MAP Growth

Product or Programme · All products and programmes
Evidence · 103 claims (99 for, 4 mixed) · 47 studies (35 associational, 6 design, 4 theoretical, 2 causal), q1–q3 · 2 of 47 report an effect size · 99 claims rest on one study

Description

MAP Growth is NWEA's computer-adaptive assessment system, with national achievement and growth norms and reporting tools such as the MAP Growth National Dashboard.

Components

• NWEA 2020 MAP Growth Achievement Status and Growth Norms for Students and Schools: The 2020 MAP Growth Norms are a norming study and released reference tables characterizing student and school achievement and growth in academic subjects on the MAP Growth scale. The report states that norms "characterize each score in terms of its re lative distance from other scores in a distri- bution of scores." The release covers mathematics, reading, language usage, and science, with appendices of status norms, growth norms, status percentiles, and conditional growth distributions and percentiles for students and schools. (Thum et al. (2020))
• 2025 MAP Growth norms update with Enhanced Item Selection Algorithm (EISA) alignment: The 2025 MAP Growth norms update recalibrates the national reference group for RIT scores, achievement percentiles, conditional growth percentiles, and projected proficiency. It was prompted by post-COVID achievement and growth patterns falling "substantially below prepandemic trends" and incorporates technical enhancements, most notably that "the update aligns norms to the EISA, which strengthens alignment of MAP Growth to grade level content." NWEA updates its norms every three to five years; supporting documentation includes the 2025 Norms Technical Manual and Norms Toolkit. (Hashim (2026))
• Adaptive interim assessment with an automated disengagement-detection engagement feature: The article evaluates an adaptive interim assessment administered to K-12 students both in school and remotely. Its engagement feature "automatically paused the test event of a disengaged student and notified the test proctor," enabling proctors to intervene whether they were "in the same room as the student" or proctoring remotely. The study used disengagement on this assessment to compare remote and in-school testing contexts. (Steven Wise et al. (2022))
• Content Proximity item-selection algorithm for MAP Growth assessments: Content Proximity is a new item-selection algorithm used by MAP Growth assessments, introduced in a spring 2022 pilot study reported by NWEA. The article describes it as "this new item-selection algorithm" leveraged by MAP Growth assessments, and the pilot evaluated the resulting assessments' validity, reliability, and test score comparability. The supplied text prints no further detail about how the algorithm selects items or how it differs from prior approaches. (Patrick Meyer et al. (2023))
• Technical appendix document detailing sample and methods for the Learning during COVID-19 achievement brief: A technical brief published by NWEA Research in July 2021, released alongside the research brief on COVID-19-era reading and math achievement. Its stated purpose is to "share more detailed results and to describe more fully the sample and methods used in the research included in the brief." It serves as the methods and detailed-results companion to the main brief on 2020-21 school-year achievement. (NWEA Research (2021))
• MAP Reading and Mathematics computerized adaptive tests (NWEA): MAP tests are computerized adaptive assessments published by NWEA since 2000 for grades 3-9, intended "to provide educators with information to inform teaching and learning in Reading, Mathematics, and Science". The Reading test has about 40 multiple-choice items and four goals; the Mathematics test has 50 items and seven goals. Tests are aligned to state content standards, and "the marginal reliabilities of tests across 50 states and grades are consistently in the low to mid 0.90's". (Shudong Wang et al. (2013))
• MAP Growth assessment dataset covering 5.5 million students in grades 3-8 in reading and math: The brief draws on the MAP Growth assessment program, NWEA's national assessment product, to examine pandemic-era learning. The analysis uses "data from 5.5 million students in grades 3-8 who took MAP Growth assessments in reading and math," comparing "gains across the 2020-21 school year and student achievement in spring 2021" to pre-pandemic trends. The dataset supports both within-year growth comparisons and spring achievement level comparisons. (Learning during COVID-19: Reading et al. (2021))
• MAP Growth assessment dataset used to measure pandemic achievement effects (8.3 million students, grades 3-8): The brief's evidence base is a large-scale observational dataset of MAP Growth assessment records in reading and math. It covers "8.3 million students in grades 3-8 who took MAP Growth assessments in reading and math between 2018–19 and 2021–22" alongside "a similarly sized sample of students who tested between 2015–16 and 2018–19" as a pre-pandemic comparison. The dataset supports grade-, subgroup-, and school-poverty-level analyses of pandemic impacts and recovery. (Kuhfeld et al. (2022))
• MAP Growth assessment: MAP Growth is an assessment product published by NWEA, listed as the product associated with NWEA research publications on achievement, growth, equity, and test engagement. In the supplied text it appears repeatedly as "Products: MAP Growth" across related technical reports, research briefs, and data tools, including national trend dashboards reporting "student academic achievement and growth for grades K-8." It is the assessment context within which NWEA research on test effort and subgroup score differences is situated. (James Soland (2019))
• NWEA MAP Growth assessment data as the measurement basis for COVID-19 learning-loss research: The page situates the technical appendix within NWEA's portfolio of research using MAP Growth assessment data, including the "MAP Growth National Dashboard" cited as the data source for related post-COVID trend reports. MAP Growth serves as the achievement measure underpinning the brief's comparisons of 2020-21 gains and spring 2021 achievement to pre-pandemic trends and levels. (NWEA Research (2021))
• MAP Growth assessment data as the evidence base for district-level absenteeism and recovery analysis: MAP Growth is NWEA's assessment product whose district-level data underpins this research brief. The brief states it is "Using MAP Growth data from 2,000 US school districts" to study the link between absenteeism and academic recovery in math and reading. The dataset spans a nationwide sample of US districts, enabling district-level comparisons of recovery trajectories. (Sofia Postell et al. (2026))
• MAP Growth rapid-guessing invalidation criterion and four-level disengagement classification: MAP Growth test events carry both performance data (RIT score plus standard error) and engagement data (percent of item responses that were rapid guesses). The study formed "four levels of test-taking disengagement": serious (30% or more rapid guesses), moderate (11 to 30%), mild (1 to 10%), and zero disengagement. The invalidation criterion holds that when students rapid-guess on 30% or more of items, scores are apt to be so distorted they are too untrustworthy to report. (Wise (2019))
• MAP Growth assessment as a data source for gender-gap trend analysis: MAP Growth is NWEA's national assessment used in this brief as one of three data sources for estimating gender gaps in 8th-grade STEM skills. The brief analyzes "five years of data from three national assessments – TIMMS, NAEP, and NWEA's MAP Growth" and separately draws on "a sample of MAP Growth data from two million students" for subgroup analyses. (Megan Kuhfeld et al. (2025))
• MAP Growth assessments as the measurement instrument for cross-sector K-8 growth comparison: MAP Growth assessments are standardized tests administered to students in the study's national sample. The article uses scores from students "who took MAP Growth assessments" to "examine achievement growth over time between sectors," comparing Catholic and public school students from kindergarten through eighth grade in math and reading. (Dallavis et al. (2021))
• MAP Growth linking study report with cut score tables, classification accuracy statistics, and proficiency projections: A state-specific report NWEA produces for each linking study that "explains the methodology, summarizes the study sample, presents the results, and provides evidence that shows how well the RIT scores can predict state summative performance." Each report presents sample demographics, descriptive statistics, fall/winter/spring RIT cut scores corresponding to state performance levels, classification accuracy statistics, and probabilities of achieving proficiency based on RIT scores. Results are also embedded in the NWEA reporting system and individual score reports. (Ann Hu (2021))
• MAP Growth assessment and National Dashboard as the data infrastructure for NWEA COVID-recovery research: MAP Growth is NWEA's assessment product, and the MAP Growth National Dashboard is the data source NWEA researchers use for post-COVID academic recovery analyses. The page repeatedly names it: reports 'focus on academic growth trends post-COVID' 'Using data from the MAP Growth National Dashboard', and one related brief analyzed 'MAP Growth data from 2,000 US school districts.' The 2025 update to MAP Growth national norms and an enhanced item selection algorithm (EISA) are also named as changing how norm-referenced metrics are interpreted. (Megan Kuhfeld et al. (2022))
• MAP Growth assessment and National Dashboard as data sources for NWEA COVID-recovery research: MAP Growth is NWEA's assessment product, and the MAP Growth National Dashboard is the data source NWEA researchers use for post-COVID achievement and growth trend analyses. The page repeatedly names it as the underlying data source: "Using data from the MAP Growth National Dashboard, this research brief highlights an urgent challenge in academic recovery: middle school reading." It supports NWEA's series of briefs on pandemic recovery, absenteeism, and classroom academic diversity. (Megan Kuhfeld (2022))
• MAP Growth assessment data as the evidence base for national pandemic-recovery monitoring: MAP Growth is NWEA's assessment product whose nationwide score data underpins this brief's findings on pandemic recovery. The brief is described as sharing "several key findings" "Based on data from 2023-24," covering growth trends, pre-COVID gap widening, months-of-schooling catch-up estimates, and race/ethnicity comparisons. It functions here as the measurement instrument enabling longitudinal recovery monitoring. (Karyn Lewis (2024))
• Same-day MAP Growth retest dataset spanning four test types and four testing terms: The study extracted MAP Growth Common Core test events in Reading 2-5, Reading 6+, Math 2-5, and Math 6+ from four testing terms: Fall 2017, Winter 2018, Spring 2018, and Fall 2018. Table 1 prints total test events ranging from 4,324,638 (Reading 6+) to 4,864,398 (Math 2-5), with retests within one day of 36,971 (Reading 2-5), 31,694 (Reading 6+), 16,106 (Math 2-5), and 16,938 (Math 6+), over 100,000 retests in total. (Wise (2019))
• MAP Growth assessment scores as the achievement outcome data source: MAP Growth is NWEA's assessment product whose scores serve as the achievement outcome in this heat-achievement analysis. The brief states the study "combines daily temperature data from various sources with MAP Growth scores for grades 3–8 across six states", drawing on roughly three million test events from fall 2022, 2023 and 2024. It functions here as the student achievement measure linked to daily temperature exposure. (Sofia Postell et al. (2025))
• NWEA MAP Growth vertically scaled assessment as a common comparison measure: MAP Growth is NWEA's assessment used in this study as a common assessment against which SEDA's district growth estimates are compared. The article describes it as a "vertically scaled" assessment from which district growth estimates in mathematics and ELA can be derived, serving as an independent benchmark for the convergent validity analysis of SEDA. Its estimates show a high degree of congruence with SEDA's, with small systematic discrepancies in some contexts. (Megan Kuhfeld et al. (2019))
• NWEA 2023-24 student achievement data brief on pandemic recovery: A research brief from NWEA reporting student achievement trends based on data from 2023-24. It shares key findings on growth relative to pre-pandemic trends, the widening gap between pre-COVID and COVID test score averages, and months of schooling needed to catch up. The brief is associated with the MAP Growth product line and topics of COVID-19 and schools, equity, and growth. (Karyn Lewis (2024))
• NWEA chronic absenteeism research brief linking absenteeism to slower academic recovery: A research brief by Sofia Postell, Emily Morton, and Megan Kuhfeld examining how absenteeism relates to recovery from COVID-era learning disruptions. The page describes it as follows: "Using MAP Growth data from 2,000 US school districts, NWEA researchers analyzed whether persistently high absenteeism rates were associated with slower recovery in math and reading." It is listed with a companion technical appendix under the topics COVID-19 & schools and Growth. (Megan Kuhfeld (2022))
• NWEA MAP Growth assessment as national data source for seasonal skill-growth analysis: The NWEA MAP Growth assessment supplies the recent national data on U.S. public school students used in this study's seasonal comparisons. The abstract states the analysis covers "numerous grade levels from the NWEA MAP Growth assessment" and four domains: math, reading, language use, and science. In the article it functions as the measurement instrument enabling school-year versus summer variance comparisons. (Dennis Condron et al. (2021))
• School Challenge Index (SCI) and MAP Growth conditional growth metrics for school-level poverty and growth measurement: The School Challenge Index (SCI) is an NWEA metric created during the 2011 norms development to consistently measure the level of challenge a school faces across the United States. The article explains that "The SCI is primarily based on the percentage of students eligible for FRL, but is moderated by other factors, such as location, school level, Title I eligibility, and school type." MAP Growth supplies the achievement and growth data, with conditional growth index (CGI) and conditional growth percentile (CGP) metrics designed to compare the relative growth of different students fairly. The study used median achievement and growth percentiles per school as its school-level measures. (Andy Hegedus (2018))
• Technical appendix document detailing sample and methods for the COVID-19 achievement divide brief: A technical brief published by NWEA Research in November 2022 accompanying the research brief 'The widening achievement divide during COVID-19.' Its stated purpose is to 'share more detailed results and to describe more fully the sample and methods used in the research included in the brief.' It is tagged under the topics COVID-19 & schools and Equity and is authored by Megan Kuhfeld, Meredith Langi, and Karyn Lewis. (Megan Kuhfeld et al. (2022))
• ARLIS: Algorithm for Reward Listed Item Selection: ARLIS is the enhanced MAP Growth item-selection algorithm introduced by the Content Proximity Project. It is "a compensatory version of the maximum priority index (Cheng & Chang, 2009) that borrows ideas from reinforcement learning," which may be thought of as reinforcement learning with a greedy policy and frequent deterministic rewards. An item-selection engine acts as the agent choosing items from the pool; a reward is assigned to each content feature, a total reward (a weighted average of functions representing statistical and content requirements) is computed per item, items are listed in descending order of total reward, and the highest-value item is selected, with ties broken randomly (J. Patrick Meyer et al. (2023))
• MAP Growth theory of action within a comprehensive assessment system: The report articulates a theory of action positioning MAP Growth as the interim component of a comprehensive assessment system alongside summative and formative assessments. Its premise is that "when MAP Growth is situated in a comprehensive assessment system and used for its intended purposes (to yield information about student learning and enable educators to make data-informed decisions about curriculum and instruction), all students learn." Intended uses are classified as instructional, predictive, or evaluative, with aggregation matched to the level of decision-making. (MAP Growth Technical Report for 2024–2025 (2026))
• RIT vertical equal-interval scale grounded in item response theory: The RIT scale is the report's central measurement framework: an equal-interval vertical scale spanning grades K–12, derived from item response theory and the Rasch model. The report explains that "The RIT scale has two key characteristics. It is a vertical scale with equal-interval units," and that "the equal-interval characteristic of the RIT scale allows the measurement of growth over two or more time points (i.e., learning) by calculating the difference in scores." IRT places item difficulty and person ability on the same continuous scale, enabling adaptive testing and score comparability across item sets. (MAP Growth Technical Report for 2024–2025 (2026))
• MAP (Measures of Academic Progress) computerised adaptive test system: MAP is a computerised adaptive assessment system published by NWEA since 1976, covering reading, language usage, mathematics and science. "MAP items are calibrated with the Rasch model (Rasch, 1961), and all MAP tests are vertically scaled", and the article reports that "the marginal reliabilities of test scores are consistently in the low- to mid- 0.90s range across grades, tests and states". Tests use fixed-length CAT with state-customised item pools organised into content-standard goals, and goal scores serve as the observed indicators in this study's MLGM analyses. (Wang et al. (2013))
• MAP Growth computer-adaptive assessment and its national norms and dashboard: MAP Growth is NWEA's assessment product, administered remotely or in person, whose scores are interpreted against national achievement and growth norms and summarized through tools such as the MAP Growth National Dashboard. The page describes reports "Using data from the MAP Growth National Dashboard" and a technical report documenting "adaptive testing methods, reliability, validity, fairness, and score interpretation across grades and courses." It underpins the COVID-era achievement and growth analyses indexed here. (Technical appendix for: Learning during COVID-19: Initial findings on students' reading et al. (2020))
• MAP Growth assessments as the data source for pandemic learning-loss analysis: MAP Growth is NWEA's assessment product whose scores supply the achievement and growth data used in this brief and related NWEA COVID-19 recovery research. The brief draws on "data from 6 million students in grades 3-8 who took MAP Growth assessments in reading and math" to compare pandemic-period outcomes with pre-pandemic trends. Related NWEA publications also use the MAP Growth National Dashboard for post-COVID trend analyses. (Karyn Lewis (2021))
• MAP Growth interim computer adaptive assessment system: MAP Growth is an interim computer adaptive test administered up to four times per year in Math, Reading, Language Usage, and Science, with scores on the RIT scale. The report states it is "an interim computer adaptive test administered multiple times per year (fall, winter, spring, summer)" and that "Scores are reported on the RIT scale, an equal-interval vertical scale that allows comparison across grades and terms." It supports in-school and remote administration with trained proctors. (MAP Growth Technical Report for 2024–2025 (2026))
• MAP assessment dataset on reading growth for students with visual impairments: The study used data from Northwest Evaluation Association's Measures of Academic Progress (MAP) assessment to analyze reading achievement for "224 students with visual impairments in grades 3 - 10, in four states over an eight-year time period." Growth was compared with "a nationally normed group of students from the general population." The dataset supports longitudinal trajectory analysis of an understudied population. (Beth Boroson et al. (2017))
• MAP Growth reading and mathematics assessment dataset (7 million students, grades 3-8, 25,000 schools, 2020-21 to fall 2022): The brief draws on MAP Growth reading and mathematics assessments administered between 2020-21 and fall 2022 to 7 million students in grades 3-8 in 25,000 schools. The text states the data come from "7 million students in grades 3-8 in 25,000 schools who took MAP Growth reading and mathematics assessments between 2020–21 and fall 2022." It supports NWEA's ongoing research agenda on pandemic effects on achievement. (Karyn Lewis (2022))
• Nationwide MAP Growth middle-school score sample: The data source for this brief is a nationwide sample of MAP Growth scores for middle school students, which NWEA researchers used to study Algebra placement. The description states the researchers worked "Using a nationwide sample of MAP Growth scores for middle schoolers" to examine readiness and enrollment. It serves as the evidence base for the brief's placement and outcome analyses. (Megan Kuhfeld et al. (2026))
• Longitudinal MAP reading dataset for 224 students with visual impairments: A longitudinal dataset of reading achievement drawn from "Northwest Evaluation Association’s Measures of Academic Progress assessment" covering "224 students with visual impairments in grades 3–10, in four states over an eight-year time period." The article uses it to describe reading growth for this population and to benchmark it against a nationally normed general-population group. It is the evidentiary basis of the study's analysis. (Boroson et al. (2017))
• National analysis dataset covering over 2,300 rural US schools' math and reading achievement: The brief draws on a large national dataset of rural schools to describe student achievement. The announcement states it uses "data from over 2,300 rural schools across the US" to provide "unique insight into math and reading achievement of students in rural schools." The dataset supports descriptive analysis of rural achievement and growth patterns nationwide. (Johnson et al. (2022))
• NWEA longitudinal growth database used to track top students' achievement trends: The study's data source is NWEA's longitudinal growth database, which the description says was used to track achievement trends: "achievement trends from NWEA's longitudinal growth database were used to track students who scored at or above the 90th percentile on this assessment." It supports multi-year follow-up of individual students' percentile positions across the middle school and high school span. (Yun Xiang et al. (2013))
• NWEA MAP Growth longitudinal dataset for DHH students, grades 2–8, seven reading and mathematics domains over 5 years: This element is the longitudinal achievement dataset analyzed in the study: NWEA MAP® Growth™ data covering DHH students from grades 2 to 8. The article states it "provides an analysis of NWEA MAP® Growth™ data from grades 2 to 8 across seven reading and mathematics domains over a period of 5 years," making it a multi-domain, multi-year record of academic growth used to model how DHH students' reading and mathematics achievement progresses over time. (Stephanie Cawthon et al. (2022))
• NWEA MAP Growth interim assessments: NWEA's MAP Growth assessments are interim assessments used by states and districts during the 2020-2021 school year. The article describes them as the data source for investigating school-composition change: researchers "examine data from NWEA's MAP Growth assessments, interim assessments used by states and districts during the 2020–2021 school year". School-aggregate scores from these assessments are used by school officials to monitor school performance and make policy decisions. (Jonathan Schweig et al. (2022))
• Spanish MAP Growth Math calibration dataset of 3,908 items and over 147,000 monolingual and 33,000 bilingual students: The study's empirical basis is item and person data pulled from the NWEA Growth Research Database (GRD) covering five years of Spanish math testing. The item side comprises "3,908 Spanish math items across different tests" in the selected sample (3,955 collected), spanning 24 Spanish math tests from K-2 through 6+. The person side comprises 147,094 collected monolingual students (147,044 selected) and 33,624 collected bilingual students (33,607 selected), about 75% Hispanic across grades. The dataset supports the fixed-person-score calibration designs and all item and person results in the report. (Wang et al. (2020))
• NWEA MAP Growth assessment and longitudinal achievement database: MAP Growth is a computer adaptive test that measures achievement status and growth over time, vertically scaled to allow estimation of gains across time and aligned to state content standards, with scores reported on the RIT (Rasch unIT) scale. The study drew on the NWEA anonymized longitudinal student achievement database covering approximately 2.7 million grade 3-8 students in 17,000 public schools across the 2018-19, 2019-20, and 2020-21 school years, with percentile ranks computed against the 2020 NWEA MAP Growth norms from a pre-pandemic 2015-16 through 2017-18 norming sample. (Megan Kuhfeld et al. (2021))

Claims

• The 2020 norms report states the MAP Growth scale remains stable while status patterns shifted relative to the 2015 norms [+W]
• EISA-based MAP Growth testing produced slightly lower fall Mathematics scores but higher winter and spring Mathematics scores than traditional MAP Growth, and equipercentile linking was used to align the two versions for norming [+W]
• Under the 2025 norms, higher math growth expectations lower conditional growth percentiles for higher-growth students [+W]
• Under the 2025 MAP Growth norms, the same reading RIT score can rank higher in achievement percentile due to national declines in achievement [+W]
• Norm shifts can change teacher evaluation CGP outcomes independently of instructional quality [+W]
• Norm shifts can raise achievement percentiles for schools serving struggling students even when RIT gains and projected proficiency do not improve [+W]
• Static percentile screening cut points carried across norm sets can under-identify students who remain below grade level [+W]
• The 2025 MAP Growth norms were derived from one of the largest norming samples ever created, spanning 116,225,831 test events from 13,804,182 students in 29,720 schools across 7,000 districts over six testing terms [+W]
• The assessment's engagement feature had a consistently positive impact under both in-person and remote proctoring [+W]
• The Content Proximity spring 2022 pilot study examined validity, reliability, and test score comparability of MAP Growth assessments using the new item-selection algorithm [+W]
• Content Proximity pilot tests contained significantly more on-grade items and made better use of adjacent-grade items than traditional MAP Growth tests [+M]
• Content Proximity math scores were consistently higher than traditional MAP Growth math scores, with effects ranging from negligible to small by grade [+W]
• Growth-norms predictions showed larger RMSDs for pilot math scores but similar predictions for reading [~W]
• The adaptive algorithm's item selection produces a significantly lower SEM than fixed-form tests [+W]
• Pilot test events largely fulfilled updated blueprint requirements, including increased reading passages per test event [+M]
• A one-factor unidimensional model is the most reasonable fit for MAP computerized adaptive Reading and Mathematics tests across grades, terms, and ten states, though Michigan Mathematics misses the RMSEA criterion [+W]
• MAP tests show metric invariance across grades for all studied tests except Michigan Mathematics tests [+W]
• Chi-square difference tests show significant loading invariance constraints for Kansas Reading but all constraints significant for Michigan Mathematics [+W]
• The authors conclude that MAP reading and mathematics CAT constructs are invariant across five time points in ten states (except Indiana mathematics), though their mathematics tables show strong-invariance CFI drops above 0.01 in most states [+M]
• On average, students across most grades made reading and math gains during the 2020-21 pandemic-affected school year [+W]
• Reading and math gains during 2020-21 were diminished compared to pre-pandemic trends, especially in the latter half of the school year [+W]
• American Indian and Alaska Native, Black, and Latinx students and students in high-poverty schools were disproportionately impacted by pandemic-era achievement declines, particularly in elementary grades [+W]
• Students in grades 3-8 made reading and math gains in 2020-21 but ended the year with lower achievement than in a typical year, with larger declines in math than in reading [+W]
• Differential student test effort may bias achievement gap estimates [+W]
• Student test effort differs substantially across student gender and racial subgroups [+M]
• The Learning during COVID-19 research investigated whether 2020-21 school-year gains and spring 2021 achievement differed from pre-pandemic trends and levels in reading and math. [+W]
• Persistently high chronic absenteeism rates are associated with slower academic recovery in math and reading after COVID-era disruptions [+W]
• Rapid guesses on MAP Growth reflect a lack of engagement rather than a lack of knowledge [+M]
• Students who rapid-guess less on a MAP Growth retest tend to see score gains, while those who rapid-guess more tend to see declines [+M]
• Seriously disengaged initial testers who stopped rapid-guessing on retest showed large RIT gains, averaging about 6 points in Reading and 12 in Math [+M]
• E-M scoring partially accounts for observed RIT score differences between disengaged first tests and engaged retests [+W]
• E-M scoring leaves residual score distortion, suggesting some disengaged test responses are nonrapid rather than rapid guesses [+W]
• Rapid-guessing behavior can distort test scores and adversely affect measurement [+W]
• Gender gap trends in 8th-grade STEM were estimated from five years of TIMSS, NAEP, and MAP Growth data spanning the pandemic [+W]
• A two-million-student MAP Growth sample was used to test whether gender gaps widened among low- and high-achieving students [+W]
• Adaptive testing yields better measurement precision than a fixed-form test of the same length [+W]
• Catholic school students score higher in math and reading than public school students on average, but enter each school year at a higher level [+W]
• Catholic school students in earlier grades show less academic-year growth than public school peers in both reading and math [+W]
• In middle school, academic-year growth in math was comparable across Catholic and public school sectors [+W]
• Public school students close the achievement gap with Catholic school students to some degree during the school year [+W]
• Achievement growth patterns between Catholic and public school students varied by age and subject [+W]
• MAP Growth spring RIT cut scores classify students as proficient or not proficient on the MCA-III Mathematics test with 0.87–0.90 overall accuracy [+W]
• Proficiency projections estimate the probability of reaching Meets proficiency on the state test from fall, winter, or spring RIT scores [+W]
• MAP Growth RIT scores correlate strongly (r = 0.92–0.93) with state summative mathematics scores in the Minnesota MCA-III linking study [+W]
• Linking studies predict proficiency on state summative assessments from MAP Growth scores [+W]
• The average student needs the equivalent of 4.8 additional months of schooling to catch up in reading and 4.3 months in math [+W]
• Student growth during 2023-24 fell short of pre-pandemic trends in nearly all grades, continuing stalled pandemic recovery [+W]
• The gap between pre-COVID and COVID test score averages widened in 2023-24 in nearly all grades, by an average of 36% in reading and 18% in math [+W]
• Test-taking disengagement frequently differs between a MAP Growth initial test and a same-day retest [+M]
• Discrepancies between SEDA and MAP Growth ELA growth estimates are slightly more pronounced in high-socioeconomic-status districts [+W]
• Small systematic discrepancies imply SEDA growth estimates may not generalize to MAP Growth-based estimates in some states [+W]
• SEDA and MAP Growth district growth estimates in ELA correlate at .82 (precision-adjusted) [+W]
• SEDA growth estimates show a high degree of congruence with MAP Growth-based estimates overall [+W]
• SEDA and MAP Growth district growth estimates in mathematics correlate at .90 (precision-adjusted) [+W]
• Growth for all race/ethnicity groups lagged pre-pandemic trends in 2023-24, with marginalized students furthest from recovery [+W]
• NWEA analysis of 2,000 US school districts examined whether persistently high absenteeism was associated with slower math and reading recovery [+W]
• A NWEA research brief examines the relationship between chronic absenteeism and academic recovery from COVID-era disruptions to learning [+W]
• Seasonal patterns are discrepant across national data sets [+W]
• Seasonal comparisons can test whether schooling exacerbates, reduces, or reproduces overall skill inequality [+W]
• Prior seasonal studies were limited to social-category skill gaps and early grades [~W]
• About 46% of the lowest 5% achieving schools show the same growth as the highest 5% achieving schools [+W]
• 77% of the lowest 5% achieving schools (spring reading) grow their typical student better than the 30th percentile [+W]
• Growth of schools selected by fall achievement differs by subject: bottom 5% math schools tended to grow worse, while reading showed similar average growth [+W]
• School poverty (%FRL) accounts for about 50% of a school's median student achievement [+W]
• School poverty is only weakly associated with median student growth in a school [+W]
• Achievement of the lowest-growth schools varies dramatically, and fall achievement accounts for about 80% of spring achievement versus 14% for growth [+W]
• The brief compared pandemic achievement gains to pre-pandemic trends for students low- or high-achieving at the pandemic's start [+W]
• The brief investigated the degree to which students' reading and math test scores became more variable as a result of the COVID-19 pandemic [+W]
• MAP Growth reliability coefficients average about 0.95, with lower values in Science and early grades and stronger test-retest reliability over shorter intervals [+W]
• MLGM model fit failed for the Indiana mathematics test, the sole exception to invariance support [~W]
• Quadratic latent growth fits MAP longitudinal achievement data better than linear growth across ten states [+W]
• The dashboard primarily focuses on grades 3 through 8 because remote and in-person MAP Growth results are largely comparable for grades 3 and up [+W]
• The dashboard reports median achievement percentiles normed to a nationally representative pre-COVID sample and projected proficiency against state cut scores [+W]
• Findings of reliability, engagement, and consistent score trends increase confidence in the quality of remotely administered MAP Growth data in grades 3 and up [+W]
• Remote and in-person fall 2020 MAP Growth tests show comparable psychometric characteristics and indicators of test quality [+W]
• Test engagement was high across all grades on fall 2020 MAP Growth tests in both administration modes [+W]
• DIF analyses indicate the vast majority of MAP Growth items show negligible differential item functioning across gender and racial/ethnic groups [+W]
• The test platform detects rapid guessing in real time and attempts to re-engage disengaged students to protect score validity [+W]
• Reading achievement was analyzed from 224 students with visual impairments in grades 3–10 in four states over an eight-year period using MAP data [+W]
• Students with visual impairments show accelerated reading growth between 9th and 10th grade while the nationally normed group tends to drop [+W]
• The rate of reading growth for students with visual impairments is suggested to be greater than the national norm [+W]
• Students with visual impairments show lower initial reading achievement in third grade than the national norm [+W]
• Reading growth trajectories of students with visual impairments trend upward at a steady pace while the gap with the national norm remains wide [+W]
• The study compared reading growth for students with visual impairments with a nationally normed group of students from the general population [+W]
• The brief examines whether taking Algebra in 8th grade instead of 9th grade improves student outcomes [+M]
• The brief examines how outcomes were affected for students who were or were not ready for 8th-grade Algebra [+M]
• Using nationwide MAP Growth scores, the brief examines how many students ready for Algebra actually enrolled in it [+M]
• Access to 8th-grade Algebra remains highly inequitable across the United States [+M]
• High-achieving students who fell below the 90th percentile typically stayed at the 70th percentile or higher [+W]
• The high-flyer cohort comprised 91,643 students from more than 800 schools in 28 states [+W]
• About three in five students identified as high-achieving in the first year remained high-achieving in the last year of the study [+W]
• DHH students in grades 2–8 continue to build reading and mathematics skills and do not necessarily plateau in the elementary grades [+W]
• After the U.S. Department of Education offered assessment waivers to all 50 states in 2019-2020, many educators and policymakers advocated restarting assessment programs in 2020-2021 to evaluate teaching and learning and inform recovery policies [+W]
• Student test-taking disengagement on remotely administered adaptive interim assessments differs from disengagement on the same assessment administered in school [~W]
• The NWEA COVID-19 visualization provides state-level insights into 2020–2021 MAP Growth performance to inform academic recovery [+W]
• Adaptive testing explains why about one third of calibrated Spanish math items carry calibration status 10 [+W]
• Outlier rates in Spanish item calibration are extremely low, which the authors take as evidence of very high calibration quality [+W]
• The Spanish math test population is predominantly Hispanic (about 78%), unlike the English math test population (about 15%), with implications for Spanish norms [+W]
• Old (English-borrowed) and newly calibrated Spanish item RITs differ negligibly, with correlations of 0.98 and no statistically significant paired t-test differences [+W]
• Including or excluding calibration status 10 items in scoring has a negligible impact on Spanish student RIT scores for monolingual and bilingual students [+W]
• Rescoring with new item parameters changed average ability estimates negligibly, with new and original scores correlated above 0.99 [+W]
• DIF study finds most items in category A, with C DIF rare (~1%) except for the Native English/Bilingual group (6.66%) [+W]
• Large-scale longitudinal study measures middle school academic trajectories relative to college readiness benchmarks for more than 360,000 US students [+W]

Related Products and Programmes

- - MAP Growth grades 1–2 achievement dataset (spring 2019 and spring 2022 samples)

Key Sources

• Thum, Y. M., & Kuhfeld, M. (2020). NWEA 2020 MAP Growth Achievement Status and Growth Norms for Students and Schools. NWEA Research Report. Portland, OR: NWEA. https://www.nwea.org/research/publication/nwea-2020-map-growth-achievement-status-and-growth-norms-for-students-and-schools/
• Hashim, A. K. (2026). How post-COVID norm shifts change the meaning of MAP Growth metrics for school accountability. NWEA White Paper. https://www.nwea.org/research/publication/how-post%e2%80%91covid-norm-shifts-change-the-meaning-of-map-growth-metrics-for-school-accountability/
• Steven Wise, Megan Kuhfeld, John Cronin. (2022). Assessment in the time of COVID-19: Understanding patterns of student disengagement during remote low-stakes testing. Educational Assessment, 27(2), 136-151. https://doi.org/10.1080/10627197.2022.2087621
• Patrick Meyer, Ann Hu, Xueming (Sylvia) Li. (2023). Executive Summary: Content proximity spring 2022 pilot study. NWEA. https://www.nwea.org/research/publication/executive-summary-content-proximity-spring-2022-pilot-study/
• NWEA Research. (2021). Technical appendix for: Learning during COVID-19: Reading and math achievement in the 2020-2021 school year. Technical brief. https://www.nwea.org/research/publication/technical-appendix-for-learning-during-covid-19-reading-and-math-achievement-in-the-2020-2021-school-year/
• Shudong Wang, Marty McCall, Hong Jiao, & Gregg Harris. (2013). Construct Validity and Measurement Invariance of Computerized Adaptive Testing: Application to Measures of Academic Progress (MAP) Using Confirmatory Factor Analysis. Journal of Educational and Developmental Psychology, 3(1). https://doi.org/10.5539/jedp.v3n1p88
• Learning during COVID-19: Reading and math achievement in the 2020-2021 school year. (2021). NWEA Research. https://www.nwea.org/research/publication/learning-during-covid-19-reading-and-math-achievement-in-the-2020-2021-school-year/
• Kuhfeld, M., & Lewis, K. (2022). Student achievement in 2021-22: Cause for hope and continued urgency. NWEA Research brief. https://www.nwea.org/research/publication/student-achievement-in-2021-22-cause-for-hope-and-continued-urgency/
• James Soland. (2019). Are achievement gap estimates biased by differential student test effort? NWEA Research. https://www.nwea.org/research/publication/are-achievement-gap-estimates-biased-by-differential-student-test-effort/
• Sofia Postell, Emily Morton, Megan Kuhfeld. (2026). Chronic absenteeism: A continued impediment to academic recovery. NWEA research brief. https://www.nwea.org/research/publication/chronic-absenteeism-a-continued-impediment-to-academic-recovery/
• Wise, S. (2019). The Relationship Between Test-Taking Disengagement and Performance on MAP Growth Retests. (The Collaborative for Student Growth at NWEA Working Paper). https://www.nwea.org/research/publication/the-relationship-between-test-taking-disengagement-and-performance-on-map-growth-retests/
• Megan Kuhfeld, Karyn Lewis, Gustave Robinson. (2025). Boys regain the advantage in middle school STEM skills: Post-COVID trends in gender achievement gaps. NWEA Research Brief. https://www.nwea.org/research/publication/boys-regain-the-advantage-in-middle-school-stem-skills-post-covid-trends-in-gender-achievement-gaps/
• Meyer, J. P., & Dahlin, M. (2022). MAP Growth theory of action. NWEA. https://www.nwea.org/research/publication/map-growth-theory-of-action/
• Dallavis, J., Kuhfeld, M., Tarasawa, B., & Ponisciak, S. (2021). Achievement growth in K-8 Catholic schools using NWEA data. Journal of Catholic Education, 24(2). http://dx.doi.org/10.15365/joce.2402012021
• Ann Hu, NWEA Psychometric Solutions. (2021). MAP Growth Linking Studies: Intended Uses, Methodology, and Recent Studies. NWEA. https://www.nwea.org/resource/type/linking-studies/
• Megan Kuhfeld, Meredith Langi, Karyn Lewis. (2022). Technical appendix for: The widening achievement divide during COVID-19. NWEA Research. https://www.nwea.org/research/publication/technical-appendix-for-the-widening-achievement-divide-during-covid-19/
• Megan Kuhfeld, Karyn Lewis. (2022). Technical appendix for progress towards pandemic recovery: Continued signs of rebounding achievement at the start of the 2022-2023 school year. NWEA Research. https://www.nwea.org/research/publication/technical-appendix-for-progress-towards-pandemic-recovery-continued-signs-of-rebounding-achievement-at-the-start-of-the-2022-2023-school-year/
• Karyn Lewis, Megan Kuhfeld. (2024). Recovery still elusive: 2023-24 student achievement highlights persistent achievement gaps and a long road ahead. NWEA Research Technical Brief. https://www.nwea.org/research/publication/technical-brief-recovery-still-elusive-2023-24-student-achievement-highlights-persistent-achievement-gaps-and-a-long-road-ahead/
• Sofia Postell, Megan Kuhfeld, Susan Kowalski, Jazmin Isaacs. (2025). Hot test days, lower math scores: How heat affects student achievement. NWEA research brief. https://www.nwea.org/research/publication/hot-test-days-lower-math-scores-how-heat-affects-student-achievement/
• Megan Kuhfeld, Thurston Domina, Paul Hanselman. (2019). Validating the SEDA measures of district educational opportunities via a common assessment. AERA Open 5(2). https://www.nwea.org/research/publication/validating-the-seda-measures-of-district-educational-opportunities-via-a-common-assessment/
• Karyn Lewis, Megan Kuhfeld. (2024). Recovery still elusive: 2023-24 student achievement highlights persistent achievement gaps and a long road ahead. NWEA Research. https://www.nwea.org/research/publication/recovery-still-elusive-2023-24-student-achievement-highlights-persistent-achievement-gaps-and-a-long-road-ahead/
• Dennis Condron, Douglas Downery, Megan Kuhfeld. (2021). Schools as refractors: Change in variance in children's cognitive skills change while in school versus out. Sociology of Education, 94(4), 316-340. https://journals.sagepub.com/home/soe
• Andy Hegedus, Ed.D. (2018). Evaluating the Relationships between Poverty and School Performance. NWEA Research Report. https://www.nwea.org/research-data-galleries/poverty-and-school-performance
• J. Patrick Meyer, Ann Hu, Sylvia Li. (2023). Content Proximity Spring 2022 Pilot Study Research Brief. Psychometric Solutions, NWEA. https://www.nwea.org/research/publication/content-proximity-spring-2022-pilot-study-research-report/
• MAP Growth Technical Report for 2024–2025. (2026). HMH Education Company. https://www.nwea.org/research/publication/map-growth-technical-report/
• Wang, S., Jiao, H. and Zhang, L. (2013) 'Validation of longitudinal achievement constructs of vertically scaled computerised adaptive tests: a multiple-indicator, latent-growth modelling approach', Int. J. Quantitative Research in Education, Vol. 1, No. 4, pp.383–407. https://www.nwea.org/research/publication/validation-of-longitudinal-achievement-constructs-of-vertically-scaled-computerised-adaptive-tests-a-multiple-indicator-latent-growth-modelling-approach/. doi:10.1504/ijqre.2013.058307
• Technical appendix for: Learning during COVID-19: Initial findings on students' reading and math achievement and growth. (2020). NWEA Research. https://www.nwea.org/research/publication/technical-appendix-for-learning-during-covid-19-initial-findings-on-students-reading-and-math-achievement-and-growth/
• Karyn Lewis, Megan Kuhfeld. (2021). Learning during COVID-19: An update on student achievement and growth at the start of the 2021-22 school year. NWEA Research brief. https://www.nwea.org/research/publication/learning-during-covid-19-an-update-on-student-achievement-and-growth-at-the-start-of-the-2021-22-school-year/
• Beth Boroson, Elizabeth Barker, Xueming Li. (2017). A longitudinal study of reading growth for students with visual impairments. Journal on Technology and Persons with Disabilities 5, 158-171. https://www.nwea.org/research/publication/a-longitudinal-study-of-reading-growth-for-students-with-visual-impairments/
• Karyn Lewis, Megan Kuhfeld. (2022). Progress towards pandemic recovery: Continued signs of rebounding achievement at the start of the 2022-23 school year. NWEA Research brief. https://www.nwea.org/research-publications/
• Megan Kuhfeld, Daniel Long, Scott J. Peters. (2026). Getting Algebra Placement Right: Expanding Opportunity and Strengthening Preparation. NWEA Research. https://www.nwea.org/research/publication/getting-algebra-placement-right-expanding-opportunity-and-strengthening-preparation/
• Boroson, Beth; Barker, Elizabeth; Li, Xueming (Sylvia). (2017). A longitudinal study of reading growth for students with visual impairments. Journal on Technology and Persons with Disabilities, 6, 158–171. https://www.nwea.org/research/publication/a-longitudinal-study-of-reading-growth-for-students-with-visual-impairments-2/
• Johnson, A., Kuhfeld, M., & Soland, J. (2022). The forgotten 20 percent: Achievement and growth in rural schools across the nation. NWEA Research Brief. https://www.nwea.org/research-publication/the-forgotten-20-percent-achievement-and-growth-in-rural-schools-across-the-nation/
• Yun Xiang, Michael Dahlin, John Cronin, Robert Theaker, Sarah Durant. (2013). Do high flyers maintain their altitude? Performance trends of top students. NWEA Research white paper, Thomas B. Fordham Institute. https://www.nwea.org/research/publication/do-high-flyers-maintain-their-altitude-performance-trends-of-top-students/
• Stephanie Cawthon, Johny Daniel, North Cooc, Ana Vielma. (2022). Longitudinal models of reading and mathematics achievement in deaf and hard of hearing students. The Journal of Deaf Studies and Deaf Education. https://doi.org/10.1093/deafed/enac033
• Jonathan Schweig, Megan Kuhfeld, Andrew McEachin, Melissa Diliberti, Louis Mariano. (2022). Changes in school composition during the COVID-19 pandemic: Implications for school-average interim test score use. NWEA Research. https://www.nwea.org/research/publication/changes-in-school-composition-during-the-covid-19-pandemic-implications-for-school-average-interim-test-score-use/
• Wang, S., & Li, S. (2020). Calibration of Spanish MAP Growth math tests. Portland, OR: NWEA. https://www.nwea.org/research/publication/calibration-of-spanish-map-growth-math-tests/
• Megan Kuhfeld, Erik Ruzek, Karyn Lewis, Jim Soland, Angela Johnson, Beth Tarasawa, & Lindsay Dworkin. (2021). Understanding the initial educational impacts of COVID-19 on communities of color. NWEA Research Report. https://www.nwea.org/research/publication/understanding-the-initial-educational-impacts-of-covid-19-on-communities-of-color/

Page metadata

Field	Value
type	product
id	map-growth
aliases	nwea-map-growth
title	MAP Growth
description	MAP Growth is NWEA's computer-adaptive assessment system, with national achievement and growth norms and reporting tools such as the MAP Growth National Dashboard.
status	review
moved_from	element/2020-map-growth-status-and-growth-norms, element/2025-map-growth-norms-eisa-update, element/adaptive-interim-assessment-engagement-feature-element, element/content-proximity-item-selection-algorithm, element/learning-during-covid-technical-appendix, element/map-cat-assessment-system-element, element/map-cat-reading-mathematics-tests, element/map-growth-5-5-million-students-2020-21-dataset, element/map-growth-8-3-million-students-pandemic-dataset, element/map-growth-assessment-element, element/map-growth-assessment-nwea, element/map-growth-assessment-pandemic-recovery-data, element/map-growth-covid-research-data-source, element/map-growth-data-2000-districts-absenteeism-brief, element/map-growth-disengagement-levels-invalidation-criterion, element/map-growth-gender-gap-analysis-source, element/map-growth-interim-assessment, element/map-growth-interim-cat-assessment, element/map-growth-k8-sector-comparison-element, element/map-growth-linking-study-report, element/map-growth-national-dashboard-data-infrastructure, element/map-growth-national-dashboard-data-source, element/map-growth-pandemic-recovery-data, element/map-growth-retest-dataset-2017-2018, element/map-growth-scores-heat-study-outcome, element/map-growth-vertically-scaled-comparison-measure, element/nwea-2023-24-achievement-recovery-brief, element/nwea-chronic-absenteeism-recovery-brief, element/nwea-map-growth-seasonal-inequality-data, element/sci-and-map-growth-school-metrics, element/technical-appendix-widening-achievement-divide-covid, theory/arlis-reward-listed-item-selection, theory/map-growth-theory-of-action-comprehensive-assessment-system, theory/rit-vertical-equal-interval-scale
product_kind	assessment
generated	by process:sweep-kinds on 2026-10-10
trust	unverified — no one has confirmed this page's content
sources	36 — thum-2020, hashim-2026, steven-wise-2022, patrick-meyer-2023, nwea-research-2021, shudong-wang-2013, learning-during-covid-19-reading-and-math-achievement-in-the-2020-2021-school-year-2021, kuhfeld-2022, james-soland-2019, sofia-postell-2026, wise-2019, megan-kuhfeld-2025 … and 24 more

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