET) programs are National Science Foundation (NSF) funded programs designed to provide K- 12 Science, Technology, Engineering, and Mathematics (STEM) teachers with immersive, hands-on research experiences at Universities around the country. The NSF RET in nanotechnology encourages teachers to translate cutting-edge research into culturally relevant Project-Based Learning (PjBL) and engineering curriculum. Traditionally, the evaluation of RET programs focuses on the growth and development of teacher self-efficacy, engineering content knowledge gains, or classroom implementation of developed curriculum materials. However, reported methods for evaluating the impact of RETs on their female, minority student populations' high school graduation and undergraduate STEM major rates are limited. This study's objective was to compare RET high school student graduation rates and undergraduate STEM major rates across gender, race, and ethnicity to a comparison sample to determine the RET program's long-term impact on students' likelihood of pursuing STEM careers. The approach of collecting and analyzing the Texas Education Research Center Database (EdRC) data is a novel methodology for assessing RET programs' effectiveness on students. The EdRC is a repository of K-12 student data from the Texas Education Agency (TEA) and Higher Education data from the Texas Higher Education Coordinating Board (THECB). This joint database contains demographic, course registration, graduation, standardized testing, and college major, among others, for all students that attended a K-12 public school in Texas and any college in Texas, public or private. The RET program participants at Rice University (2010 – 2018) taught numerous students, a sample size of 11,240 students. A propensity score matching generated the student comparison group within the database. Students' school campus, gender, race/ethnic status, and English proficiency status were applied to produce a graduation comparison sample size of 11,240 students of Non-RET participants. Linking the TEA database to the THECB database resulted in college STEM participants and comparison sample sizes of 4,029 students. The project team conducted a logistic regression using RET status to predict high school graduation rates as a whole and by individual variables: gender, Asian American, Black, Caucasian, and Latinx students. All models were significant at p less than 0.05, with models in favor of students RET teachers. The project team conducted a logistic regression using RET status to predict student STEM undergraduate major rates as a whole and by individual variables: Gender, Asian American, Black, Caucasian, and Latinx students. African American and Caucasian models were significant at p less than 0.05; Gender, Asian American, and Latinx models were marginally significant (0.05 less than p greater than 0.1), where RET students had higher STEM major rates than matched controls. The findings demonstrate that RET programs have a long-term positive impact on the students' high school graduation rates and undergraduate STEM major rates. As teachers who participate in the RET programs are more likely to conduct courses using PjBL strategies and incorporate real-world engineering practices, female and minority students are more likely to benefit from these practices and seek careers utilizing these skills. 

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