From the abstract: "Computer adaptive assessments were used to monitor the academic status and growth of students with emotional behavior disorders (EBD) in reading (N = 321) and math (N = 322) in a regional service center serving 56 school districts. A cohort sequential model was used to compare that performance to the status and growth of a national user base of more than 7,500,000 students without disabilities. Consistent with numerous previous findings, status or level of performance of students with EBD was consistently low relative to their nondisabled peers. However, for the most part the students with significant EBD demonstrated rates of growth similar to the nationwide sample of nondisabled peers. There was considerable variability in the academic growth of students across grades and between treatment programs, and this variability is described and discussed. Implications for policy and practice in student progress monitoring and teacher evaluation systems are discussed." Citation: Ysseldyke, J., Scerra, C., Stickney, E., Beckler, A., Dituri, J., & Ellis, K. (2017). Academic growth expectations for students with emotional and behavior disorders. Psychology in the Schools, 54(8), 792-807.

To develop Pathway to Proficiency reports for Georgia Star Reading Enterprise and Star Math Enterprise schools, we linked our scaled scores with the scaled scores from Georgia's achievement test. This technical report details the statistical method behind the process of linking Georgia's state test (Georgia Milestones) and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R45876>.

To develop Pathway to Proficiency reports for Maine Star Reading and Star Math Enterprise schools, we linked our scaled scores with the scaled scores from the Maine achievement test. This technical report details the statistical method behind the process of linking Maine Educational Assessment (MEA) and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R58548>.

From the abstract: "Multitiered systems of support depend on screening technology to identify students at risk. The purpose of this study was to examine the use of a computer-adaptive test and latent class growth analysis (LCGA) to identify students at risk in reading with focus on the use of this methodology to characterize student performance in screening. Participants included 3,699 students in Grades 3-5. Three time points of administration (fall,winter, and spring) of the computer-adaptive reading measure were selected. LCGA results indicated 6-7 classes, depending on grade, informed by level and growth in student performance that significantly predicted failure on the statewide test administered at the end of the year. The lowest-performing classes had failure rates above 90% across all grades. The results indicate that identifying homogeneous groups of learners through LCGA may be valuable as an approach to determining students who need additional instruction. Practical implications and future directions are discussed."Citation: Keller-Margulis, M., McQuillin, S. D., Castañeda, J. J., Ochs, S., & Jones, J. H. (2018). Identifying students at risk: An examination of computer-adaptive measures and latent class growth analysis. Journal of Applied School Psychology, 34(1), 18-35.

The author explores how myON can help support literacy learning both at home and school and the factors that contribute to variations in the amount of use.Citation: O'Conner, W. L. (2017). Sociocultural early literacy practices in the school and home context: The role of a digital library (Unpublished doctoral dissertation). University of California, San Diego, CA.The full report is available online: <https://escholarship.org/uc/item/1j95c5k2>.

To develop Pathway to Proficiency reports for Arkansas Star Reading Enterprise and Star Math Enterprise schools, we linked our scaled scores with the scaled scores from the ACT Aspire achievement test. This technical report details the statistical method behind the process of linking ACT Aspire and Star Reading and Star Math scaled scores. The full report is available online: <https://docs.renaissance.com/R45710>.

This technical report details the statistical method behind the process of linking ACT Aspire and Star Reading and Star Math scaled scores in Grade 3-10. The full report is available online: <https://docs.renaissance.com/R60968>.

Developing psychometrically sound decision rules for Star Math. Manuscript submitted for publication.

From the abstract: "The purpose of this action research project was to determine if the use of a supplemental computerized math practice program increases student achievement in the fractions domain for fifth graders. Two fifth grade math classes received the same classroom instruction. However, one class also utilized a computerized program three times per week for 20-30 minutes over a six-week period. Data was collected through quantitative pre- and post-assessments, quantitative survey questions, and qualitative student responses. Analysis of the data collected suggests that using the supplemental computerized math program, Freckle Math (formerly Front Row Education), in addition to regular classroom instruction, increases student achievement in the fractions domain at the fifth grade level."Citation: Tesch, S. (2017). Math technology: Assessing the education value of a supplemental practice program (Master's thesis). Northwestern College, Orange City, Iowa.The research study is available online: <https://nwcommons.nwciowa.edu/education_masters/19/>.

From the abstract: "Although extensive research exists on the use of curriculum-based measures for progress monitoring, little is known about using computer adaptive tests (CATs) for progress-monitoring purposes. The purpose of this study was to evaluate the impact of the frequency of data collection on individual and group growth estimates using a CAT. Data were available for 278 fourth- and fifth-grade students. Growth estimates were obtained when five, three, and two data collections were available across 18 weeks. Data were analyzed by grade to evaluate any observed differences in growth. Further, root mean square error values were obtained to evaluate differences in individual student growth estimates across data collection schedules. Group-level estimates of growth did not differ across data collection schedules; however, growth estimates for individual students varied across the different schedules of data collection. Implications for using CATs to monitor student progress at the individual or group level are discussed." Citation: Nelson, P. M., Van Norman, E. R., Klingbeil, D. A., & Parker, D. C. (2017). Progress monitoring with computer adaptive assessments: The impact of data collection schedule on growth estimates. Psychology in the Schools, 54(5), 463-471.More information about this article is available online: <http://onlinelibrary.wiley.com/doi/10.1002/pits.22015/abstract>.

This white paper explains the research base for Freckle's differentiation methodology.Citation: Freckle Education. (2017). Freckle's research-based differentiation methodology. San Francisco, CA: Author.The research study is available online: <https://docs.renaissance.com/R63352>.

From the abstract: "This paper presents a longitudinal study for predicting third grade reading proficiency levels and scaled scores from Pre-K and Kindergarten early literacy assessments. The study examines the predictive relationship of early pre-literacy status measures and the same students' reading proficiency measures, four years later, at the end of third grade."Citation:Olsen, J. B., & McBride, J. R. (2015, April 15-19). Predicting early reading proficiency using pre-school early literacy scores [Paper presentation]. 2015 National Council on Measurement in Education Annual Meeting, Chicago, IL, United States.(Email research@renaissance.com to request a copy of this study from the Renaissance Research Department.)

This technical report details the statistical method behind the process of linking the State of Texas Assessments of Academic Readiness (STAAR) Algebra 1 End of Course (EOC) and Star Math scaled scores.The full report is available online: <https://docs.renaissance.com/R57781>.