IXL Universal Reading Comprehension Screener
Reading

Descriptive Information

Please provide a description of your tool:

The IXL Universal Reading Comprehension Screener is a brief, accurate assessment designed specifically for identifying students from third through eighth grade who are experiencing moderate or severe reading difficulties. It may be administered up to five times per academic year, with performance expectations automatically adjusting for the beginning, middle, and end of the academic year. By scheduling the first administration early in the academic year, educators can promptly identify students with moderate or severe difficulties. Results are available immediately following the administration so that intervention resources can be deployed quickly to the students who need them. Results are also available by strand, providing insight into the specific areas where each student is struggling. The IXL Universal Reading Comprehension Screener is designed to be appropriate for students with diverse backgrounds (e.g., race, ethnicity, culture, and gender) and levels of ability. It also supports assistive technologies to provide equal accessibility for all students. These include screen reader compatibility, audio support, keyboard shortcuts, browser zoom up to 200 percent, and high contrast ratios. More broadly, the Reading Comprehension Screener is highly adaptive, which allows it to quickly adjust and meet students' diverse abilities and proficiency levels.

The tool is intended for use with the following grade(s).

Preschool / Pre - kindergarten
Kindergarten
First grade
Second grade
Third grade
Fourth grade
Fifth grade
Sixth grade
Seventh grade
Eighth grade
Ninth grade
Tenth grade
Eleventh grade
Twelfth grade

The tool is intended for use with the following age(s).

0-4 years old
5 years old
6 years old
7 years old
8 years old
9 years old
10 years old
11 years old
12 years old
13 years old
14 years old
15 years old
16 years old
17 years old
18 years old

The tool is intended for use with the following student populations.

Students in general education
Students with disabilities
English language learners

ACADEMIC ONLY: What skills does the tool screen?

Reading

Phonological processing:

RAN
Memory
Awareness
Letter sound correspondence
Phonics
Structural analysis

Word ID

Accuracy
Speed

Nonword

Accuracy
Speed

Spelling

Accuracy
Speed

Passage

Accuracy
Speed

Reading comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Other (please describe):

Listening comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Vocabulary
Expressive
Receptive

Mathematics

Global Indicator of Math Competence

Accuracy
Speed
Multiple Choice
Constructed Response

Early Numeracy

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Concepts

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Computation

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematic Application

Accuracy
Speed
Multiple Choice
Constructed Response

Fractions/Decimals

Accuracy
Speed
Multiple Choice
Constructed Response

Algebra

Accuracy
Speed
Multiple Choice
Constructed Response

Geometry

Accuracy
Speed
Multiple Choice
Constructed Response

Other (please describe):

Please describe specific domain, skills or subtests:

BEHAVIOR ONLY: Which category of behaviors does your tool target?

Internalizing
Externalizing
Internalizing and Externalizing

BEHAVIOR ONLY: Please identify which broad domain(s)/construct(s) are measured by your tool and define each sub-domain or sub-construct.

Acquisition and Cost Information

Where to obtain:

Email Address

orders@ixl.com

Address

777 Mariners Island Blvd., Suite 600, San Mateo, CA 94404

Phone Number

855-255-8800

Website

www.ixl.com/membership/quote

Initial cost for implementing program:

Cost

$14.00

Unit of cost

annual student license, covering up to five screens per school year per purchase

Replacement cost per unit for subsequent use:

Cost

$14.00

Unit of cost

annual student license

Duration of license

one school year, covering up to five screens per school year per purchase

Additional cost information:

Describe basic pricing plan and structure of the tool. Provide information on what is included in the published tool, as well as what is not included but required for implementation.

Access to IXL ELA is priced on an annual per student basis, and includes access to the IXL Universal Reading Comprehension Screener. Each student license purchase covers up to five screens per school year for the student. Training is priced separately.

Provide information about special accommodations for students with disabilities.

The IXL Universal Reading Comprehension Screener supports assistive technologies, including screen reader compatibility, audio support, keyboard shortcuts, browser zoom up to 200 percent, and high contrast ratios. More broadly, the Reading Comprehension Screener is highly adaptive, which allows it to quickly adjust and meet students' diverse abilities and proficiency levels.

Administration

BEHAVIOR ONLY: What type of administrator is your tool designed for?

General education teacher
Special education teacher
Parent
Child
External observer
Other

If other, please specify:

What is the administration setting?

Direct observation
Rating scale
Checklist
Performance measure
Questionnaire
Direct: Computerized
One-to-one
Other

If other, please specify:

Does the tool require technology?

Yes

If yes, what technology is required to implement your tool? (Select all that apply)

Computer or tablet
Internet connection
Other technology (please specify)

If your program requires additional technology not listed above, please describe the required technology and the extent to which it is combined with teacher small-group instruction/intervention:

The IXL Universal Reading Comprehension Screener is fully web-based, and requires a computer, tablet, or phone with a connection to the Internet. The Screener can be accessed via popular web browsers such as Chrome, Safari, or Edge.

What is the administration context?

Individual
Small group   If small group, n=
Large group   If large group, n=
Computer-administered
Other

If other, please specify:

What is the administration time?

Time in minutes

20

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

per (student/group/other unit)

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

The assessment is ended for a student when one of the four stopping rule components has been met. The four components of the stopping rule are (1) overall precision, (2) confidence in classification, (3) maximum number of items, and (4) maximum amount of time. There is a 30 minute time limit for completion of the Universal Screener.

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

The IXL Universal Reading Comprehension Screener is typically administered in three distinct windows (beginning-of-year, middle-of-year, and end-of-year). It may be administered up to five times per academic year.

If yes, for which months are benchmarks available?

Benchmarks are set relative to the beginning (August 1 – November 30), middle (December 1 – February 28), and end (March 1 – June 1) of the school year.

BEHAVIOR ONLY: Can students be rated concurrently by one administrator?

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

No

Describe the time required for administrator training, if applicable:

Administrator training for the Universal Screener is optional, though highly recommended. Administrator training is offered as virtual sessions.

Please describe the minimum qualifications an administrator must possess.

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

Yes

Are training manuals/materials included in cost of tools?

Yes

If No, please describe training costs:

Administrators and teachers can refer to online resources including the IXL Universal Reading Comprehension Screener Technical Manual (https://www.ixl.com/materials/us/IXL_Reading_Comprehension_Screener_technical_manual.pdf), Teacher Implementation Guide (https://www.ixl.com/materials/i_guides/Teacher_Guide_Universal_Reading_Screeners.pdf), and Administrator Implementation Guide (https://www.ixl.com/materials/i_guides/Admin_Guide_IXL_Universal_Reading_Screeners.pdf) for guidance on administration and use of the Screener. These are available at no additional cost. Live virtual administrator training is available as an additional purchase. These are offered at $695 for up to 50 attendees and $1095 for large audiences (50-200 attendees).

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Administrators and teachers can refer to online resources including the IXL Universal Reading Comprehension Screener Technical Manual, Teacher Implementation Guide, and Administrator Implementation Guide for guidance on administration and use of the Screener. Users can also refer to IXL’s online help center for additional user guides and answers to frequently asked questions at www.ixl.com/help-center. IXL offers technical support via phone (855-255-6676) from 7AM to 7PM, Monday to Friday. Users may also contact IXL via email (help@ixl.com). Other than closure for major holidays, IXL’s attentive staff will respond to inquiries within one business day.

Scoring

How are scores calculated?

Manually (by hand)
Automatically (computer-scored)
Other

If other, please specify:

Do you provide basis for calculating performance level scores?

Yes

What is the basis for calculating performance level and percentile scores?

Age norms
Grade norms
Classwide norms
Schoolwide norms
Stanines
Normal curve equivalents

What types of performance level scores are available?

Raw score
Standard score
Percentile score
Grade equivalents
IRT-based score
Age equivalents
Stanines
Normal curve equivalents
Developmental benchmarks
Developmental cut points
Equated
Probability
Lexile score
Error analysis
Composite scores
Subscale/subtest scores
Other

If other, please specify:

After students complete the screener, administrators and teachers have access to key data for planning instruction: nationally-normed percentiles and overall performance levels, as well as percentiles and levels in each strand.

Does your tool include decision rules?

No

If yes, please describe.

We provide administrators with a table linking classifications of students into proficiency levels and national percentile ranks. Classification at a given time of the year provides a criterion-referenced inference regarding a student’s reading ability at that time relative to standards-based expectations for their grade level. Percentile ranks provide a norm-referenced inference of a student’s performance relative to their peers; specifically, percentile ranks indicate the percentage of scores that fall below a specific score. This table serves the following three purposes: (1) it provides educators with a high-level breakdown of student performance nationwide; (2) it allows for the differentiation of students who received the same classification; and (3) educators can use the percentile ranges associated with the Far below grade level, as well as school and individual student contexts, to identify appropriate cutoffs for Tier III interventions within a Response-to-Intervention/Multi-Tiered System of Supports (RTI/MTSS) framework.

Can you provide evidence in support of multiple decision rules?

No

If yes, please describe.

Please describe the scoring structure. Provide relevant details such as the scoring format, the number of items overall, the number of items per subscale, what the cluster/composite score comprises, and how raw scores are calculated.

Item development for the IXL Universal Reading Comprehension Screener was not constrained to a single item type, such as multiple-choice questions. Instead, items were developed using the most appropriate item type for assessing the target knowledge, skill, or ability. The screener covers two strands: (1) Literary Texts, and (2) Informational Texts. After students complete the screener, administrators and teachers have access to key data for planning instruction: nationally-normed percentiles and overall performance levels, as well as percentiles and levels in each strand. Nationally-normed percentiles provide information about the typical levels of performance for an identifiable population of students or schools. For example, a student may achieve the highest test score in her class on a given assessment but still fall below the national average of students at her grade level who have completed the same assessment (i.e., a percentile rank < 50). This information allows educators to compare their students’ scores to the scores of students across the United States who completed the same assessment. National norms for the IXL Universal Reading Comprehension screener may be found in its Technical Manual, https://www.ixl.com/materials/us/IXL_Reading_Comprehension_Screener_technical_manual.pdf). The Reading Comprehension Screener also classifies students as performing far below grade level, below grade level, or on or above grade level. In addition, achievement levels are provided for each of the two strands (i.e., far below/below/on or above grade level in the Literary Texts strand and in the Informational Texts strand). Due to the adaptive nature of the IXL Universal Reading Comprehension Screener, the total number of items delivered to students varies based on several factors. However, lower and upper bounds on the number of items from any content strand are set based on the student grade and administration window (i.e., beginning, middle, or end of year). Though not included here, the test constraints associated with each time of year are published in the IXL Universal Reading Comprehension Screener Technical Manual. As a student progresses through the assessment, the algorithm updates its estimate of ability and the standard error of this ability estimate each time the student responds to an item using a conventional Bayesian expected a posteriori (EAP) estimation method. As the algorithm becomes more certain in its running estimate of student ability (as indicated by the decreasing standard error of the estimate), it selects items more suitable to the student’s ability estimate. That is, the algorithm chooses more difficult items as the student answers correctly and less challenging items as the student answers incorrectly. This adaptivity reduces the number of items needed to measure reading ability, thus increasing test efficiency and improving the test experience.

Describe the tool’s approach to screening, samples (if applicable), and/or test format, including steps taken to ensure that it is appropriate for use with culturally and linguistically diverse populations and students with disabilities.

The IXL Universal Reading Comprehension Screener is a brief, accurate assessment designed specifically for identifying students from third through eighth grade who are experiencing moderate or severe reading difficulties. It may be administered up to five times per academic year, with performance expectations automatically adjusting for the beginning, middle, and end of the academic year. By scheduling the first administration early in the academic year, educators can promptly identify students with moderate or severe difficulties. Results are available immediately following the administration so that intervention resources can be deployed quickly to the students who need them. Results are also available by strand, providing insight into the specific areas where each student is struggling. Content for the screener was developed using a principled assessment design framework. Principled assessment design is a general framework for designing, developing, and implementing an assessment to support the ongoing accumulation and synthesis of evidence to support the validity claims made by the assessment (e.g., Ferrara et al., 2016). This framework requires assessment targets to be clearly defined at the beginning of the process. These assessment targets drive the entire assessment development plan, and continuous focus on targets ensures that all subsequent decisions are consistent with providing evidence to support validity claims. The assessment targets for the Reading Comprehension Screener are achievement level descriptors (ALDs) derived from all 50 states’ standards, including the Common Core. Achievement level descriptors are statements that describe expectations of what students at specific achievement levels should know and be able to do. As the IXL Universal Reading Comprehension Screener is designed to identify students who are experiencing moderate or severe difficulties in reading, the achievement level was conceptualized as the threshold between on-grade achievement and below-grade achievement. Therefore, the ALDs were written for each educational standard to describe what a minimally competent student would know and be able to do. An external panel of experienced educators was recruited to review sample items alongside the evaluation of ALDs described previously. The SME panel consisted of in-service educators with significant experience teaching or supervising the grade level(s) they evaluated, and with demographics matching that of elementary and middle school teachers in the U.S. Multiple rounds of internal review were then conducted to ensure all items appropriately targeted the intended achievement level descriptors while meeting the content and item writing specifications. The format of the Reading Comprehension Screener includes selected-response questions for which a student chooses an answer from a provided set of options. Where appropriate, the writers of these items applied the principles of Universal Design (Thompson et al., 2002) to reduce construct-irrelevant variance while simultaneously increasing accessibility. Items were written to target achievement level descriptors while considering important factors such as reading level, cognitive load, vocabulary, and sentence length, among others. The IXL Universal Reading Comprehension Screener is designed to be appropriate for students with diverse backgrounds (e.g., race, ethnicity, culture, and gender) and levels of ability. It also supports assistive technologies to provide equal accessibility for all students. These include screen reader compatibility, audio support, keyboard shortcuts, browser zoom up to 200 percent, and high contrast ratios. More broadly, the Reading Comprehension Screener is highly adaptive, which allows it to quickly adjust and meet students' diverse abilities and proficiency levels.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

NWEA MAP Growth

Classification Accuracy

Select time of year

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

The criterion measure is the NWEA MAP® Growth™ assessment in reading (MAP Growth), a computer-adaptive assessment of reading ability. The MAP Growth assessment and the IXL Universal Reading Comprehension Screener are independent assessments developed by different organizations.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

For the fall classification accuracy analyses, both measures were administered in the beginning of the school year (2024-25 and 2025-26). The IXL Universal Reading Comprehension Screener was administered between October 11 and November 26, 2024 or between August 11 and November 30, 2025. The MAP Growth reading assessment was administered between August 26 and November 29, 2024 or between September 12 and November 25, 2025. As these two measurements were taken in close temporal proximity, concurrent validity analyses were conducted. For the winter classification accuracy analyses, both measures were administered in the middle of the school year (2024-25 and 2025-26). The IXL Screener was administered between December 2, 2024 and February 28, 2025 or between December 3, 2025 and January 26, 2026. The MAP Growth reading assessment was administered between December 1, 2024 and February 28, 2025 or between December 14, 2025 and January 29, 2026. As these two measurements were taken in close temporal proximity, concurrent validity analyses were conducted.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Cut points on the MAP Growth reading assessment were determined using the 2025 nationwide MAP Growth norms for student achievement. In line with NCII TRC guidance, students with RIT scores below the 20th percentile were considered “at risk,” while those with RIT scores at or above the 20th percentile were considered “not at risk.” Cut points on the screening measure were identified as theta values that maximized classification accuracy with the criterion measure. Using these values, students were classified as “at risk” if they scored below the cut point and “not at risk” if they scored at or above the cut point. Note that in the classification accuracy analyses, the sample for each grade at each time point contains more than 150 students from the West, Midwest, South, and Northeast regions of the U.S. Census Bureau and the following divisions: West North Central (MO), East North Central (MI, OH, IL), West South Central (TX), New England (ME), South Atlantic (DC, MD), East South Central (KY), and Pacific (CA).

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

Some students may have been involved in various interventions in their particular schools, but we do not know which interventions or which students.

Cross-Validation

Has a cross-validation study been conducted?

No

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the cross-validation analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Classification Accuracy - Fall

Evidence	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	-1.6	-1.5	-1.4	-1.2	-0.7	-0.6
Classification Data - True Positive (a)	104	160	128	146	69	60
Classification Data - False Positive (b)	163	173	168	140	168	139
Classification Data - False Negative (c)	41	38	36	27	27	28
Classification Data - True Negative (d)	475	502	516	451	581	638
Area Under the Curve (AUC)	0.73	0.77	0.77	0.80	0.75	0.75
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.69	0.74	0.73	0.77	0.70	0.70
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.77	0.81	0.80	0.84	0.79	0.80

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.19	0.23	0.19	0.23	0.11	0.10
Overall Classification Rate	0.74	0.76	0.76	0.78	0.77	0.81
Sensitivity	0.72	0.81	0.78	0.84	0.72	0.68
Specificity	0.74	0.74	0.75	0.76	0.78	0.82
False Positive Rate	0.26	0.26	0.25	0.24	0.22	0.18
False Negative Rate	0.28	0.19	0.22	0.16	0.28	0.32
Positive Predictive Power	0.39	0.48	0.43	0.51	0.29	0.30
Negative Predictive Power	0.92	0.93	0.93	0.94	0.96	0.96

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Fall 2024; Fall 2025	Fall 2024; Fall 2025	Fall 2024; Fall 2025	Fall 2024; Fall 2025	Fall 2024; Fall 2025	Fall 2024; Fall 2025
Sample Size	783	873	848	764	845	865
Geographic Representation	East South Central (KY) New England (ME) Pacific (CA) South Atlantic (DC, MD) West North Central (MO) West South Central (TX)	East South Central (KY) New England (ME) Pacific (CA) South Atlantic (DC, MD) West North Central (MO) West South Central (TX)	East South Central (KY) New England (ME) Pacific (CA) South Atlantic (DC, MD) West North Central (MO) West South Central (TX)	East South Central (KY) New England (ME) Pacific (CA) South Atlantic (DC, MD) West North Central (MO)	East South Central (KY) New England (ME) Pacific (CA) South Atlantic (DC, MD) West North Central (MO)	East South Central (KY) New England (ME) Pacific (CA) South Atlantic (DC, MD) West North Central (MO)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth	NWEA MAP Growth
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	-1.3	-1.6	-1.4	-1.2	-0.9	-0.5
Classification Data - True Positive (a)	202	112	334	76	114	123
Classification Data - False Positive (b)	73	161	276	64	103	67
Classification Data - False Negative (c)	38	19	106	23	48	43
Classification Data - True Negative (d)	409	633	984	327	278	340
Area Under the Curve (AUC)	0.85	0.83	0.77	0.80	0.72	0.79
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.82	0.79	0.75	0.76	0.67	0.75
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.87	0.86	0.79	0.85	0.76	0.83

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.33	0.14	0.26	0.20	0.30	0.29
Overall Classification Rate	0.85	0.81	0.78	0.82	0.72	0.81
Sensitivity	0.84	0.85	0.76	0.77	0.70	0.74
Specificity	0.85	0.80	0.78	0.84	0.73	0.84
False Positive Rate	0.15	0.20	0.22	0.16	0.27	0.16
False Negative Rate	0.16	0.15	0.24	0.23	0.30	0.26
Positive Predictive Power	0.73	0.41	0.55	0.54	0.53	0.65
Negative Predictive Power	0.91	0.97	0.90	0.93	0.85	0.89

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Winter 2024-25; Winter 2025-26	Winter 2024-25; Winter 2025-26	Winter 2024-25; Winter 2025-26	Winter 2024-25; Winter 2025-26	Winter 2024-25; Winter 2025-26	Winter 2024-25; Winter 2025-26
Sample Size	722	925	1700	490	543	573
Geographic Representation	East North Central (IL, MI, OH) New England (ME) Pacific (CA) South Atlantic (FL, GA, MD) West South Central (TX)	East North Central (IL, MI, OH) New England (ME) Pacific (CA) South Atlantic (FL, GA, MD) West South Central (TX)	East North Central (IL, MI, OH) New England (ME) Pacific (CA) South Atlantic (FL, GA, MD) West South Central (TX)	East North Central (IL, MI, OH) New England (ME) Pacific (CA) South Atlantic (FL, GA, MD) West South Central (TX)	East North Central (IL, MI, OH) New England (ME) Pacific (CA) South Atlantic (FL, GA, MD) West South Central (TX)	East North Central (IL, MI, OH) New England (ME) Pacific (CA) South Atlantic (FL, GA, MD) West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating

*Offer a justification for each type of reliability reported, given the type and purpose of the tool.

Often, the reliability of a traditional, fixed-form assessment is evaluated via internal consistency measures such as Cronbach’s alpha or McDonald’s omega. However, in a CAT assessment context, these measures are not appropriate. Below, we report two types of reliability that are appropriate for CAT assessments: marginal reliability and standard error of measurement (SEM; including accompanying plots). Marginal Reliability is reported in the table further below. Standard Error of Measurement: A student’s standard error of measurement (SEM) is an indicator of the precision of their score and describes the range in which a score may vary upon repeated testing that is due to chance. SEM is a function of the interaction between the ability of a student, the difficulty of the items, and the number of items on a test. A lower SEM indicates less error and more precision around a score. Because the CAT algorithm selects items based on a student’s estimated ability level, it is able to target a student more accurately, and significantly decrease the SEM with fewer items than a traditional fixed-form assessment. Although the CAT algorithm targets students more accurately according to their ability, it generally performs better for students whose ability and performance are closer to the typical ability of students at or near their grade level. As seen in the attached plots (available from the Center upon request), the SEM is lowest for grade 3 students whose ability is between -1.5 and -1.0, grade 4 students whose ability is between -1.0 and -0.5, grade 5 students whose ability is between -0.5 and 0, grade 6 students whose ability is between 0.0 and 0.5, grade 7 students whose ability is around 0.5, and grade 8 students whose ability is between 0.5 and 1.0.

*Describe the sample(s), including size and characteristics, for each reliability analysis conducted.

The sample for calculating the marginal reliability and SEM included student records from the 2024-2025 school year. The sample included students in all nine U.S. Census Bureau divisions. Sample sizes by grade are presented in the table below. As illustrated in the attached histogram plots (available from the Center upon request), this sample included students across all performance levels.

*Describe the analysis procedures for each reported type of reliability.

Marginal reliability: Although traditional measures of reliability are not estimable for adaptive assessments, marginal reliability provides a method that closely approximates the traditional measures of internal consistency when the ability distribution and item parameters are known (see Dimitrov, 2003; Samejima, 1977, 1994). SEM: As mentioned, the IXL Universal Reading Comprehension Screener uses expected a-posteriori (EAP) and maximum likelihood (ML; in final scoring) to estimate student ability and SEM to indicate the level of certainty or reliability of this estimate. It derives the SEM by calculating the standard deviation of the posterior distribution of the EAP estimate by integrating over all possible values of ability given a response pattern (see Bock & Mislevy, 1982). Although not summarized here, the Technical Manual for the Reading Comprehension Screener (https://www.ixl.com/materials/us/IXL_Reading_Comprehension_Screener_technical_manual.pdf) includes additional reliability information, including test-retest reliability and classification consistency.

*In the table(s) below, report the results of the reliability analyses described above (e.g., internal consistency or inter-rater reliability coefficients).

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Standard error of measurement (SEM) describes the range in which a score may vary upon repeated testing that is due to chance. It is inversely related to marginal reliability, with lower SEM values indicating higher precision. In this sample, median SEM coefficients ranged from 0.519 in grade 3 to 0.653 in grade 7, indicating low error and high precision around students’ scores on the IXL Universal Reading Comprehension Screener across grades 3-8. The attached plots (available from the Center upon request) display SEM for each grade in the analysis.

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Do you have reliability data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

If yes, fill in data for each subgroup with disaggregated reliability data.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Manual cites other published reliability studies:

Provide citations for additional published studies.

Validity

Grade	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating

*Describe each criterion measure used and explain why each measure is appropriate, given the type and purpose of the tool.

The IXL Universal Reading Comprehension Screener Technical Manual provides validity evidence based on test content (i.e., subject-matter expert review), internal structure (i.e., unidimensionality and DIF), and relations to other variables. (For more information, see the Technical Manual here: https://www.ixl.com/materials/us/IXL_Reading_Comprehension_Screener_technical_manual.pdf). The criterion measure was the NWEA MAP® Growth™ reading assessment (MAP Growth), a widely-used computer-adaptive assessment of reading ability. The MAP Growth reading assessment and the IXL Universal Reading Comprehension Screener are independent assessments developed by different organizations. While the two assessments were developed separately, they are expected to be related as the underlying construct being measured is the same, i.e., students’ reading ability.

*Describe the sample(s), including size and characteristics, for each validity analysis conducted.

For the concurrent validity analyses, the sample included students who completed both the IXL Screener and MAP Growth during the same timeframe of the 2024-25 or 2025-26 school year (e.g., for Fall/Fall analyses, both measures were completed between August and November of the same school year). The sample size for the concurrent validity analyses ranged from 112 (Grade 3) to 846 (Grade 5). For the predictive validity analyses, the sample included students who completed the IXL Screener during the beginning-of-year time frame (August to November) and the MAP Growth assessment during the middle-of-year time frame (December to February). The sample size for the predictive validity analyses ranged from 492 (Grade 3) to 805 (Grade 4). The sample represented students across all performance levels and across five of the nine U.S. Census Bureau geographical divisions: Pacific (CA), New England (ME), West North Central (MO), East South Central (KY), and South Atlantic (MD, DC).

*Describe the analysis procedures for each reported type of validity.

Pearson product-moment correlation analyses were conducted between IXL Universal Reading Comprehension Screener theta values (i.e., continuous measures of student reading ability) and students’ scores on the outcome measure (MAP Growth RIT scores in reading). Confidence intervals (95%) were calculated using Fisher’s r-to-z transformation. Concurrent analyses examined the relationship between students’ scores on the IXL Screener and their concurrent MAP Growth reading RIT scores, while predictive analyses examined the relationship between students’ scores on the IXL Screener and their subsequent MAP Growth reading RIT scores. For both types of validity, a positive correlation between the two variables indicates that students who have higher reading ability as measured by the IXL Screener also have higher reading ability as measured by the criterion assessment.

*In the table below, report the results of the validity analyses described above (e.g., concurrent or predictive validity, evidence based on response processes, evidence based on internal structure, evidence based on relations to other variables, and/or evidence based on consequences of testing), and the criterion measures.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of validity analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Describe the degree to which the provided data support the validity of the tool.

In each grade, there is evidence for strong predictive validity of the IXL Universal Reading Comprehension Screener. Coefficients ranged from .68 to .77, reflecting positive relationships between students’ ability as measured by the IXL Screener and the widely-used MAP Growth assessment. This was true in both the concurrent and predictive validity analyses. These results illustrate a strong relationship between the two assessments.

Do you have validity data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

If yes, fill in data for each subgroup with disaggregated validity data.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of validity analysis not compatible with above table format:

Manual cites other published reliability studies:

Provide citations for additional published studies.

Bias Analysis

Grade	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	Provided	Provided	Provided	Provided	Provided	Provided

Have you conducted additional analyses related to the extent to which your tool is or is not biased against subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)? Examples might include Differential Item Functioning (DIF) or invariance testing in multiple-group confirmatory factor models.

Yes

If yes,

a. Describe the method used to determine the presence or absence of bias:

Differential item functioning (DIF) analysis investigates each item for signs of interactions with sample characteristics. An item is said to exhibit DIF when equally able individuals from different groups have notably differing probabilities of answering the item correctly. DIF detection procedures help to gather validity evidence for the proposed interpretations of test scores by ensuring that scores are free from potential bias and that individual items do not create an advantage for one group over another. To examine manifest DIF, we used the Rasch separate calibration t-test method. This method is based on the differences between two separate calibrations of the same item from the subpopulations of interest, holding the other item and person parameters constant to ensure scale stability (Wright & Stone, 1979). Determining the flagging criterion for the Rasch separate calibration t-test method typically involves setting the magnitude for the difference between the two calibrations and an appropriate p-value for significance. Wright and Douglas (1975) proposed a “half-logit” rule, where a difference in item difficulty between examinee subgroups of at least 0.5 logits warrants additional investigation. This critical value reflects a sizable portion of the typical range of item difficulty in operational tests (about -2.5 logits to about +2.5 logits); accordingly, a shift of 0.5 logits (10% of the scale) begins to affect the accuracy of the measurement. These criteria (a statistically-significant p-value and a shift of 0.5 logits) were used to assess manifest DIF in the IXL Universal Reading Comprehension Screener.

b. Describe the subgroups for which bias analyses were conducted:

Student gender (male vs. female) and student race/ethnicity (white vs. non-white).

c. Describe the results of the bias analyses conducted, including data and interpretative statements. Include magnitude of effect (if available) if bias has been identified.

When investigating DIF based on reported sex, out of several hundreds of items that received sufficient exposures from the relevant groups, we flagged 10 items in grade 3, 15 items in grade 4, 13 items in grade 5, 13 items in grade 6, 10 items in grade 7, and 15 items in grade 8 for potential DIF. When investigating DIF based on race/ethnicity, we flagged 1 item in grade 5, 6 items in grade 7, and 7 items in grade 8 for potential DIF. More importantly, all the flagged items were free from substantive DIF. It is important to distinguish between statistical DIF and substantive DIF (Penfield & Lam, 2000; Roussos & Stout, 1996). Statistical DIF refers to the statistical identification of DIF, whereas substantive DIF refers to the identification of construct-irrelevant factors responsible for the statistical DIF (i.e., potential sources of bias). It is always important to remember that statistical DIF is simply a detection strategy and that careful scrutiny of items is always warranted to identify and address substantive DIF. DIF detection methods may identify some items as being unbiased even though they are indeed biased, while some items identified as having bias may not actually be biased. Therefore, all items identified as potentially biased using the methods outlined above were reviewed by subject-matter experts to ensure that all items are free from substantive DIF.