Classworks Universal Screener
Reading

Descriptive Information

Please provide a description of your tool:

Classworks Universal Screeners are formal assessments used to measure readiness for grade level instruction, help identify baseline learning levels, and measure growth. The Universal Screeners were designed to screen students who may need additional intervention and can be used as part of the Response to Intervention (RtI) process. In addition to reporting an overall scaled score, the assessment reports a score for each domain and nationally normed percentile ranks. Key strands include a minimum of four test questions to provide a reasonable estimate of student strengths and weaknesses. The test includes multiple grade levels of content to allow sufficient reach for students who may be struggling. Additionally, based on assessment results, Classworks automatically delivers an individualized learning path based on a student's specific needs. Students engage with content appropriate for their instructional level, regardless of grade level.

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:

At-risk status for early literacy foundational skills and are indicated for students completing the K-3 screeners in English or Spanish. Additional dyslexia screening recommendations are also indicated.

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

hello@classworks.com

Address

2 N Nevada Ave, Ste 1200, Colorado Springs, CO, 80903-1702

Phone Number

770-325-5555

Website

www.curriculumadvantage.com

Initial cost for implementing program:

Cost

$8.00

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$0.00

Unit of cost

Duration of license

1 year

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.

Classworks Universal Screener may be purchased on its own or as part of a comprehensive solution that includes individualized instruction. Classworks MyProgress includes Universal Screeners for reading and math, progress monitoring tools for reading and math, and comprehensive reporting and data dashboards. Classworks MyInterventions includes all of the above plus evidence-based reading and math instruction for grades K-HS and AI-powered differentiated instruction with Wittly by ClassworksTM.

Provide information about special accommodations for students with disabilities.

The Classworks Universal Screener is designed to support the principles of Universal Design: to be fair, accessible, and appropriate for all students, including students with different abilities, disabilities, and backgrounds including race, ethnicity, gender, culture, language, age, and socioeconomic status. Features included in Classworks Assessment content are categorized as Universal or Designated. Universal features are available to all learners. Designated features are activated for specific students as part of a decision-making process. Classworks strongly recommends using a consistent process that ensures that all learners receive the appropriate level of support. Embedded Universal Features: Amplification (Audio amplification, increased volume, audio aids), Keyboard Navigation (keyboard shortcuts and two switch system, Zoom (item level). Non-embedded Features: Breaks (frequent breaks), English Dictionary, Noise Buffer (headphones, audio aids), Note Pad or Scratch Paper (blank paper), Spanish dictionary. Embedded Designated Feature: Oral Administration of Assessment Items (audio support, spoken audio). Non-embedded Designated Features: Bilingual dictionary (word-to-word dictionary in English and native language), Color contrast, Human reader (human read aloud), Magnification device (low-vision aids), Native language translation.

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:

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

30

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

0

per (student/group/other unit)

student

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

3

If yes, for which months are benchmarks available?

fall, winter, spring

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?

Yes

Describe the time required for administrator training, if applicable:

Less than 1 hour of training.

Please describe the minimum qualifications an administrator must possess.

The test can be administered by teachers, alternatively credited teachers, and paraprofessionals.

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:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Dedicated account manager. Classworks support is available online via in product chat, phone, and email.

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:

Strand level proficiency

Does your tool include decision rules?

No

If yes, please describe.

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.

Raw scores are calculated as the total number of items answered correctly on the screener. Performance on the screeners is reported as a scaled score on a vertical scale ranging from 200 to 800 spanning across grades K to 10.

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.

An item alignment review was conducted to ensure that the Universal Screener items align to the Classworks objectives and individual state standards for mathematics. Item content and bias reviews were conducted to ensure that the items selected for the Reading and Mathematics Universal Screeners were appropriate and reasonable for the purpose of screening students. Test specifications include a range of coverage including items at grade level, one grade below, and two grades below. This test design has been found to be effective for the purpose of screening. A Field test was conducted using a national sampling of students. The item level data was used to calibrate the items using the Rasch model. Items that did not fit the model or showed differential performance were edited or removed from the final forms.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

NWEA MAP Growth Assessment

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.

TThe NWEA MAP Growth assessment is the criterion used. This assessment is nationally normed and published by NWEA, completely independent of the Classworks assessment.

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.

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 for the Classworks scores were determined empirically. For each grade and season: - Students scoring within a small range around the 20th percentile on NWEA were identified. - The median of these students’ Classworks scores was calculated and set as the cut-point. - Students scoring below the cut-point on Classworks were classified as "at-risk," and those scoring above were classified as "not-at-risk." This methodology aligns with NCII guidelines for identifying students requiring intensive intervention.

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.

Cross-Validation

Has a cross-validation study been conducted?

Yes

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.

For each season and subject combination, we found metrics such as the true positive, false positive, false negative, true negative, ROC AUC curve, AUC lower bound, AUC upper bound, accuracy, sensitivity, specificity, false positive rate, false negative rate, positive predicting power, and negative predictive power. These values were obtained for each grade level within each of the k values tested. We looked at the accuracy metric, to determine which k values performed the best for each season and subject combination, and all the k values had reasonable performances.

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).

Cross-validation was implemented to assess the generalizability of the screener. Using K-Fold Cross-Validation (k = 3, 5, 10), the dataset was split into training and testing subsets. Key steps included: 1) Merging and cleaning the NWEA and Classworks datasets for each season and subject. 2) Organizing data into grade-level subsets. 3) Determining training data cut-points based on the classification methodology and applying them to the testing data. Metrics such as accuracy, ROC AUC, sensitivity, specificity, and confusion matrix components were calculated for each fold. Edge cases, such as folds with only one class present, were handled to ensure robust results. Non-numeric and null values were also addressed to avoid errors during calculation. Results from these analyses are available from the Center upon request.

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 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	154	154	162	180	179	183	187
Cut Points - Corresponding performance score (numeric) on screener measure	275	255	280	380	440	430	490
Classification Data - True Positive (a)	4	9	5	24	35	18	15
Classification Data - False Positive (b)	22	21	11	50	33	28	27
Classification Data - False Negative (c)	3	7	7	9	1	17	3
Classification Data - True Negative (d)	14	110	130	167	231	257	250
Area Under the Curve (AUC)	0.48	0.70	0.67	0.75	0.92	0.71	0.87
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.25	0.55	0.50	0.65	0.86	0.61	0.76
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.71	0.85	0.84	0.85	0.98	0.81	0.98

Statistics	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.16	0.11	0.08	0.13	0.12	0.11	0.06
Overall Classification Rate	0.42	0.81	0.88	0.76	0.89	0.86	0.90
Sensitivity	0.57	0.56	0.42	0.73	0.97	0.51	0.83
Specificity	0.39	0.84	0.92	0.77	0.88	0.90	0.90
False Positive Rate	0.61	0.16	0.08	0.23	0.13	0.10	0.10
False Negative Rate	0.43	0.44	0.58	0.27	0.03	0.49	0.17
Positive Predictive Power	0.15	0.30	0.31	0.32	0.51	0.39	0.36
Negative Predictive Power	0.82	0.94	0.95	0.95	1.00	0.94	0.99

Sample	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	08/01/2023/11/01/2023	08/01/2023/11/01/2023	08/01/2023/11/01/2023	08/01/2023/11/01/2023	08/01/2023/11/01/2023	08/01/2023/11/01/2023	08/01/2023-11/30/2023
Sample Size	43	147	153	250	300	320	295
Geographic Representation	East North Central (IN) Middle Atlantic (NJ) South Atlantic (DC, GA) West South Central (TX)	East North Central (IN) East South Central (KY) Pacific (CA) South Atlantic (DC, GA) West North Central (SD) West South Central (TX)	East North Central (IN) East South Central (KY) Middle Atlantic (NJ) South Atlantic (DC, GA) West North Central (SD) West South Central (TX)	East North Central (IN) East South Central (KY) Middle Atlantic (NJ) Pacific (CA) South Atlantic (DC, GA) West North Central (SD) West South Central (TX)	East South Central (AL, KY) Middle Atlantic (NJ) South Atlantic (DC, GA) West North Central (SD) West South Central (TX)	East South Central (AL, KY) Middle Atlantic (NJ) Pacific (CA) South Atlantic (GA) West North Central (SD) West South Central (TX)	East South Central (AL, KY) Middle Atlantic (NJ) South Atlantic (DC, GA) West North Central (SD) West South Central (TX)
Male	58.1%	61.2%	57.5%	56.0%	62.0%	65.0%	60.0%
Female	44.2%	38.8%	38.6%	44.0%	38.0%	35.0%	40.0%
Other
Gender Unknown
White, Non-Hispanic	2.3%	2.0%	2.6%	2.0%	4.0%	4.1%	3.4%
Black, Non-Hispanic	16.3%	19.0%	15.0%	18.0%	4.0%	4.1%	5.1%
Hispanic	4.7%	6.1%	5.9%	5.2%	5.0%	5.9%	5.1%
Asian/Pacific Islander				2.0%	1.0%
American Indian/Alaska Native						0.9%	2.0%
Other	4.7%	1.4%	2.0%	1.2%	1.0%	0.9%	1.0%
Race / Ethnicity Unknown	72.1%	72.8%	73.2%	73.2%	86.0%	84.1%	83.4%
Low SES						0.9%	4.1%
IEP or diagnosed disability	7.0%	8.2%	7.8%	8.0%	8.0%	8.1%	6.1%
English Language Learner						4.1%	2.0%

Classification Accuracy - Winter

Evidence	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	161	157	167	177	180	185	189
Cut Points - Corresponding performance score (numeric) on screener measure	310	330	300	280	380	440	470
Classification Data - True Positive (a)	5	8	8	2	15	15	7
Classification Data - False Positive (b)	18	35	6	1	17	23	16
Classification Data - False Negative (c)	1	2	4	2	5	5	6
Classification Data - True Negative (d)	15	73	82	69	78	166	113
Area Under the Curve (AUC)	0.64	0.74	0.80	0.74	0.79	0.81	0.70
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.39	0.56	0.64	0.46	0.66	0.70	0.54
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.90	0.92	0.95	1.00	0.91	0.93	0.87

Statistics	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.15	0.08	0.12	0.05	0.17	0.10	0.09
Overall Classification Rate	0.51	0.69	0.90	0.96	0.81	0.87	0.85
Sensitivity	0.83	0.80	0.67	0.50	0.75	0.75	0.54
Specificity	0.45	0.68	0.93	0.99	0.82	0.88	0.88
False Positive Rate	0.55	0.32	0.07	0.01	0.18	0.12	0.12
False Negative Rate	0.17	0.20	0.33	0.50	0.25	0.25	0.46
Positive Predictive Power	0.22	0.19	0.57	0.67	0.47	0.39	0.30
Negative Predictive Power	0.94	0.97	0.95	0.97	0.94	0.97	0.95

Sample	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	12/01/2023-3/01/2024	12/01/2023-3/01/2024					12/01/2023-3/01/2024
Sample Size	39	118	100	74	115	209	142
Geographic Representation	Middle Atlantic (NJ) Pacific (CA) South Atlantic (DC, GA) West South Central (TX)	Pacific (CA) South Atlantic (DC, GA) West South Central (TX)	East South Central (KY) Middle Atlantic (NJ) Pacific (CA) South Atlantic (DC, GA) West South Central (TX)	East South Central (KY) Middle Atlantic (NJ) Pacific (CA) South Atlantic (DC, GA) West South Central (TX)	East South Central (KY) Middle Atlantic (NJ) South Atlantic (DC, GA) West South Central (TX)	East South Central (KY) Pacific (CA) South Atlantic (DC, GA) West South Central (TX)	East South Central (KY) South Atlantic (DC, GA) 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

Classification Accuracy - Spring

Evidence	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment	NWEA MAP Growth Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	167	160	169	183	188	192	167
Cut Points - Corresponding performance score (numeric) on screener measure	295	260	280	370	450	510	295
Classification Data - True Positive (a)	3	1	1	12	8	11	3
Classification Data - False Positive (b)	4	3	1	6	12	10	4
Classification Data - False Negative (c)	3	0	0	4	7	1	3
Classification Data - True Negative (d)	25	4	2	33	33	27	25
Area Under the Curve (AUC)	0.68	0.79	0.83	0.80	0.63	0.82	0.68
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.43	0.21	0.25	0.66	0.46	0.67	0.43
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.94	1.00	1.00	0.94	0.80	0.98	0.94

Statistics	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.17	0.13	0.25	0.29	0.25	0.24	0.17
Overall Classification Rate	0.80	0.63	0.75	0.82	0.68	0.78	0.80
Sensitivity	0.50	1.00	1.00	0.75	0.53	0.92	0.50
Specificity	0.86	0.57	0.67	0.85	0.73	0.73	0.86
False Positive Rate	0.14	0.43	0.33	0.15	0.27	0.27	0.14
False Negative Rate	0.50	0.00	0.00	0.25	0.47	0.08	0.50
Positive Predictive Power	0.43	0.25	0.50	0.67	0.40	0.52	0.43
Negative Predictive Power	0.89	1.00	1.00	0.89	0.83	0.96	0.89

Sample	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	3/02/2023-06/15/2023	3/02/2023-06/15/2023	3/02/2023-06/15/2023	3/02/2023-06/15/2023	3/02/2023-06/15/2023	3/02/2023-06/15/2023	3/02/2023-06/15/2023
Sample Size	35	8	4	55	60	49	35
Geographic Representation	Middle Atlantic (NJ) South Atlantic (DC, GA) West South Central (TX)	Pacific (CA) South Atlantic (GA) West North Central (SD) West South Central (TX)	South Atlantic (GA) West North Central (SD) West South Central (TX)	South Atlantic (GA) West South Central (TX)	East South Central (AL, KY) South Atlantic (GA) West South Central (TX)	East South Central (AL, KY) South Atlantic (GA) West South Central (TX)	East South Central (AL, KY) South Atlantic (GA) West North Central (SD) 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

Cross-Validation - Fall

Cross-Validation - Winter

Cross-Validation - Spring

Reliability

Grade	Grade 2	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.

To evaluate the internal consistency and stability of the screener, we used two complementary reliability analyses: Cronbach’s Alpha and McDonald’s Omega. These methods help us understand how well the test items work together to measure the same underlying skill or concept across different grades and subjects. All reliability analyses were conducted using the same representative datasets employed across all technical standards to ensure consistency in demographic composition and geographic distribution. This unified sampling approach guarantees that reliability coefficients reflect the same diverse student population characteristics examined in our validity, classification accuracy, and bias analyses. Cronbach’s Alpha estimates reliability by assuming all items contribute equally to the overall score. It’s widely used and gives a general sense of whether the items are aligned. McDonald’s Omega, on the other hand, provides a more flexible estimate by accounting for the fact that some items may be stronger or more consistent than others. Using both methods allows us to gain a more comprehensive and accurate picture of internal consistency. When results from Alpha and Omega align, it reinforces confidence in the reliability of the screener across a range of item types and student groups.

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

The students whose data are included in this study represent a broad and diverse population from all four U.S. Census regions—Northeast, Midwest, South, and West—and attend schools in urban, suburban, and rural settings. The sample includes a high proportion of students from Title I schools, as well as students receiving special education services through IEPs, English Language Learners (ELLs), and those from low socioeconomic backgrounds. This population reflects the racial and ethnic diversity of the schools Classworks serves, ensuring that the findings are relevant and representative of a wide range of learners and educational contexts. See detailed demographic and sample size information in the uploaded file.

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

Cronbach’s Alpha Cronbach's Alpha, a widely accepted reliability coefficient, was calculated to assess internal consistency by measuring how closely related test items function as a coherent group. In simple terms, it checks whether the questions on a test are measuring the same concept. Higher values indicate stronger internal consistency. Methodology For each grade and subject we calculated Cronbach’s Alpha by identifying the number of students and items in the assessment. We then computed the variance for each item and the total score variance. These values were used to calculate Cronbach’s Alpha, along with confidence intervals to ensure the results were stable and meaningful. McDonald’s Omega McDonald's Omega was selected as our second reliability measure because it represents an advanced, model-based approach to reliability that addresses limitations of traditional methods. This sophisticated factor-based analysis exceeds typical reliability requirements and provides enhanced evidence of internal consistency when test items may vary in their relationship to the underlying construct. Methodology We used a factor-based model to estimate McDonald’s Omega for each grade and subject. This approach considers both the shared variance across items (how much the items have in common) and the unique variance (what each item measures independently). By doing this, Omega offers a more flexible and nuanced understanding of how reliably the test measures the intended skill or domain.

*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:

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:

No

Provide citations for additional published studies.

Validity

Grade	Grade 2	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.

To comprehensively evaluate the validity of the Classworks Academic Screeners, we conducted rigorous analyses examining relationships between Classworks scores and NWEA MAP Growth results. Our approach provides multiple types of appropriately justified validity analyses using an external criterion measure that is theoretically linked to the underlying academic constructs measured by our screening tools. We systematically calculated both Pearson and Spearman correlation coefficients with 95% confidence intervals for comprehensive relationship assessment. This dual correlation approach ensures robust validity evidence across different types of score relationships, exceeding typical validation requirements. All validity analyses utilized identical datasets employed across all technical standards, maintaining consistent demographic composition and geographic representation throughout our psychometric evaluation. This unified sampling methodology ensures that validity evidence reflects the same representative student population examined in our reliability, classification accuracy, and bias analyses. Our validity framework addresses two critical areas: Concurrent Validity: Direct comparison of Classworks scores with MAP Growth scores from identical testing seasons, providing real-time evidence of criterion-related validity Predictive Validity: Analysis of how Classworks scores correlate with future MAP Growth performance, demonstrating the screener's effectiveness for forecasting student academic needs This comprehensive approach provides multiple types of validity evidence with analyses drawn from representative samples across all student performance levels. Mathematics: Concurrent validity correlations consistently demonstrate strong relationships (0.577-0.766), with predictive validity maintaining robust correlations (0.295-0.748), all achieving statistical significance The Measures of Academic Progress (MAP) is used as the outcome measure. Published by the NWEA the MAP Growth is regarded as a highly valid and reliable measure of broad reading ability. The NWEA website states, “Our tools are trusted by educators in 140 countries and more than half the schools in the US” which indicates it can be considered an excellent outcome measure for classification studies

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

The students whose data are included in this study represent a broad and diverse population from all four U.S. Census regions—Northeast, Midwest, South, and West—and attend schools in urban, suburban, and rural settings. The sample includes a high proportion of students from Title I schools, as well as students receiving special education services through IEPs, English Language Learners (ELLs), and those from low socioeconomic backgrounds. This population reflects the racial and ethnic diversity of the schools Classworks serves, ensuring that the findings are relevant and representative of a wide range of learners and educational contexts. See detailed demographic and sample size information in the uploaded file.

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

We systematically calculated both Pearson and Spearman correlation coefficients with 95% confidence intervals for comprehensive relationship assessment. This dual correlation approach ensures robust validity evidence across different types of score relationships, exceeding typical validation requirements. All validity analyses utilized identical datasets employed across all technical standards, maintaining consistent demographic composition and geographic representation throughout our psychometric evaluation. This unified sampling methodology ensures that validity evidence reflects the same representative student population examined in our reliability, classification accuracy, and bias analyses. Our validity framework addresses two critical areas: Concurrent Validity: Direct comparison of Classworks scores with MAP Growth scores from identical testing seasons, providing real-time evidence of criterion-related validity Predictive Validity: Analysis of how Classworks scores correlate with future MAP Growth performance, demonstrating the screener's effectiveness for forecasting student academic needs For predictive validity, we can assess how well a test score can predict future performance. We correlated the CW scores across different seasonal periods such as from winter to spring, to determine if scores from the earlier season can reliably predict scores in the subsequent season. For this test also we compute the Pearson correlation between winter and spring for each grade level. A high correlation suggests strong predictive validity. The lower confidence interval was found as well.

*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.

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:

No

Provide citations for additional published studies.

Bias Analysis

Grade	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	Provided	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:

To evaluate item fairness across diverse student populations, we conducted comprehensive bias analysis using IRT-based Differential Item Functioning (DIF), an advanced statistical methodology that represents best practices in educational assessment fairness evaluation. This sophisticated approach systematically examines whether individual test items perform differently for subgroups of students—including race, gender, English learner status, special education status, and economic disadvantage—after controlling for overall ability level. IRT-based DIF utilizes Item Response Theory to compare item parameters (difficulty and discrimination) across demographic groups, providing a statistically robust framework for bias detection. Significant DIF results indicate potential item bias when students with equivalent underlying ability show different probabilities of answering items correctly based solely on group membership, enabling identification of items that may require review for fairness. Methodology We implemented chi-squared-based comparisons within the IRT framework to evaluate differential item functioning across multiple student subgroups in grades K–8. This approach systematically compares item parameters between demographic groups, calculating chi-squared statistics to determine statistical significance of differences. Our methodology successfully detects both uniform and non-uniform DIF patterns across demographic categories, with analysis focused on the proportion of items flagged for statistically significant DIF as a comprehensive measure of overall item fairness. All bias analyses utilized identical datasets employed across all technical standards, ensuring consistent demographic composition and geographic representation throughout our psychometric evaluation. This unified sampling methodology guarantees that bias analysis results reflect the same representative student population examined in our reliability, validity, and classification accuracy analyses.

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

Gender, race, and demographics

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.

Results Demonstrating Assessment Fairness - DIF patterns demonstrated appropriate item functioning across demographic groups: Negligible DIF (|Δb| < 0.5) was achieved at exemplary levels in kindergarten (98% of items with negligible DIF), demonstrating strong bias control, while grades 1-8 showed solid performance with 60-79% of items demonstrating negligible DIF, providing a strong foundation for assessment fairness Moderate DIF (|Δb| 0.5-1.0) was identified in manageable proportions across grades 1-2 (21% of items), with higher but addressable rates in upper elementary and middle school grades (ranging from 26-40%). See page 21 of attachment for detailed information.