A new study by Student Achievement Partners identifies early interventions that can increase later Algebra success.
We’re excited to share the results of a groundbreaking new study by Student Achievement Partners!
The study, “Preparation for Success in Algebra: Exploring Math Education Relationships by Analyzing Large Data Sets (EMERALDS),” is one of the largest longitudinal assessments of what prepares students for success in Algebra.
The study was designed to explore how to better support students who are Black, Latino, English learner-designated, experiencing poverty, and/or female to achieve better results in Algebra. It involved a large-scale, four-year longitudinal assessment of data sets from more than a million students representing a broad range of racial/ethnic backgrounds, income levels, and geographic regions.
The main results of the study were based on detailed, longitudinal data from 100,000 Florida students using our MATHia software, which provided the fine-tuned information necessary to identify middle school topics that led to success in Algebra.
We are committed to advancing equity in education and helping all students learn, and we are thrilled to have the efficacy of MATHia validated in this research.
Carnegie Learning is helping students learn why, not just what. Born from more than 30 years of learning science research at Carnegie Mellon University, the company has become a recognized leader in the ed tech space, using artificial intelligence, formative assessment, and adaptive learning to deliver groundbreaking solutions to education’s toughest challenges. With the highest quality offerings for K-12 math, ELA, literacy, world languages, professional learning and more, Carnegie Learning is changing the way we think about education, fostering learning that lasts.
Explore more related to this authorWe're proud to have supported this work that helps us better understand how we can help all students succeed in Algebra and in life.
Dr. Steve Ritter, Co-Founder and Chief Scientist, Carnegie Learning
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