Amira
Assessment & Progress Monitoring

Descriptive Information

Please provide a description of your tool:

Amira has two essential jobs: (1) provide supplemental instructional materials and student data to teachers with a bridge from science of reading professional development to classroom execution and 2) engage students in the time on the tongue needed to bridge identified reading skills gaps. Amira’s personalized learning software listens to students read aloud, identifies dyslexic students, continuously assesses reading mastery, and delivers individualized tutoring. Our solution empowers the learning community, from students to paraprofessionals, teachers, parents, and administrators, with AI-powered tools for real-time results. Utilizing Amira, teachers can continuously assess and monitor student progress in oral language, phonological awareness, phonics, fluency, vocabulary and comprehension. Amira optimizes instructional strategies and resource allocation to provide a comprehensive solution. Anytime a student reads with Amira, all stakeholders are provided up-to-the-moment feedback critical for teachers to make the right decisions for intervention support.

Is your tool designed to measure progress towards an end-of-year goal (e.g., oral reading fluency) or progress towards a short-term skill (e.g., letter naming fluency)?

End-year goal
Short-term skill

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 dimensions does the tool assess?

Reading

Global Indicator of Reading Competence
Listening Comprehension
Vocabulary
Phonemic Awareness
Decoding
Passage Reading
Word Identification
Comprehension

Spelling & Written Expression

Global Indicator of Spelling Competence
Global Indicator of Writting Expression Competence

Mathematics

Global Indicator of Mathematics Comprehension
Early Numeracy
Mathematics Concepts
Mathematics Computation
Mathematics Application
Fractions
Algebra

Other

Please describe specific domain, skills or subtests:

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

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

Acquisition and Cost Information

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:

BEHAVIOR ONLY: What is the administration format?

Direct observation
Rating scale
Checklist
Performance measure
Other

If other, please specify:

BEHAVIOR ONLY: What is the administration setting?

General education classroom
Special education classroom
School office
Recess
Lunchroom
Home
Other

If other, please specify:

Does the program require technology?

Yes

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

Computer or tablet
Internet connection
Other technology (please specify)

What is the administration context?

Individual

Small group   If small group, n=4

Large group   If large group, n=20

Computer-administered
Other

If other, please specify:

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

Administrators are encouraged to attend or complete online asynchronous 45-minute training, “Getting Started with Amira” prior to initial administration. Following administration, administrators may access self-paced asynchronous training, including understanding data on Amira University at https://amiralearninguniversity.thinkific.com/. Amira handles most of the tasks that typically are difficult for teachers. The software acts as a proctor, guiding the student through each task. The software is a highly adept support technician, identifying hardware and software issues that may impact the assessment process. Finally, Amira produces a consistent and comprehensive scoring of the items, providing the teacher with a framework for evaluating outputs.

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:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Scoring

BEHAVIOR ONLY: What types of scores result from the administration of the assessment?

Score

Observation	Behavior Rating
Frequency Duration Interval Latency	Raw score

Conversion

Observation	Behavior Rating
Rate Percent	Standard score Subscale/ Subtest Composite Stanine Percentile ranks Normal curve equivalents IRT based scores

Interpretation

Observation	Behavior Rating
Error analysis Peer comparison Rate of change	Dev. benchmarks Age-Grade equivalent

How are scores calculated?

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

If other, please specify:

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:

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.

Amira leverages machine learning and AI to automatically score every student interaction. Interactions that Amira automatically scores include rapid automatized naming (RAN), letter sound fluency, blending, word reading, word part manipulation, spelling, listening comprehension, oral reading, multiple-choice questions, open-ended oral responses, and open-ended written responses, among others. The cornerstone of Amira’s scoring system lies in its ability to automatically and accurately score these varied interactions. Where necessary, Amira incorporates rubric-based scoring to further enhance the precision of scoring, particularly for open-ended responses such Amira’s dialogue-based comprehension questions. Amira’s ability to automatically and accurately score each student interaction uniquely enables the software not only to provide immediate feedback to students but also to maintain continuously updated profiles on each student's progress, achievements, and instructional needs. Each time a student completes an activity, Amira instantly and automatically scores that activity. The scoring process also immediately updates all teacher-facing reports. This means that Amira’s scoring system allows Amira to maintain a real-time profile of each student’s progress, achievements, and instructional needs, and makes that always up-to-date profile available to educators. An integral feature of Amira’s scoring system is mechanisms for quality and equity assurance. One such mechanism is a “meta-analysis” conducted by our machine learning models. This process identifies any discrepancies that may indicate a misrepresentation of a student’s true abilities, enabling the system to flag activities that may not represent a student’s best effort. A second mechanism is a set of automated data integrity tests to ensure data quality and consistency across the locations data is stored. The third and critical mechanism is that recordings of activities are always available to educators should they want to listen and adjust any of Amira’s scoring. Educators may click on an activity in any of Amira’s reports to bring up a recording of the activity and adjust scores as needed. Amira’s Scoring System rigorously and universally adheres to the following principles: (a) The scoring of ALL items is visible and transparent to teachers/proctors. There is no black box – educators can see every item taken and every Amira score; (b) educators have the ability to actually listen to student responses, enabling 100% auditability of Amira’s scoring; (c) educators/teachers/proctors have the ability to correct Amira’s scoring manually. If desired, a state/district can allow educators to have the final word on the scoring of any student’s assessment, with the ability to override the Amira scoring item by item.

Do you provide basis for calculating slope (e.g., amount of improvement per unit in time)?

Yes

ACADEMIC ONLY: Do you provide benchmarks for the slopes?

Yes

ACADEMIC ONLY: Do you provide percentile ranks for the slopes?

Yes

What is the basis for calculating slope and percentile scores?

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

Describe the tool’s approach to progress monitoring, behavior samples, test format, and/or scoring practices, including steps taken to ensure that it is appropriate for use with culturally and linguistically diverse populations and students with disabilities.

Amira administers Progress Monitoring using a grade level oral reading frequency that can level down in difficulty, if needed for the student to access the passage. Schools administer this as often as every other week to monitor improvement throughout the year. Amira automatically scores and provides a Progress Monitoring Report after each administration. The Progress Monitoring report includes a predictive growth slope and one-click access to recordings of students reading. Amira texts and skills used for progress monitoring and assessment are fully aligned with each state’s standards and provide educators with reports aligned with their specific state standards. Our content is carefully created and curated to offer the right skills for practice but also be engaging and interesting to different students. All content in Amira is tested and evaluated by teachers and students. Amira’s library of over 4,000 stories includes narratives, fiction and non-fiction texts, poetry, letters, and other forms of writing and includes over 1,000 unique Spanish language texts authored by native Spanish-speaking authors. Amira software is accessibility ready, adhering to the Web Content Accessibility Guidelines (WCAG 2.0), ensuring that content is accessible and enhancing usability for all users.

Rates of Improvement and End of Year Benchmarks

Is minimum acceptable growth (slope of improvement or average weekly increase in score by grade level) specified in your manual or published materials?

Yes

If yes, specify the growth standards:

Using Amira's national growth norms, Amira generates “Expected Growth” for each student depending on their grade level and beginning of year (fall) assessment percentile rank. The minimum acceptable growth is .03 points of ARM score increase per elapsed school week of time. This equates to a year’s growth in a school year.

Are benchmarks for minimum acceptable end-of-year performance specified in your manual or published materials?

Yes

If yes, specify the end-of-year performance standards:

Performance standards are available for the end-of-year assessment. These levels are set based on percentile rank (PR) values and vary by grade level. For Kindergarten and grade 1, levels are set based on Amira Reading Mastery PRs, which must exceed the 50th PR to achieve minimum standards. For grades 2 through 6, performance standards are set based on the Amira Reading Mastery and Oral Reading Fluency PRs. Students in grades 2-6 must be at the 50th PR or higher on both scores’ PRs to achieve minimum acceptable end-of-year performance.

What is the basis for specifying minimum acceptable growth and end of year benchmarks?

Norm-referenced
Criterion-referenced
Other

If other, please specify:

False

National representation (check all that apply):

Northeast:

New England

Middle Atlantic

Midwest:

East North Central

West North Central

South:

South Atlantic

East South Central

West South Central

West:

Mountain

Pacific

Local representation (please describe, including number of states)

Amira’s norms are based on a sample of students from hundreds of districts across the country. These districts were selected to emulate the diversity and variation of the national population of students. Schools and districts were selected to be nationally representative in a variety of dimensions, including school type (public, private, and charter), multilingual learners, socioeconomic status, geographic region, gender, and ethnicity. The total sample size was approximately 1.5 million students across Grades K to 6 in English, which was collected during the 2023-2024 school year. Data was collected from all 50 states plus the District of Columbia.

Date

2023-2024

Size

Approximately 1.5 million students

Gender (Percent)

Male

52

Female

48

Unknown

0

SES indicators (Percent)

Eligible for free or reduced-price lunch

Other SES Indicators

Census information on household median annual income reported at the district level was used to determine inclusion in the norm sample.

Race/Ethnicity (Percent)

White, Non-Hispanic

36.6

Black, Non-Hispanic

21.1

Hispanic

25.8

American Indian/Alaska Native

1.3

Asian/Pacific Islander

9.7

Other

5.5

Unknown

0

Disability classification (Please describe)

Not available

First language (Please describe)

Not available

Language proficiency status (Please describe)

16% of norm sample contains EL (English Learner) status as reported by NCES at the district level

Do you provide, in your user’s manual, norms which are disaggregated by race or ethnicity? If so, for which race/ethnicity?

White, Non-Hispanic
Black, Non-Hispanic
Hispanic
American Indian/Alaska Native
Asian/Pacific Islander
Other
Unknown

If criterion-referenced, describe procedure for specifying criterion for adequate growth and benchmarks for end-of-year performance levels.

Describe any other procedures for specifying adequate growth and minimum acceptable end of year performance.

Reliability

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

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

Reliability refers to the relative stability with which a test measures the same skills across minor differences in conditions. Two types of reliability are reported in the table below, Parallel Form reliability and Cronbach’s coefficient alpha. Parallel Forms Reliability is crucial for ensuring the consistency of the Amira Progress Monitoring assessment. This analysis measures the consistency of results across different assessment forms, which is essential for accurately tracking student growth, since students receive a different form each time they receive a progress monitoring assessment. By confirming that each form is equivalent, we can ensure that any observed improvements in student scores are due to actual learning, not differences in complexity or difficulty in the test forms. The coefficient reported is the average correlation among alternate forms of the measure. High alternate-form reliability coefficients suggest that these multiple forms are measuring the same construct. Coefficient alpha, commonly known as Cronbach's alpha, is a measure of internal consistency reliability used widely in education research and other fields. It estimates the proportion of total variance in a set of scores that is attributable to the true score variance, reflecting the reliability of the measurement.

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

The samples used to establish reliability include students who tested in the 2023-2024 school year. Both samples encompassed at least dozens of districts across the country for each grade. These districts were selected to emulate the diversity and variation of the national population of students and are representative in a variety of dimensions including school type, socioeconomic status, geographic region, gender, race, and ethnicity. Students in the parallel forms reliability sample each took two different Progress Monitoring forms within the same time window (1 week). Students in the internal consistency analyses were those who had taken at least 5 forms (instances) of Progress Monitoring across the 2023-2024 school year.

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

To assess parallel forms reliability, two forms of the assessment were administered to the same group of students within the range of one week. The scores obtained on each assessment version were then correlated to assess the degree of consistency between them. We measure these correlations using Pearson’s correlation coefficient, which is a measure of the strength of the linear relationship between two variables. The practical significance of the reliability coefficients was evaluated as follows: poor (0−0.39), adequate (0.40−0.59), good (0.60−0.79), and excellent (0.80−1.0). These estimates of practical significance are arbitrary, but conventionally used, and provide a useful heuristic for interpreting the reliability data. Confidence intervals were then calculated for the correlation coefficients computed across distinct pairs of forms. To obtain an estimate of internal consistency reliability, Cronbach's alphas were calculated for students who had taken at least 5 forms of Progress Monitoring assessment over the year. The 95% confidence interval of each reliability metric is computed using the bootstrap method, where 1000 samples with replacement are drawn from the data, and the 2.5% and 97.5% quantiles are calculated and reported.

*In the table(s) below, report the results of the reliability analyses described above (e.g., model-based evidence, internal consistency or inter-rater reliability coefficients). Include detail about the type of reliability data, statistic generated, and sample size and demographic information.

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.

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	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

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

Concurrent validity measures how well Amira scores correlate with the scores of another test that is administered at the same time and is already established as valid for measuring the same construct. Predictive validity refers to the extent to which scores on the Amira assessment can accurately predict future performance on a related outcome or criterion. The external assessments used in these studies include the i-Ready Reading Diagnostic and NWEA MAP Reading assessment. Both assessments are nationally-normed, computer adaptive measures of reading ability that are widely used in many states with established validity studies of their own.

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

The samples include students who tested in the 2022-2023 school year. This includes a sample of students from hundreds of districts across the country. These districts were selected to emulate the diversity and variation of the national population of students and are representative in a variety of dimensions including school type, socioeconomic status, geographic region, gender, race, and ethnicity. Sample sizes for each validity study vary across testing window, grade and criterion measure ranging from 988 to 5,643.

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

Concurrent validity was established by correlating Amira’s Reading Mastery (ARM) scores from students in grades K through 3 who took both an Amira Progress Monitoring assessment and the external measure within the same two-week window of one another. The predictive validity of Amira was examined by correlating Amira’s Progress Monitoring assessment scores taken during the beginning of year (Fall) window to scores from external measures taken at the end of the school year (Spring). In both forms of validity, the relationship between Amira’s scores and the external criterion measure was evaluated using Pearson’s correlation coefficient. Coefficients were calculated using bootstrap sampling across 100 random samples, and median correlation coefficients as well as 95% confidence intervals on the correlation coefficients are reported. All median and lower-bound correlation coefficients are 0.70 or higher, indicating a strong positive linear relationship between Amira and the external measure.

*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.

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

No

Manual cites other published reliability studies:

Yes

Provide citations for additional published studies.

Rice, M. L., & Hoffman, L. (2015). Predicting vocabulary growth in children with and without specific language impairment: A longitudinal study from 2;6 to 21 years of age. Journal of Speech, Language, and Hearing Research, 58(2), 345–359. Boscardin, C. K., Muthén, B., Francis, D. J., & Baker, E. L. (2008). Early identification of reading difficulties using heterogeneous developmental trajectories. Journal of Educational Psychology, 100(1), 192. https://www.amiralearning.com/amira-technical-guide.html

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

All results show a correlation of 0.7 or higher (strong correlation) between Amira’s Progress Monitoring scores and external criterion scores, so the provided data support the validity of the tool to a high degree.

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.

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

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.

No

If yes,

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

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

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.

Sensitivity: Reliability of Slope

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

Describe the sample, including size and characteristics. Please provide documentation showing that the sample was composed of students in need of intensive intervention. A sample of students with intensive needs should satisfy one of the following criteria: (1) all students scored below the 30th percentile on a local or national norm, or the sample mean on a local or national test fell below the 25th percentile; (2) students had an IEP with goals consistent with the construct measured by the tool; or (3) students were non-responsive to Tier 2 instruction. Evidence based on an unknown sample, or a sample that does not meet these specifications, may not be considered.

The sample consisted of students who took Amira Progress Monitoring during the Fall (August through November) and Winter (November through April) windows of the 2023-2024 school year. Sample sizes ranged from 90 to 660 based on grade level. A student was included in the sample if they had Fall benchmark performance below the 30th PR and had tested at least 10 times on a regular cadence spanning a period of at least 20 weeks.

Describe the frequency of measurement (for each student in the sample, report how often data were collected and over what span of time).

Progress monitoring data were collected throughout the school year at the discretion of the administering educator. Any student who had fewer than ten progress monitoring assessments was excluded from the analysis and each measurement point was spaced at least one week apart.

Describe the analysis procedures.

Hierarchical Linear Modeling was used to compute the reliability of slope for Amira Progress Monitoring. HLM is particularly useful for computing the reliability of slopes in longitudinal data analysis where scores are nested within students. In such models, the reliability of the growth rate (slope) can be assessed to understand how consistently students improve over time. Reliability of the slope is the ratio of the true score variance to the total variance. The true score variance is the random slope variance in the multilevel regression using a random intercept and random slope model, while the total variance is the estimation of total variance of each student’s individual slope of improvement. An HLM model where score was predicted by unique student IDs and time (number of days from the student’s first assessment) in a mixed-effects model, with student slopes treated as random effects, was fitted to the dataset using the lme4 package in R. True score variance and total variance were extracted from the random effects parameter estimates of the model and used to calculate the reliability of slope in accordance with Raudenbush and Bryk (2002). Bootstrap sampling was used to create 100 random samples of the population in order to compute a 95% confidence interval on the reliability of slope measure. Raudenbush, Stephen W., and Anthony S. Bryk. Hierarchical linear models: Applications and data analysis methods. Vol. 1. Sage, 2002.

In the table below, report reliability of the slope (e.g., ratio of true slope variance to total slope variance) by grade level (if relevant).

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 of the slope data that is disaggregated by subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?

No

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

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.

Sensitivity: Validity of Slope

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

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

Describe the sample(s), including size and characteristics. Please provide documentation showing that the sample was composed of students in need of intensive intervention. A sample of students with intensive needs should satisfy one of the following criteria: (1) all students scored below the 30th percentile on a local or national norm, or the sample mean on a local or national test fell below the 25th percentile; (2) students had an IEP with goals consistent with the construct measured by the tool; or (3) students were non-responsive to Tier 2 instruction. Evidence based on an unknown sample, or a sample that does not meet these specifications, may not be considered.

Describe the frequency of measurement (for each student in the sample, report how often data were collected and over what span of time).

Describe the analysis procedures for each reported type of validity.

In the table below, report predictive validity of the slope (correlation between the slope and achievement outcome) by grade level (if relevant).
NOTE: The TRC suggests controlling for initial level when the correlation for slope without such control is not adequate.

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

Manual cites other published validity studies:

Provide citations for additional published studies.

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

Do you have validity of the slope data that is disaggregated by subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?

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

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

Manual cites other published validity studies:

Provide citations for additional published studies.

Alternate Forms

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

Describe the sample for these analyses, including size and characteristics:

What is the number of alternate forms of equal and controlled difficulty?

If IRT based, provide evidence of item or ability invariance

If computer administered, how many items are in the item bank for each grade level?

If your tool is computer administered, please note how the test forms are derived instead of providing alternate forms:

Decision Rules: Setting & Revising Goals

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

In your manual or published materials, do you specify validated decision rules for how to set and revise goals?

No

If yes, specify the decision rules:

What is the evidentiary basis for these decision rules?
NOTE: The TRC expects evidence for this standard to include an empirical study that compares a treatment group to a control and evaluates whether student outcomes increase when decision rules are in place.

Decision Rules: Changing Instruction

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

In your manual or published materials, do you specify validated decision rules for when changes to instruction need to be made?

No

If yes, specify the decision rules:

What is the evidentiary basis for these decision rules?
NOTE: The TRC expects evidence for this standard to include an empirical study that compares a treatment group to a control and evaluates whether student outcomes increase when decision rules are in place.