Principal Education Researcher, SRI Education
Nonye M. Alozie, PhD, specializes in science education, science standards-based assessment design using evidence centered design, classroom research, machine learning to understand human-human interactions in learning settings, and scientific discourse. Her work also aims to provide quality science education through equitable and inclusive learning opportunities and experiences.
Alozie’s background is in biology and science education. Before joining SRI, she was an assistant professor at Albion College, a small liberal arts college in Michigan. She taught undergraduate courses in elementary and secondary math and science pedagogy and elementary and secondary reading in the content area. She also developed and implemented a co-ed molecular biology and biotechnology outreach program for under-resourced high school students and a museum studies program for girls.
Alozie was awarded her doctorate in science education and MS in ecology and evolutionary biology from the University of Michigan. She has a BS in organismic biology, ecology, and evolution from the University of California, Los Angeles.
- Automated Collaboration Skills Assessment
- National Comprehension Center Service Center: Designing for Diversity: A Systematic Curriculum Design Approach for Incorporating Equity and Inclusion Design Principles in STEM
- Science Projects Integrating Computing and Engineering
- Speech Based Learning Analytics for Collaboration
- Next Generation Science Assessments in Life Science
Recent publicationsmore +
This paper addresses an important consideration for promoting equitable engineering instruction: understanding how teachers contextualize curricular materials to draw upon student and community resources.
Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cue
We propose simple CNN deep-learning models that take in spatio-temporal representations of individual student roles and behavior annotations as input for group collaboration assessment.
Towards Explainable Student Group Collaboration Assessment Models Using Temporal Representations of Individual Student Role and Behavioral Cues
In this paper we propose using simple temporal-CNN deep-learning models to assess student group collaboration that take in temporal representations of individual student roles as input.
In this paper, we provide suggestions for how state and local leaders can move towards transformation and change in curriculum use in schools and communities that serve students with diverse needs, strengths, and contributions to society.
In this paper, we introduce The Equity and Inclusion Framework for Curriculum Design (EI-CD) approach and Equity and Inclusion Design Principles (EI Design Principles).
This paper, the first in a series of three, describes why current approaches to designing STEM+CS curricula are inadequate; defines diversity, equity, and inclusion in the context of curriculum design; and introduces The Equity and Inclusion Framework for Curriculum Design approach for designing and adapting STEM+CS curriculum materials to meet the needs of diverse students.