The current study expands on previous research on gender differences and similarities in science test scores. Using three different approaches – differential item functioning, differential distractor functioning, and decision tree analysis – we examine a high school science assessment administered to 3,849 10th-12th graders, of whom 2,021 are girls. Our findings confirm no significant gender differences in overall science performance. However, we identify differences in the incorrect distractor responses between females and males, especially on items that require spatial thinking.
Research

CREATE for STEM Institute teams conduct research that focuses on impactful projects in undergraduate education through Discipline-Based Educational Research (DBER). We design innovative K-16 science curricula and investigate the effects of new teaching methods on student learning, engagement, and community impact, with our work increasingly incorporating artificial intelligence to enhance support for teachers and learners. We are leaders in STEM assessment design and professional development for educators, collaborating with international partners in over a dozen countries. CREATE fosters new talent, provides seed money for initial work and supports the grant writing process. Our goal is for CREATE to be a hub for the exchange of information and ideas!
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Culturally Responsive Project-Based Learning Intervention in Secondary Science
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Interactions - Physical Science Curriculum
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Supporting High School Students in Constructing Quantitative Knowledge-in-Use of Energy
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Developing a Teacher Micro-credential for Integrating Computational Thinking Across Disciplines
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Collaborative Research: Modeling inclusive computational thinking instruction
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3DLP (Learning Progression)
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Multiple Literacies in Project-Based Learning
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Auto Feedback 3DLP
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Circadian regulation in potatoes
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Collaborative Research: Scaffolding Computational Thinking Through Multilevel System Modeling
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Assessing Mathematical Sensemaking in Science (AMASS)
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Health in Our Hands: A New Genomic Framework for Schools and Communities
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Supporting Instructional Decision Making (PASTA)
Publications
Gender Differences and Similarities in High School Science Performance— What Do Item Response Patterns Tell Us?
Employing automatic analysis tools aligned to learning progressions to assess knowledge application and support learning in STEM
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.
Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness towards Power and Ethics of AI in K–12 STEM Classrooms
Artificial intelligence (AI) incorporates the applications of machine-learning systems dominantly within the automated assessment and intelligent tutoring systems. These AI applications have promising potential to increase capacity within science, technology, engineering, and mathematics (STEM) education by supporting the social and cognitive development and learning experiences of students.
IF science AND making AND computing: Insights for project-based learning and primary science curriculum design
Achieving the ambition of global science education reforms remains an ongoing challenge. Ideas from other STEM domains, however, could spur needed innovation in science education. The maker movement – or engaging in making – and computer science education – or learning computing – have proven rich contexts for STEM learning. This review analyses making and computing education research with primary-aged learners for insights on designing more meaningful science learning, an underlying goal of reforms.
FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts
There is growing support and interest in postsecondary interdisciplinary environmental education, which integrates concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts.