Technology

There are few guidelines related to how to implement integrated STEM education in the K-12 science classroom. It is important that teachers have opportunities to reflect on integrated STEM instruction when implemented so that they may further develop their practice. This research aimed to understand how the STEM Observation Protocol (STEM-OP) may be used as a way for teachers to reflect on their integrated STEM practice. This exploratory case study was designed to better understand secondary science teachers’ reflections on the STEM-OP by addressing the following research questions: 1) What are secondary science teachers’ reflections on integrated STEM practices as measured by the STEM-OP? and 2) In what ways do secondary science teachers envision using the STEM-OP as a tool in their practice?

This study examines the demographics, qualifications, and turnover of STEM teachers in Kansas and Missouri—two contiguous, predominantly rural states in the Midwestern region of the United States. The existing literature lacks detailed insights regarding U.S. STEM teachers, especially with recent economic and social changes over the COVID-19 pandemic, and there is particularly limited evidence regarding STEM teachers in the U.S. Midwest. Utilizing large-scale administrative longitudinal data, we filled part of this gap by documenting the characteristics and turnover patterns of STEM teachers in Kansas and Missouri over a 13-year period, from 2010 through 2023.

Socioscientific issues (SSI) are problems involving the deliberate use of scientific topics that require students to engage in dialogue, discussion, and debate. The purpose of this project is to utilize issues that are personally meaningful and engaging to students, require the use of evidence-based reasoning, and provide a context for scientific information. This study highlights the value of integrating SSI in science education to engage students with social justice.

This paper explores the transformative impact of generative artificial intelligence (GenAI) on teachers’ roles and agencies in education, presenting a comprehensive framework that addresses teachers’ perceptions, knowledge, acceptance, and practices of GenAI.

We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently.