Ali Raza

Hi, I am a Research Scientist at the Institute of Cognitive Science, University of Colorado Boulder. I am a design researcher focused on designing equitable learning experiences for all students working at the intersection of Human-Computer Interaction, STEM Education, AI in Education, Learning Sciences, and Learning Analytics by focusing on developing learning technologies and environments for improvement with educators and students about equity of participation. Specifically, my research looks at how educational data can be used to support meaningful learning across formal and informal environments for critical conversations. I am passionate about designing technologies and educational resources by partnering with educators and students and studying their use for supporting meaningful and equitable learning experiences.

Currently, I am co-leading a funded project on embedding the use of research evidence in data-driven instructional routines within two local school districts. The work focuses on improving students' science experience within data-driven instruction cycles using learning analytics and evidence-based research strategies.

I completed my Ph.D. in Computer Science & Cognitive Science at the University of Colorado Boulder, working with Prof. Tamara Sumner and Prof. William Penuel. I co-design and co-developed a visual learning analytics tool, Science Student Electronic Exit Ticket (SEET) system, and studied its use in leading two improvement cycles in middle school science classrooms to support equity of participation. To support this improvement, my colleagues and I designed a professional learning series called Student Experience Improvement Cycle (SEIC) to elevate students' experience using evidence-based instructional strategies, gather evidence on their effectiveness, and support teachers' orchestration. Based on the SEIC intervention, we found significant improvement differentially in the experiences of students identifying as Black/African American, Native Hawaiian/Pacific Islanders, Asian and White.