Renaissance tools, including Accelerated Reader, eduCLIMBER, myIGDIs, myON, Nearpod, and Star Assessments, are supported by peer-reviewed research. This document provides a list of the peer-reviewed research available for reading and math practice products as well as assessment products. This document is only available online: <https://docs.renaissance.com/R35595>.

This whitepaper introduces the Renaissance Star assessment Biliteracy Trajectories Project, The full whitepaper is aavailable online: <https://renaissance.widen.net/s/vtlslkjdns/809564-biliteracytrajectories-whitepaper>.

This document contains updated classifaction accuracy for Star Early Literacy.The study is available online: <https://docs.renaissance.com/R67671>.

These Freckle for Math and Freckle for ELA studies involved more than 420,000 students from Grades K-12 (math) and Grades K-12 (ELA). The studies examined Freckle usage, normative achievement on Star Assessments, and growth in general math or reading ability (Star Student Growth Percentile) over the fall-to-spring period in the 2022-2023 school year. Practice with Freckle was associated with a greater percentage of students meeting Star proficiency benchmarks at the end of the year compared to the beginning of the year, and higher levels of growth in general math or reading ability. These positive outcomes increased as students engaged in practice that met or exceeded Freckle usage guidelines and as students used the program for a larger portion of the school year.The full study is available online: <https://docs.renaissance.com/R67477>.

The purpose of this study was the examine the patterns of reading achievement growth for students using myON. Results showed that students who spent more time on myON (per session and number of sessions) experienced more growth in Star Reading. And how students use myON affects student growth. Gains were shown in student growth percentiles (SGP), and analyses are presented for the general population, struggling readers, and English Language Learners.The research study is available online: <https://docs.renaissance.com/R62687>.(Email research@renaissance.com to request a copy of the 2019 report Special report: Trends in student outcome measures: myON and student achievement from the Renaissance Research Department.)

This technical manual is a compendium of all relevant information about content and item development, item and scale calibration, reliability and measure precision, validity, norming, classification accuracy, and score definitions for Star Early Literacy.Email research@renaissance.com to request a copy of the technical manual from the Renaissance Research Department.

These Freckle for math and Freckle for ELA studies involved more than 450,000 students from Grades 1-12 (math) and Grades K-12 (ELA). The studies examined Freckle usage, normative achievement on Star Assessments, and growth in general math or reading ability (Star Student Growth Percentile) over the fall-to-spring period in the 2021-2022 school year. Practice with Freckle was associated with a greater percentage of students meeting Star proficiency benchmarks at the end of the year compared to the beginning of the year, and higher levels of growth in general math or reading ability. These positive outcomes increased as students engaged in practice that met or exceeded Freckle usage guidelines and as students used the program for a larger portion of the school year.The Full Report is available online: <https://docs.renaissance.com/R64618>.

Throughout 2021, Renaissance released a series of reports titled How Kids Are Performing, which estimated the impact of the COVID-19 pandemic on US student achievement in reading and mathematics during the 2020-2021 school year. Although the incidence of school shutdowns and remote learning is now much lower during the 2021-2022 school year than the prior year, the COVID-19 pandemic continues to affect K-12 education in myriad ways. This continuation of the How Kids Are Performing report series will serve as a status check, summarizing US K-12 student performance and growth as of the end of the 2021-2022 school year and contrasting those results to the same period in 2020-2021.The full report is available online: <https://www.renaissance.com/how-kids-are-performing/>.

From the abstract; "Traditional screening approaches identify students who might be at risk for academic problems based on how they perform on a single screening measure. However, using multiple screening measures may improve accuracy when identifying at-risk students. The advent of machine learning algorithms has allowed researchers to consider using advanced predictive models to identify at-risk students. The purpose of this study is to investigate if machine learning algorithms can strengthen the accuracy of predictions made from progress monitoring data to classify students as at risk for low mathematics performance. This study used a sample of first-grade students who completed a series of computerized formative assessments (Star Math, Star Reading, and Star Early Literacy) during the 2016-2017 (n = 45,478) and 2017-2018 (n = 45,501) school years. Predictive models using two machine learning algorithms (i.e., Random Forest and LogitBoost) were constructed to identify students at risk for low mathematics performance. The classification results were evaluated using evaluation metrics of accuracy, sensitivity, specificity, F1, and Matthews correlation coefficient." Across the five metrics, a multi-measure screening procedure involving mathematics, reading, and early literacy scores generally outperformed single-measure approaches relying solely on mathematics scores. These findings suggest that educators may be able to use a cluster of measures administered once at the beginning of the school year to screen their first grade for at-risk math performance."Citation: Bulut, O., Cormier, D. C., & Yildirim-Erbasli, S. N. (2022). Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach. Information, 13(8), 400.The full article is available online: <https://doi.org/10.3390/info13080400>.

This paper describes student growth percentiles (SGP), an increasingly popular method of characterizing student growth that is used in Renaissance's Star Reading, Star Math, Star Early Literacy, Star Reading Spanish, Star Math Spanish, and Star Early Literacy Spanish assessments. Topics covered include: explanation of performance versus growth measures, information on the evolution of the SGP score (purpose of the prior score and time adjustment), and a list of frequently asked questions (FAQs) with answers. The full report is available online: <https://docs.renaissance.com/R57137>.

Throughout 2021, Renaissance released a series of reports titled How Kids Are Performing, which estimated the impact of the COVID-19 pandemic on US student achievement in reading and mathematics during the 2020-2021 school year. Although the incidence of school shutdowns and remote learning is now much lower during the 2021-2022 school year than last year, the COVID-19 pandemic continues to affect K-12 education in myriad ways. This continuation of the How Kids Are Performing report series will serve as a status check, summarizing US K-12 student performance and growth as of the middle of the 2021-2022 school year and contrasting those results to the same period in 2020-2021. The report is available online: <https://www.renaissance.com/how-kids-are-performing/>.

Renaissance Star Early Literacy and Renaissance Star Reading are approved as Diagnostic Assessments, also known as screeners, by the Ohio Department of Education (ODE). Ohio educators can use these assessments to determine if K-3 students are on track or not on track for reading at grade level.The Star Reading Third Grade Proficiency Assessment is also approved by the ODE. This test is an alternative reading assessment that enables students to "demonstrate an acceptable level of reading performance for promotion to fourth grade." The full report is available online: <https://docs.renaissance.com/R63538>.

About the review: "The Colorado Department of Education (CDE) has contracted with WestEd (www.WestEd.org), a national, nonprofit, nonpartisan research and development organization, to conduct the legislatively mandated READ ACT Evaluation. CDE selected the WestEd-led partnership, including Augenblick, Palaich and Associates (www.apa-consulting.net) and RTI International (www.rti.org), through a competitive bidding process conducted between October and December 2019. The purpose of this component of the evaluation is to assess whether CDE-approved assessments meet the requirements of SB 19-199 and widely accepted professional technical and quality criteria. This report begins with ratings regarding the extent to which the vendor-provided assessment evidence addresses the SB 19-199 requirements and the additional technical and quality criteria. It then details how we made our summary ratings, first across the legislative requirements and then across the additional criteria, including a selection of descriptive notes on the findings. The final page includes a summary rating for both the SB 19-199 requirements and the additional technical and quality criteria and, if relevant, a rationale for removal from the advisory list."The review is available online: <https://drive.google.com/file/d/1GKFtMHAqHoGZVsMU0PX0rbzJ10BRPl0x/view>.