Star Reading and Star Math have been rated highly by the National Center on Intensive Intervention (NCII) as academic screening assessments. The Full Review is available online: <https://charts.intensiveintervention.org/ascreening>.

From the overview: "This whitepaper presents a comprehensive, developmentally informed framework for dyslexia screening, grounded in current research and practical application. State mandates for dyslexia screening could lead to unintended outcomes of fragmented and compliance-driven approaches, when schools need a coherent system that reflects the multifactorial nature of dyslexia and its developmental trajectory."The full report is available online: <https://renaissance.widen.net/s/xlnjtrnqcj/11448889249-bringing-dyslexia-screening-into-the-future-whitepaper>.

About What Kids are Reading 2026 This year’s report is based on reading data for the 2024–2025 school year for 4.7 million kids who read 161 million books at 19,962 schools and the 1.5 million kids who spent 545,888,348 minutes reading digitally. What Kids Are Reading displays popular books by grade range, by grade level, and—new this year—for Growing Readers (kids who started the year with low reading achievement but finished with exceptionally high growth). Two research spotlights dig further into the data to share what we know about How Kids Are Reading and our Insights on Growing Readers. Dr. Tiffany P. Hogan, a Professor at MGH Institute of Health Professions and Research Associate at Harvard Medical School, wrote the introduction to the report. The Full Report and additional information is available online: <https://www.renaissance.com/resources/what-kids-are-reading/>.

From the abstract: "Skill proficiency prediction is an important problem in the personalized learning space. We describe a machine learning approach to skill proficiency prediction that is highly interpretable, efficient, and can be used to incorporate data from assessment, practice and potentially, other sources. We formulated the approach as a theory driven supervised learning problem - specifically, we used concepts from Item Response Theory to construct features that provide model interpretability. Results indicate that the most predictive features are those that are estimates of student ability, skill difficulty and ability-difficulty difference, thus aligning well with Item Response Theory. Model diagnostics and error analysis provided several insights into the model that align with learning theories. We incorporated data from both assessment and practice products in the prediction model. Results indicate that the model performs better when there is both prior assessment and practice student data as opposed to when there is just assessment or practice data. Finally, we proposed an approach that recommends the instructional category that best represents each student’s current level of skill development as determined by the skill proficiency prediction value. The proposed approach provides confidence metrics for the recommendations and optimizes for percentage of students placed in the categories." Citation: Borbora, Z. H., Bielinski, J., & Bray, B. (2026, April). A theory driven supervised learning approach for math skill proficiency prediction using assessment and practice data (pp. 239–249). In Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference. Association for Computing Machinery. The full report is available online: <https://dl.acm.org/doi/pdf/10.1145/3785022.3785056>.

Anderson School District One in Williamston, South Carolina, has many high-performing students but is a low-funded district, meaning leaders must be selective about what they invest in and must thoroughly vet all tools to ensure they will be impactful. At the beginning of the 2022–2023 school year, Anderson One switched assessment programs to begin using Renaissance Star Assessments in order to access detailed information about students’ knowledge and mastery of standards-based skills. Shortly thereafter, the district added Freckle for Math and ELA, as well as Lalilo. K–5 students in the district have seen their growth scores soar using these Renaissance tools. The full study is available online: <https://renaissance.widen.net/s/wjbswknhc2/r69052>.

From the abstract: "Academic screener tools are commonly used within the context of multitiered systems of support, and it is expected that they have a high standard of technical adequacy, including reliability and validity evidence. In United States school systems, there are large numbers of students who speak languages other than English with varying levels of proficiency, and it is imperative that well-developed academic screening tools be accessible to active bilingual users/users of English (ABLE) in languages beyond English. This review evaluated the translation process, norming data, and National Center on Intensive Interventions (NCII) standards of academic screening tools that were available in the fall 2023 in early literacy, reading, and math in languages other than English. Information was coded using a rigorous, double-coding process. Findings revealed that academic screening tools for ABLE students are available only in English and Spanish. Very few academic screeners met development criteria for translated measures or met convincing evidence standards established by the NCII. Academic screening conducted in languages other than English is severely constrained by limited availability of high-quality measures. Additional effort should be devoted to developing new measures and appropriately translating and trans-adapting existing measures for ABLE students." Includes Star Alfabetización temprana, Star CBM Lectura, Star Lectura, and Star Matemáticas Citation: Huenergarde, M. C., Floyd, R. G., Harris, B., & Moreno, J. (2026). A systematic review of academic screening tools for active bilingual learners/users of English. Psychology in the Schools, 63(2), 314–331.

"Trends in student outcome measures: The role of individualized Lalilo practice" was independently reviewed and certified by Instructure to meet ESSA Tier 3 standards. The review is available online: <https://certifications.eduevidence.org/products-details?recordId=rec4sck12vicchZpw>.

Executive Summary: "Early literacy screening plays a critical role in identifying students who may be at risk for reading difficulties. However, most widely used screening tools were designed and normed within monolingual English-speaking populations, limiting their utility when applied to multilingual learners. This white paper presents findings from a study examining the use of Rapid Automatic Naming (RAN) within dual language contexts, with a specific focus on Spanish/English multilingual students. While Star CBM Lectura RAN benchmarks were developed using a large, multi-state norming sample, the findings presented in this paper draw exclusively from a focused study conducted in two dual language school districts in Oregon. Although both districts offer PK–12 dual language programs, the study sample consisted specifically of students in Kindergarten through Grade 3. Using Star CBM Lectura RAN measures, the study examined student performance across four RAN task types: colors, pictures, letters, and numbers, and analyzed language response patterns alongside teacher feedback collected during administration. Results demonstrate that RAN performance varies systematically by language of response. Students responding only in English showed faster naming speeds, followed by Spanish-only responses, while responses using both languages yielded the lowest scores. These differences reflect linguistic structure, and processing demands rather than reading ability or language proficiency, a finding consistent with cross-linguistic research on RAN and reading development (Georgiou et al., 2008; Landerl et al., 2013). Teacher feedback further highlighted the importance of interpreting RAN data through a bilingual lens, noting patterns of cross-language substitution, task-specific difficulty, and instructional relevance. The findings suggest that RAN can be a valuable early indicator of reading risk for multilingual learners when interpreted appropriately. However, equitable use requires responsive-to-language benchmarks, careful attention to task demands, and professional learning that supports educators in distinguishing bilingual processing patterns from indicators of reading difficulty." The study is available online: <https://docs.renaissance.com/R69049>.

Early literacy practice with Lalilo was associated with higher levels of growth in general literacy achievement. Students who used Lalilo more intensively and for a longer period experienced the most growth in their literacy skills. Less intense and less frequent use of Lalilo was associated with relatively less achievement growth, but even lighter use was associated with more growth relative to students who did not use the program at all. These results remained consistent across struggling readers, English language Learners, and students from Title I schools.The full report is available online: <https://docs.renaissance.com/R63694>.

From the Introduction: "To provide optimal power and usefulness within our Star Assessments and many of our practice products, Renaissance takes a unique approach to content creation through the development of custom learning progressions. In general, learning progressions are descriptions of successively more sophisticated ways of thinking about key disciplinary concepts and practices across multiple grades, which outline intermediate steps toward expertise (Pellegrino, 2011). Learning progressions lay out in words and examples what it means to move toward more expert understanding (Wilson & Bertenthal 2005) in a subject area. While not unlike a traditional scope and sequence, learning progressions are far more thoughtfully developed and emphasize a holistic, cross-grades focus."The Full Report is available online: <https://renaissance.widen.net/s/jdm9kgx5cw/780549-lpfocusskills-whitepaper>.

From the introduction: "In the spring of 2024, Renaissance undertook a special study to gather further reliability and validity evidence for its four Star CBM rapid automatic naming (RAN) measures. These four assessments measure a student’s ability to rapid name colors (Rapid Color Naming (RCN), pictures (Rapid Picture Naming (RPN)), letters (Rapid Letter Naming (RLN)), and numbers (Rapid Number Naming (RNN)). The four RAN measures each have two test forms and are offered in grades K through 3."The study is available online: <https://docs.renaissance.com/R67673>.

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

From the study: "The purpose of this report was to evaluate the Lalilo software program. Three research questions guided the current study.1. Do students in classrooms using Lalilo experience more growth in literacy skills relative to students in classrooms that do not use the program? Overall, students in schools that used Lalilo grew at a greater rate than students in schools that did not use Lalilo.2. Do classrooms that follow best practice guidelines for the program confer greater student growth relative to classrooms that do not? More specific measures of fidelity or usage benchmarks may shed light on the relationship between adherence to best practice guidelines and student growth in early literacy skills.3. To what degree is the intensity in which Lalilo is used (e.g., number of lessons completed / number of minutes program was used) related to the magnitude of growth in literacy skills amongst students in classrooms that use the program?Increasing the number of minutes or days spent using Lalilo was not critical to the improvement in weekly growth of early literacy skills. Instead increasing the number of learning objectives validated resulted in the largest improvements".Citation: Van Norman, E. R., & Forcht, E. R. (2024). An evaluation of the impact of the Lalilo software program: Research report prepared for Renaissance Learning. Center for Promoting Research to Practice, Lehigh University.The study is available online: <https://docs.renaissance.com/R67557>.