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>.

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>.

To develop Pathway to Proficiency reports for Delaware Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the Smarter Balanced achievement test. This technical report details the statistical method behind the process of linking Smarter Balanced Asessments and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R53517>.

To develop Pathway to Proficiency reports for Virginia Star Reading and Star Math, we linked our scaled scores with the scaled scores from Virginia's achievement test. This technical report details the statistical method behind the process of linking Virginia's state tests (SOL) and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R53864>.

To develop Pathway to Proficiency reports for Oregon Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the Oregon Statewide Assessment System (OSAS) Performance (originally Smarter Balanced )achievement test. This technical report details the statistical method behind the process of linking OSAS and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R53784>.

To develop Pathway to Proficiency reports for Alabama Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from Alabama's achievement test. This technical report details the statistical method behind the process of linking Alabama's state test (ACAP) and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R44719>.

To develop Pathway to Proficiency reports for North Dakota Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the North Dakota achievement test. This technical report details the statistical method behind the process of linking NDSA and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R53755>.

To develop Pathway to Proficiency reports for California Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the Smarter Balanced achievement test. This technical report details the statistical method behind the process of linking Smarter Balanced Asessments and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R44908>.

To develop Pathway to Proficiency reports for Idaho Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the Smarter Balanced achievement test. This technical report details the statistical method behind the process of linking Smarter Balanced Asessments and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R45428>.

To develop Pathway to Proficiency reports for Kansas Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the Kansas Assessment Program. This technical report details the statistical method behind the process of linking Kansas' state test and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R45820>.

To develop Pathway to Proficiency reports for Washington Star Reading and Star Math schools, we linked our scaled scores with the scaled scores from the Smarter Balanced achievement test. This technical report details the statistical method behind the process of linking Smarter Balanced Asessments and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R45817>.