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The study uses two California student cohorts with Smarter Balance (SB) mathematics scores, clustering SB items by prealgebra knowledge domains and algebra content areas. Residuals and factor scores support regression models linking prealgebra competencies to later algebra outcomes, informing SB use for monitoring readiness for high school algebra.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-math-education-relations-by-analyzing-large-data-sets-ii-research-memorandum-no-rm-21-02/583725/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-math-education-relations-by-analyzing-large-data-sets-ii-research-memorandum-no-rm-21-02/583725.png","ImageObject",300,407,{"name":92,"@type":93},"Connor ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the goal of EMERALDS II in this report?","Question",{"text":112,"@type":113},"EMERALDS II aims to identify specific Common Core procedural, conceptual, and problem-solving competencies in earlier grades that best predict success in later algebra areas.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data sources are used to study these relationships?",{"text":117,"@type":113},"The study uses two cohorts of California students with Smarter Balance mathematics scores, with SB items clustered by prealgebra knowledge domains and algebra content areas.",{"name":119,"@type":110,"acceptedAnswer":120},"How are student performance measures characterized and modeled?",{"text":121,"@type":113},"Student performance is characterized using item residuals and factor scores, which are then used in a series of regression models to examine relationships between prealgebra competencies and later algebra outcomes.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},583725,1792191236,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},687207022233,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Research Memorandum  \nETS RM–21-02  \nExploring Math Education Relations by Analyzing Large Data Sets II  \nJonathan Weeks Patricia Baron  \nApril 2021  \nETS Research Memorandum Series  \n\n|  | EIGNOR EXECUTIVE EDITOR |  |\n| --- | --- | --- |\n|  | Laura Hamilton |  |\n|  | Associate Vice President |  |\n|  | ASSOCIATE EDITORS |  |\n| Beata Beigman Klebanov |  | Anastassia Loukina |\n| Senior Research Scientist |  | Managing Senior Research Scientist |\n| Heather Buzick |  | Gautam Puhan |\n| Senior Research Scientist |  | Psychometric Director |\n| Tim Davey |  | Jonathan Schmidgall |\n| Research Director |  | Research Scientist |\n| John Davis |  | Jesse Sparks |\n| Research Scientist |  | Research Scientist |\n| Marna Golub-Smith |  | Michael Walker |\n| Principal Psychometrician |  | Distinguished Presidential Appointee |\n| Priya Kannan |  | Klaus Zechner |\n| Research Scientist |  | Research Scientist |\n| Sooyeon Kim |  | Rebecca Zwick |\n| Principal Psychometrician |  | Distinguished Presidential Appointee |\n| Kim Fryer | PRODUCTION EDITORS | Ayleen Gontz |\n| Manager, Editing Services |  | Senior Editor |\n\nSince its 1947 founding, ETS has conducted and disseminated scientific research to support its products and services, and to advance the measurement and education fields. In keeping with these goals, ETS is committed to making its research freely available to the professional community and to the general public. Published accounts of ETS research, including papers in the ETS Research Memorandum series, undergo a formal peer-review process by ETS staff to ensure that they meet established scientific and professional standards. All such ETS-conducted peer reviews are in addition to any reviews that outside organizations may provide as part of their own publication processes. Peer review notwithstanding, the positions expressed in the ETS Research Memorandum series and other published accounts of ETS research are those of the authors and not necessarily those of the Officers and Trustees of Educational Testing Service.  \nThe Daniel Eignor Editorship is named in honor of Dr. Daniel R. Eignor, who from 2001 until 2011 served the Research and Development division as Editor for the ETS Research Report series. The Eignor Editorship has been created to recognize the pivotal leadership role that Dr. Eignor played in the research publication process at ETS.  \nExploring Math Education Relations by Analyzing Large Data Sets II  \nJonathan Weeks and Patricia Baron  \nETS, Princeton, New Jersey, United States  \nApril 2021  \nCorresponding author: J. Weeks, [E-mail: jweeks@ets.org](E-mail: jweeks@ets.org)  \nSuggested citation: Weeks, J., & Baron, P. (2021) . Exploring math education relations by analyzing large data sets II (Research Memorandum No. RM-21-02) . ETS.  \nFind other ETS-published reports by searching the ETS ReSEARCHER  \ndatabase at [https://search.ets.org/researcher/](https://search.ets.org/researcher/)  \nTo obtain a copy of an ETS research report, please visit  \n[https://ets.org/research/contact/](https://ets.org/research/contact/)  \nAction Editor: Sooyeon Kim  \nReviewers: Dan McCaffrey and Diego Zapata  \nCopyright © 2021 by Educational Testing Service. All rights reserved.  \nETS and the ETS logo are registered trademarks of  \nEducational Testing Service (ETS) . All other trademarks are the property of their respective owners.  \nAbstract  \nThe current project, Exploring Math Education Relations by Analyzing Large Data Sets (EMERALDS) II, is an attempt to identify specific Common Core State Standards procedural, conceptual, and problem-solving competencies in earlier grades that best predict success in algebraic areas in later grades. The data for this study include two cohorts of California students with Smarter Balance (SB) mathematics scores. SB items were clustered based on a priori defined prealgebra knowledge domains and algebra content areas. Two approaches were then used to characterize student performance for several of these competen","cbCaiqV1CukMjDfj","https://ap.wps.com/l/cbCaiqV1CukMjDfj","pdf",1571612,98,"English","# Overview\n## Smarter Balanced Test\n## Current Project\n## Methodology\n## Data","[{\"question\":\"What is the goal of EMERALDS II in this report?\",\"answer\":\"EMERALDS II aims to identify specific Common Core procedural, conceptual, and problem-solving competencies in earlier grades that best predict success in later algebra areas.\"},{\"question\":\"What data sources are used to study these relationships?\",\"answer\":\"The study uses two cohorts of California students with Smarter Balance mathematics scores, with SB items clustered by prealgebra knowledge domains and algebra content areas.\"},{\"question\":\"How are student performance measures characterized and modeled?\",\"answer\":\"Student performance is characterized using item residuals and factor scores, which are then used in a series of regression models to examine relationships between prealgebra competencies and later algebra outcomes.\"}]","Exploring Math Education Relations by Analyzing Large Data Sets II - Research Memorandum No. RM-21-02 | PDF",1792048178,247]