From the abstract: "This study explored the validity of growth on two computer adaptive tests, Star Reading and Star Math, in explaining performance on an end-of-year achievement test for a sample of students in Grades 3 through 6. Results from quantile regression analyses indicate that growth on Star Reading explained a statistically significant amount of variance in performance on end-of-year tests after controlling for baseline performance in all grades. In Grades 3 through 5, the relationship between growth on Star Reading and the end-of-year test was stronger among students who scored higher on the end-of-year test. In math, Star Math explained a statistically significant amount of variance in end-of-year scores after statistically controlling for baseline performance in all grades. The strength of the relationship did not differ among students who scored lower or higher on the end-of-year test across grades.Citation: Van Norman, E. R., & Forcht, E. R. (2023). An evaluation of the validity of growth on two computer adaptive tests to predict performance on end-of-year achievement tests using quantile regression. Assessment for Effective Intervention, 48(2), 80-89.

From the abstract: "This study tries to evaluate the teaching and learning process of Teaching Functions and Relations of Sets using Nearpod. The purpose of the study is to determine whether Nearpod can be used as a Learning Platform in interactively teaching Functions and Relations between Sets in a Blended Learning Environment. This is a phenomenological study conducted in a Mathematics Class at Marikina Polytechnic College. A lesson was presented and delivered to MAT Mathematics Major students of the Marikina Polytechnic College who also assess and evaluate the lesson based on the implementation of Nearpod in teaching Functions and Relations between Sets. Reflections and Feedback given by teachers were analyzed through a thematic approach. Results show that Nearpod provides ease of access to using different devices and promotes teaching both in online and face-to-face settings. Ease of access of creating diagrams through the Draw-it feature makes teaching and learning g functions and the relation between sets interactively engaging. There are, however, the teacher must be mindful of the risk in teaching relations and function of sets. To avoid it, the teacher must specify the objective of teaching Functions and relations between sets and delivering the lesson efficiently to maximize the potential of the feature of Nearpod. Thus, it is suggested to implement Interactive Teaching using Nearpod not only in teaching Mathematics but in other subjects."Citation: Salansan, K. M., Palmeria, Z. T., Leop, S. M., Boyoro, R. M. E., Mamangon, D. M., Roxas, D. J. B. (2023). Nearpod for mathematics: Realizing functions and relations between sets. IOER International Multidisciplinary Research Journal, 5(1), 104-111.The study is available online: <https://doi.org/10.54476/ioer-imrj/551715>.

Frome the abstract: "Accelerated reader (AR) is a computerized reading program commonly used in schools. The program aims to enhance students' reading achievement and encourage students to read more through goal setting and frequent reading practice. A meta-analytic review of the AR was conducted to analyse its effectiveness as an evidence-based intervention for improving student reading achievement, attitude, and motivation. This study investigated potential moderating variables, including publication type, participant, and study characteristics that impact student reading outcomes. A total of 44 studies from peer-reviewed journal articles and dissertations met the inclusion criteria. Participants included 16,653 students enrolled in elementary, middle, and high school. Hedges' g effect sizes measures suggest pretest-posttest one-group AR studies have moderate effects (g = 0.541) while comparison group AR studies have marginal effects (g = 0.278). A meta-regression model of six potential categorical moderators of comparison group studies indicted no significant moderators. Implications and the need for further research regarding evidence-based and culturally appropriate reading interventions are discussed."PLEASE NOTE: The Summary of this peer-reviewed journal article: Tischner, C. M., Ebner, S. E., Aspiranti, K. B., Klingbeil, D. A., & Fedewa, A. L. (2023). Effectiveness of Accelerated Reader on children's reading outcomes: A meta-analytic review. Dyslexia, 29(1), 22-39. is available online: <https://docs.renaissance.com/R66509>.

This Accelerated Reader summary graphic explains the breadth of educational study designs, displays a barometer of study rigor/strength of findings, and pinpoints examples of research supporting Accelerated Reader for each study type. Unlike our competitors, research support for Accelerated Reader numbers more than 180 studies spanning all of these categories and covering a variety of school and students populations.The full report is available online: <https://docs.renaissance.com/R58266>.

FastBridge is committed to providing psychometrically sound assessments that provide valid and fair results for all students. This manual contains the psychometric evidence of the FastBridge Assessments. The full report is available online: <https://docs.renaissance.com/R64614>.

This manual describes the design and application of each FastBridge assessment. Each section begins with an overview of the test's purpose and use, followed by a detailed description of the content and design of the assessment and concludes with an overview of the administration and scoring procedures.The full report is available online: <https://docs.renaissance.com/R64613>.

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 results; "This quantitative research aimed to assess the effects of AR as a strategy to improve students' reading achievements and scaled scores. To this end, I collected perspectives from teachers who used AR for their Chinese primary tudents. In this section, I mainly present the results of the data analysis. First, I report the participants' and their students' demographic characteristics. Next, I present descriptive statistics and a Wilcoxon signed-rank test to analyse the effects of AR. Then, I illustrate the results of the correlation and multiple linear regression analyses used to test the relationships between the AR mechanisms and reading achievements. Lastly, the main points demonstrated in the chapter are summarised."Citation: Tan, H., Yang, F., Zhang, H., & Chakraborty, D. (2022, October). Inveestigate the effectiveness of the Accelerated Reader program on primary school students' reading outcomes in China. Monash University.(Email research@renaissance.com to request a copy of this study from the Renaissance Research Department.)

From the Executive Summary: "The goal of this study was to assess the validity of IXL’s Real-Time Diagnostic in both math and reading using a new criterion measure: the Star Assessment. We analyzed math and reading data from students in grades 1-8 attending public schools in a large, suburban Oklahoma district. Within each subject, we found: • Strong, positive correlations between IXL’s Diagnostic and Star performance (all rs = .70) • A high degree of overlap in student proficiency classifications by the IXL Diagnostic and Star Assessment"Citation: Schonberg, C. (2022). Assessing the predictive validity of the IXL Real-Time Diagnostic using Star Assessments as criterion. IXL Learning.The full technical report is available online: <https://ca.ixl.com/materials/us/research/Assessing_the_Predictive_Validity_of_the_IXL_Real-Time_Diagnostic_(Star).pdf>

Multi-tiered System of Supports (MTSS) is an approach to monitoring students’ achievement and allocating resources based on identified need. This approach, sometimes called Response to Intervention (RTI), is based broadly on a public health model of monitoring, identification, and intervention, and is increasingly common in educational settings. In MTSS, educators help students meet academic goals through a tiered system of support. This guide illustrates the steps of an MTSS process, highlighting key principles for a successful implementation. The full report is available online: <https://docs.renaissance.com/R43363>.

Research has documented that mastering phonics skills is critical for learning to read, and this is a critical element of The Science of Reading. Closely monitoring how students’ phonics skills are developing gives teachers critical insight to guide phonics instruction. Star Phonics assists with reading instruction by quickly and efficiently screening 12 of the most critical phonics categories while additionally providing diagnostics on 102 specific phonics skills. The assessment is designed for all students in grades 1-6 and older students who continue to struggle in reading.The full technical report is available online: <https://renaissance.widen.net/s/fmwtvgbnhh/594253-the-science-behind-star-phonics>.

From the abstract; "Star Math (SM) is a popular computer adaptive test (CAT) schools use to screen students for academic risk. Despite its popularity, few independent investigations of its diagnostic accuracy have been conducted. We evaluated the diagnostic accuracy of SM based upon vendor provided cut-scores (25th and 40th percentiles nationally) in predicting proficiency on an end of year state test in a sample of highly achieving grade three (n = 210), four (n = 217), and five (n = 242) students. Specificity exceeded sensitivity across all grades and cut-scores. Acceptable levels of sensitivity and specificity were achieved in grade three and four but not grade five when using the 40th percentile."Citation: Turner, M. I., Van Norman, E. R., & Hojnoski, R. L. (2022). An independent evaluation of the diagnostic accuracy of a computer adaptive test to predict proficiency on an end of year high-stakes assessment. Journal of Psychoeducational Assessment, 40(7), 911-916.

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>.