Acadience Math
Computation
Summary
Computation is a standardized measure designed to assess students’ progress in the basic skills of math computation. It can be administered individually or to groups. Students write their answers to basic computation problems under standardized conditions and time limits. The time limit depends on the grade level the student is in and varies from two minutes to six minutes. The total score is based on the number of correct digits in the final answer of each problem. An optional response pattern analysis can also be completed to give additional instructional information by analyzing the student's response patterns.
- Where to Obtain:
- Developer: Acadience Learning Inc., Publisher: Voyager Sopris Learning
- info@acadiencelearning.org; customerservice@voyagersopris.com
- Acadience Learning Inc. 859 Willamette Street, Suite 320, Eugene, OR 97401; Cambium Learning Group 17855 Dallas Parkway, Suite 400 Dallas, TX 75287
- (541)431-6931, (888)943-1240; (800)547-6747
- https://acadiencelearning.org/; https://www.voyagersopris.com/
- Initial Cost:
- Free
- Replacement Cost:
- Free
- Included in Cost:
- All materials are available for free download at https://acadiencelearning.org, including progress monitoring worksheets for each grade, assessor scoring booklets and keys for each grade, the Acadience Math Assessment Manual, and the Acadience Math Technical Adequacy Brief. ADDITIONAL INFO WHERE TOOL CAN BE OBTAINED: Voyager Sopris Learning (Published print version in color) Website: http://voyagersopris.com Address: 17855 Dallas Parkway, Suite 400, Dallas, TX 75287-6816 Telephone number: (800) 547-6747 ADDITIONAL INFO COST INFO: Voyager Sopris Learning Initial cost for implementing program: $5.60 Unit of cost: Student Replacement cost for subsequent use: $2.08 Unit of cost: Student License Duration: Year A grade-level classroom kit from Voyager Sopris Learning will provide the needed materials for 25 students within a classroom and in grades 2 - 6 will costs $139.95. The classroom kit includes student benchmark Computation and Concepts and Applications worksheets for assessing skills in the beginning, middle, and end of year. Also included are the needed teacher keys, student booklets, assessment manual, and progress monitoring forms for five students.
- Approved accommodations are any accommodations that will not alter the standardization of the assessment. Specific approved accommodations include, but are not limited to: 1. The use of colored overlays, filters, or lighting adjustments for students with visual impairments. 2. The use of student materials that have been enlarged or with larger print for students with visual impairments. 3. The use of assistive technology, such as hearing aids and assistive listening devices (ALDs), for students with hearing impairments. 4. The use of a marker or ruler to focus student attention on the materials for students who are not able to demonstrate their skills adequately without one. Unapproved accommodations are accommodations that are likely to change how the assessment functions (such as modifying the timing rules or reading Concepts and Applications items to students). Scores from measures administered with unapproved accommodations should not be treated or reported as official Acadience Math scores and cannot be compared to other Acadience Math scores or benchmark goals but can be used to measure individual growth for a student. An unapproved accommodation may be used when (a) a student cannot be tested accurately using the standardized rules or approved accommodations, but the school would still like to measure progress for that student; or (b) a student’s Individualized Education Plan (IEP) requires testing with an unapproved accommodation. For more information about accommodations please see the Acadience Math Assessment Manual.
- Training Requirements:
- One to two hours of training to cover foundations of Acadience Math as well as administration and scoring of the measure.
- Qualified Administrators:
- Administrator must have adequate training on administration and scoring of the assessments.
- Access to Technical Support:
- Acadience Learning provides customer support for all Acadience Math assessments, as well as support for the data management and reporting system, Acadience Data Management. Staff are available by phone and email on weekdays from 7am to 5pm Pacific Time, for no additional cost. The majority of customer support requests are resolved in less than one business day.
- Assessment Format:
-
- Scoring Time:
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- 1 minutes per worksheet
- Scores Generated:
-
- Raw score
- Percentile score
- Developmental benchmarks
- Developmental cut points
- Administration Time:
-
- 2 minutes per worksheet, based on grade
- Scoring Method:
-
- Manually (by hand)
- Technology Requirements:
-
- Accommodations:
- Approved accommodations are any accommodations that will not alter the standardization of the assessment. Specific approved accommodations include, but are not limited to: 1. The use of colored overlays, filters, or lighting adjustments for students with visual impairments. 2. The use of student materials that have been enlarged or with larger print for students with visual impairments. 3. The use of assistive technology, such as hearing aids and assistive listening devices (ALDs), for students with hearing impairments. 4. The use of a marker or ruler to focus student attention on the materials for students who are not able to demonstrate their skills adequately without one. Unapproved accommodations are accommodations that are likely to change how the assessment functions (such as modifying the timing rules or reading Concepts and Applications items to students). Scores from measures administered with unapproved accommodations should not be treated or reported as official Acadience Math scores and cannot be compared to other Acadience Math scores or benchmark goals but can be used to measure individual growth for a student. An unapproved accommodation may be used when (a) a student cannot be tested accurately using the standardized rules or approved accommodations, but the school would still like to measure progress for that student; or (b) a student’s Individualized Education Plan (IEP) requires testing with an unapproved accommodation. For more information about accommodations please see the Acadience Math Assessment Manual.
Descriptive Information
- Please provide a description of your tool:
- Computation is a standardized measure designed to assess students’ progress in the basic skills of math computation. It can be administered individually or to groups. Students write their answers to basic computation problems under standardized conditions and time limits. The time limit depends on the grade level the student is in and varies from two minutes to six minutes. The total score is based on the number of correct digits in the final answer of each problem. An optional response pattern analysis can also be completed to give additional instructional information by analyzing the student's response patterns.
ACADEMIC ONLY: What skills does the tool screen?
- Please describe specific domain, skills or subtests:
- BEHAVIOR ONLY: Which category of behaviors does your tool target?
-
- 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
Administration
- Are norms available?
- Yes
- Are benchmarks available?
- Yes
- If yes, how many benchmarks per year?
- 3
- If yes, for which months are benchmarks available?
- Beginning of year (months 1 - 3 of school year), middle of year (months 4 - 6 of the school year), and end of year (months 7 - 9 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?
- Yes
- Describe the time required for administrator training, if applicable:
- One to two hours of training to cover foundations of Acadience Math as well as administration and scoring of the measure.
- Please describe the minimum qualifications an administrator must possess.
- Administrator must have adequate training on administration and scoring of the assessments.
-
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?
- No
- If No, please describe training costs:
- The Acadience Math Assessment Manual is available for free download along with the test materials. In addition, Acadience Learning and Voyager Sopris Learning offers a variety of training options to meet different needs and at different price points. Training options include pre-recorded online training, live online webinars, onsite training (hiring a trainer to come out to the school or district), and Acadience Learning conducts a Super Institute, which takes place each summer. Acadience Learning or Voyager Sopris Learning staff can work with schools, LEAs, regional agencies, and SEAs to develop customized training plans to meet their unique needs. We also have an Acadience Math Mentor program, where a single attendee or small group of attendees can become Acadience Math Mentors and receive access to our official training materials, which they can use to train others in their school or district. For an individual teacher subscription to the online Acadience Math Essential Workshop, the cost is $129. Please note: Other training options may cost more or less depending on the circumstances and the number of attendees.
- Can users obtain ongoing professional and technical support?
- Yes
- If Yes, please describe how users can obtain support:
- Acadience Learning provides customer support for all Acadience Math assessments, as well as support for the data management and reporting system, Acadience Data Management. Staff are available by phone and email on weekdays from 7am to 5pm Pacific Time, for no additional cost. The majority of customer support requests are resolved in less than one business day.
Scoring
- Do you provide basis for calculating performance level scores?
-
Yes
- Does your tool include decision rules?
-
Yes
- If yes, please describe.
- The Acadience Math benchmark goals provide targeted levels of skill that students need to achieve by specific points in time in order to be considered to be making adequate progress. The Stanford Achievement Test Series, Tenth Edition–Total Math score (SAT10; Pearson, 2003), a high-quality, nationally norm-referenced assessment, was used as an external criterion in the validity study. In the validity study, the 40th percentile at or above the SAT10 Total Math Raw Score was used as one approximation of adequate math skill. The intent is to develop generalizable benchmark goals and cut points that are relevant and appropriate for a wide variety of math outcomes, across a wide variety of states and regions, and for diverse groups of students. The principle vision for Acadience Math is a step-by-step vision. Student skills at or above benchmark at the beginning of the year put the odds in favor of the student achieving the middle-of-year benchmark goal. In turn, students with skills at or above benchmark in the middle of the year have the odds in favor of achieving the end-of-year benchmark goal. Finally, students with skills at or above benchmark at the end of the year have odds in favor of having adequate math skills on a wide variety of external measures of math proficiency. The fundamental logic for developing the benchmark goals and cut points for risk was to begin with the external outcome goal and work backward in that step-by- step system. We first obtained an external criterion measure (the SAT10 Total Math Raw Score) at the end of the year with a level of performance that would represent adequate math skills (the SAT10 Total Math Raw Score at the 40th percentile rank). Next, we specified the benchmark goal and cut point for risk for end-of-year Computation with respect to the end-of-year external criterion. Then, using the Computation end-of-year goal as an internal criterion, we established the benchmark goals and cut points for risk for middle-of-year Computation. Finally, we established the benchmark goals and cut points for risk for beginning-of-year Computation using the middle-of-year Computation goal as an internal criterion (see the Acadience Math Benchmark Goals Document).
- Can you provide evidence in support of multiple decision rules?
-
Yes
- If yes, please describe.
- Research evidence supporting the use of Acadience Math measures for benchmark assessment three times per year is found in the Acadience Math Technical Adequacy Brief, Acadience Math Assessment Manual, and the Acadience Math Benchmark Goals document.
- 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.
- The Computation total score is based on the number of correct digits in the final answer of each of the problems completed within the time limit. Each correct digit is associated with a specific number of points, as indicated by a legend on the teacher key. There are between 16 and 25 problems on each worksheet arranged in rows and columns. The number of items on each worksheet is as follows: Grade 2: 20 problems; Grade 3: 25 problems; Grade 4: 25 problems; Grade 5: 16 problems; Grade 6: 16 problems. For each problem that the student completed or attempted, the number of points he or she received are written next to that problem. The points are added across each row, written in the margin, and then summed to calculate the student’s total score, which is recorded at the top of the page in the space provided. The final score for a progress monitoring assessment is the score from one student worksheet.
- 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 Acadience Math measures were developed to provide teachers with information they need to make decisions about instruction. The authors advocate a data-based decision-making model referred to as the Outcomes-Driven Model, because the data are used to make decisions to improve student outcomes by matching the amount and type of instructional support with the needs of the individual students. These steps of the model repeat each trimester (i.e., beginning of year, middle of year, and end of year) as a student progresses through the grades. At the beginning of the trimester, the first step is to identify students who may need additional support. At the end of the trimester, the final step is to review outcomes, which also facilitates identifying students who need additional support for the next trimester. In this manner, educators can ensure that students who are on track to become proficient at math continue to make adequate progress, and that those students who are not on track receive the support they need to become proficient at math. Step 1: Identify need for support early. This process occurs during benchmark assessment, and is also referred to as universal screening. The purpose is to identify those students who may need additional instructional support to achieve benchmark goals. The benchmark assessment also provides information regarding the performance of all students in the school with respect to benchmark goals. All students within a school or grade are tested three times per year on grade-level material. The testing occurs at the beginning, middle, and end of the school year. Step 2: Validate need for support. The purpose of this step is to be reasonably confident that the student needs or does not need additional instructional support. Before making individual student decisions, it is important to consider additional information beyond the initial data obtained during benchmark testing. Teachers can always use additional assessment information and knowledge about a student to validate a score before making decisions about instructional support. If there is a discrepancy in the student’s performance relative to other information available about the student, or if there is a question about the accuracy of a score, the score can be validated by retesting the student using alternate forms of the Acadience Math measures or additional diagnostic assessments as necessary. Step 3: Plan and implement support. In general, for students who are meeting the benchmark goals, a good, research-based core classroom curriculum should meet their instructional needs, and they will continue to receive benchmark assessment three times per year to ensure they remain on track. Students who are identified as needing support are likely to require additional instruction or intervention in the skill areas where they are having difficulties. Step 4: Evaluate and modify support as needed. Students who are receiving additional support should be progress monitored more frequently to ensure that the instructional support being provided is helping them get back on track. Students should be monitored on the measures that test the skill areas where they are having difficulties and receiving additional instructional support. Monitoring may occur once per month, once every two weeks, or as often as once per week. In general, students who need the most intensive instruction are progress monitored most frequently. Step 5: Review outcomes. By looking at the benchmark assessment data for all students, schools can ensure that their instructional supports—both core curriculum and additional interventions—are working for all students. If a school identifies areas of instructional support that are not working as desired, the school can use the data to help make decisions on how to improve. The use of Acadience Math measures within the Outcomes-Driven Model is consistent with the most recent reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA), which allows the use of a Response to Intervention (RtI) approach to identify children with learning disabilities. In an RtI approach to identification, early intervention is provided to students who are at risk for the development of learning difficulties. Data are gathered to determine which students are responsive to the intervention provided and which students need more intensive support (Fuchs & Fuchs, 2006). The Outcomes-Driven Model is based on foundational work with a problem-solving model (see Deno, 1989; Shinn, 1995; Tilly, 2008) and the initial application of the problem-solving model to early literacy skills (Kaminski & Good, 1998). The general questions addressed by a problem-solving model include: What is the problem? Why is it happening? What should be done about it? Did it work? (Tilly, 2008). The Outcomes-Driven Model was developed to address these questions, but within a prevention-oriented framework designed to preempt early math difficulty and ensure step-by-step progress toward outcomes that will result in established, adequate math achievement.
Technical Standards
Classification Accuracy & Cross-Validation Summary
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Grade 3
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Grade 4
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Grade 5
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Grade 6
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| Classification Accuracy Fall |
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| Classification Accuracy Winter |
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| Classification Accuracy Spring |
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Legend
Convincing evidencePartially convincing evidenceUnconvincing evidence
Data unavailabledDisaggregated data available
SAT 10 Math
Classification Accuracy
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- 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).
- Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?
-
No
- 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?
-
No
- If yes,
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- 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.
SAT 10 Math
Classification Accuracy
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- 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).
- Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?
-
No
- 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?
-
No
- If yes,
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- 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.
SAT 10 Math
Classification Accuracy
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- 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).
- Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?
-
No
- 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?
-
No
- If yes,
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- 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 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Criterion measure | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math |
| Cut Points - Percentile rank on criterion measure | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 25 | 20 | 20 | 20 | 20 | 20 |
| Cut Points - Performance score on criterion measure | |||||||||||||||
| Cut Points - Corresponding performance score (numeric) on screener measure | |||||||||||||||
| Classification Data - True Positive (a) | 8 | 8 | 8 | 14 | 14 | 14 | 10 | 10 | 10 | 17 | 7 | 7 | 4 | 4 | 4 |
| Classification Data - False Positive (b) | 3 | 3 | 3 | 7 | 7 | 7 | 2 | 2 | 2 | 19 | 18 | 18 | 3 | 3 | 3 |
| Classification Data - False Negative (c) | 9 | 9 | 9 | 16 | 16 | 16 | 3 | 3 | 3 | 8 | 3 | 3 | 8 | 8 | 8 |
| Classification Data - True Negative (d) | 108 | 108 | 108 | 73 | 73 | 73 | 53 | 53 | 53 | 75 | 91 | 91 | 55 | 55 | 55 |
| Area Under the Curve (AUC) | 0.87 | 0.87 | 0.87 | 0.83 | 0.83 | 0.83 | 0.91 | 0.91 | 0.91 | 0.83 | 0.81 | 0.81 | 0.87 | 0.87 | 0.87 |
| AUC Estimate’s 95% Confidence Interval: Lower Bound | 0.80 | 0.80 | 0.80 | 0.75 | 0.75 | 0.75 | 0.83 | 0.83 | 0.83 | 0.75 | 0.72 | 0.72 | 0.78 | 0.78 | 0.78 |
| AUC Estimate’s 95% Confidence Interval: Upper Bound | 0.94 | 0.94 | 0.94 | 0.91 | 0.91 | 0.91 | 0.98 | 0.98 | 0.98 | 0.90 | 0.89 | 0.89 | 0.95 | 0.95 | 0.95 |
| Statistics | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Base Rate | 0.13 | 0.13 | 0.13 | 0.27 | 0.27 | 0.27 | 0.19 | 0.19 | 0.19 | 0.21 | 0.08 | 0.08 | 0.17 | 0.17 | 0.17 |
| Overall Classification Rate | 0.91 | 0.91 | 0.91 | 0.79 | 0.79 | 0.79 | 0.93 | 0.93 | 0.93 | 0.77 | 0.82 | 0.82 | 0.84 | 0.84 | 0.84 |
| Sensitivity | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.77 | 0.77 | 0.77 | 0.68 | 0.70 | 0.70 | 0.33 | 0.33 | 0.33 |
| Specificity | 0.97 | 0.97 | 0.97 | 0.91 | 0.91 | 0.91 | 0.96 | 0.96 | 0.96 | 0.80 | 0.83 | 0.83 | 0.95 | 0.95 | 0.95 |
| False Positive Rate | 0.03 | 0.03 | 0.03 | 0.09 | 0.09 | 0.09 | 0.04 | 0.04 | 0.04 | 0.20 | 0.17 | 0.17 | 0.05 | 0.05 | 0.05 |
| False Negative Rate | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.23 | 0.23 | 0.23 | 0.32 | 0.30 | 0.30 | 0.67 | 0.67 | 0.67 |
| Positive Predictive Power | 0.73 | 0.73 | 0.73 | 0.67 | 0.67 | 0.67 | 0.83 | 0.83 | 0.83 | 0.47 | 0.28 | 0.28 | 0.57 | 0.57 | 0.57 |
| Negative Predictive Power | 0.92 | 0.92 | 0.92 | 0.82 | 0.82 | 0.82 | 0.95 | 0.95 | 0.95 | 0.90 | 0.97 | 0.97 | 0.87 | 0.87 | 0.87 |
| Sample | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||
| Sample Size | 128 | 128 | 128 | 110 | 110 | 110 | 68 | 68 | 68 | 119 | 119 | 119 | 70 | 70 | 70 |
| Geographic Representation | Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (OR) West North Central (KS, MO) |
Pacific (OR) West North Central (KS, MO) |
Pacific (OR) West North Central (KS, 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 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Criterion measure | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math |
| Cut Points - Percentile rank on criterion measure | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
| Cut Points - Performance score on criterion measure | |||||||||||||||
| Cut Points - Corresponding performance score (numeric) on screener measure | |||||||||||||||
| Classification Data - True Positive (a) | 10 | 10 | 10 | 15 | 15 | 15 | 13 | 13 | 13 | 16 | 16 | 16 | 9 | 9 | 9 |
| Classification Data - False Positive (b) | 6 | 6 | 6 | 6 | 6 | 6 | 1 | 1 | 1 | 5 | 5 | 5 | 4 | 4 | 4 |
| Classification Data - False Negative (c) | 7 | 7 | 7 | 16 | 16 | 16 | 5 | 5 | 5 | 10 | 10 | 10 | 5 | 5 | 5 |
| Classification Data - True Negative (d) | 106 | 106 | 106 | 75 | 75 | 75 | 59 | 59 | 59 | 89 | 89 | 89 | 56 | 56 | 56 |
| Area Under the Curve (AUC) | 0.87 | 0.87 | 0.87 | 0.81 | 0.81 | 0.81 | 0.93 | 0.93 | 0.93 | 0.86 | 0.86 | 0.86 | 0.89 | 0.89 | 0.89 |
| AUC Estimate’s 95% Confidence Interval: Lower Bound | 0.79 | 0.79 | 0.79 | 0.73 | 0.73 | 0.73 | 0.84 | 0.84 | 0.84 | 0.78 | 0.78 | 0.78 | 0.82 | 0.82 | 0.82 |
| AUC Estimate’s 95% Confidence Interval: Upper Bound | 0.95 | 0.95 | 0.95 | 0.89 | 0.89 | 0.89 | 0.99 | 0.99 | 0.99 | 0.93 | 0.93 | 0.93 | 0.96 | 0.96 | 0.96 |
| Statistics | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Base Rate | 0.13 | 0.13 | 0.13 | 0.28 | 0.28 | 0.28 | 0.23 | 0.23 | 0.23 | 0.22 | 0.22 | 0.22 | 0.19 | 0.19 | 0.19 |
| Overall Classification Rate | 0.90 | 0.90 | 0.90 | 0.80 | 0.80 | 0.80 | 0.92 | 0.92 | 0.92 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 |
| Sensitivity | 0.59 | 0.59 | 0.59 | 0.48 | 0.48 | 0.48 | 0.72 | 0.72 | 0.72 | 0.62 | 0.62 | 0.62 | 0.64 | 0.64 | 0.64 |
| Specificity | 0.95 | 0.95 | 0.95 | 0.93 | 0.93 | 0.93 | 0.98 | 0.98 | 0.98 | 0.95 | 0.95 | 0.95 | 0.93 | 0.93 | 0.93 |
| False Positive Rate | 0.05 | 0.05 | 0.05 | 0.07 | 0.07 | 0.07 | 0.02 | 0.02 | 0.02 | 0.05 | 0.05 | 0.05 | 0.07 | 0.07 | 0.07 |
| False Negative Rate | 0.41 | 0.41 | 0.41 | 0.52 | 0.52 | 0.52 | 0.28 | 0.28 | 0.28 | 0.38 | 0.38 | 0.38 | 0.36 | 0.36 | 0.36 |
| Positive Predictive Power | 0.63 | 0.63 | 0.63 | 0.71 | 0.71 | 0.71 | 0.93 | 0.93 | 0.93 | 0.76 | 0.76 | 0.76 | 0.69 | 0.69 | 0.69 |
| Negative Predictive Power | 0.94 | 0.94 | 0.94 | 0.82 | 0.82 | 0.82 | 0.92 | 0.92 | 0.92 | 0.90 | 0.90 | 0.90 | 0.92 | 0.92 | 0.92 |
| Sample | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||
| Sample Size | 129 | 129 | 129 | 112 | 112 | 112 | 78 | 78 | 78 | 120 | 120 | 120 | 74 | 74 | 74 |
| Geographic Representation | Pacific (CA, OR) West North Central (MO) |
Pacific (CA, OR) West North Central (MO) |
Pacific (CA, OR) West North Central (MO) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (OR) West North Central (KS, MO) |
Pacific (OR) West North Central (KS, MO) |
Pacific (OR) West North Central (KS, 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 - Spring
| Evidence | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Criterion measure | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math | SAT 10 Math |
| Cut Points - Percentile rank on criterion measure | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
| Cut Points - Performance score on criterion measure | |||||||||||||||
| Cut Points - Corresponding performance score (numeric) on screener measure | |||||||||||||||
| Classification Data - True Positive (a) | 7 | 7 | 7 | 15 | 15 | 15 | 8 | 8 | 8 | 19 | 19 | 19 | 7 | 7 | 7 |
| Classification Data - False Positive (b) | 11 | 11 | 11 | 17 | 17 | 17 | 1 | 1 | 1 | 3 | 3 | 3 | 6 | 6 | 6 |
| Classification Data - False Negative (c) | 3 | 3 | 3 | 8 | 8 | 8 | 1 | 1 | 1 | 9 | 9 | 9 | 2 | 2 | 2 |
| Classification Data - True Negative (d) | 111 | 111 | 111 | 74 | 74 | 74 | 59 | 59 | 59 | 91 | 91 | 91 | 56 | 56 | 56 |
| Area Under the Curve (AUC) | 0.86 | 0.86 | 0.86 | 0.80 | 0.80 | 0.80 | 0.90 | 0.90 | 0.90 | 0.89 | 0.89 | 0.89 | 0.90 | 0.90 | 0.90 |
| AUC Estimate’s 95% Confidence Interval: Lower Bound | 0.78 | 0.78 | 0.78 | 0.72 | 0.72 | 0.72 | 0.82 | 0.82 | 0.82 | 0.83 | 0.83 | 0.83 | 0.82 | 0.82 | 0.82 |
| AUC Estimate’s 95% Confidence Interval: Upper Bound | 0.94 | 0.94 | 0.94 | 0.88 | 0.88 | 0.88 | 0.97 | 0.97 | 0.97 | 0.95 | 0.95 | 0.95 | 0.97 | 0.97 | 0.97 |
| Statistics | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Base Rate | 0.08 | 0.08 | 0.08 | 0.20 | 0.20 | 0.20 | 0.13 | 0.13 | 0.13 | 0.23 | 0.23 | 0.23 | 0.13 | 0.13 | 0.13 |
| Overall Classification Rate | 0.89 | 0.89 | 0.89 | 0.78 | 0.78 | 0.78 | 0.97 | 0.97 | 0.97 | 0.90 | 0.90 | 0.90 | 0.89 | 0.89 | 0.89 |
| Sensitivity | 0.70 | 0.70 | 0.70 | 0.65 | 0.65 | 0.65 | 0.89 | 0.89 | 0.89 | 0.68 | 0.68 | 0.68 | 0.78 | 0.78 | 0.78 |
| Specificity | 0.91 | 0.91 | 0.91 | 0.81 | 0.81 | 0.81 | 0.98 | 0.98 | 0.98 | 0.97 | 0.97 | 0.97 | 0.90 | 0.90 | 0.90 |
| False Positive Rate | 0.09 | 0.09 | 0.09 | 0.19 | 0.19 | 0.19 | 0.02 | 0.02 | 0.02 | 0.03 | 0.03 | 0.03 | 0.10 | 0.10 | 0.10 |
| False Negative Rate | 0.30 | 0.30 | 0.30 | 0.35 | 0.35 | 0.35 | 0.11 | 0.11 | 0.11 | 0.32 | 0.32 | 0.32 | 0.22 | 0.22 | 0.22 |
| Positive Predictive Power | 0.39 | 0.39 | 0.39 | 0.47 | 0.47 | 0.47 | 0.89 | 0.89 | 0.89 | 0.86 | 0.86 | 0.86 | 0.54 | 0.54 | 0.54 |
| Negative Predictive Power | 0.97 | 0.97 | 0.97 | 0.90 | 0.90 | 0.90 | 0.98 | 0.98 | 0.98 | 0.91 | 0.91 | 0.91 | 0.97 | 0.97 | 0.97 |
| Sample | Grade 2 | Grade 2 | Grade 2 | Grade 3 | Grade 3 | Grade 3 | Grade 4 | Grade 4 | Grade 4 | Grade 5 | Grade 5 | Grade 5 | Grade 6 | Grade 6 | Grade 6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||
| Sample Size | 132 | 132 | 132 | 114 | 114 | 114 | 69 | 69 | 69 | 122 | 122 | 122 | 71 | 71 | 71 |
| Geographic Representation | Pacific (CA, OR) West North Central (MO) |
Pacific (CA, OR) West North Central (MO) |
Pacific (CA, OR) West North Central (MO) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (CA, OR) West North Central (KS) |
Pacific (OR) West North Central (KS, MO) |
Pacific (OR) West North Central (KS, MO) |
Pacific (OR) West North Central (KS, 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 |
Reliability
| Grade |
Grade 2
|
Grade 3
|
Grade 4
|
Grade 5
|
Grade 6
|
|---|---|---|---|---|---|
| Rating |
|
|
|
|
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated 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. Three types of reliability are reported in the table below, alternate-form reliability, alpha, and inter-rater reliability. Alternate-form reliability is the correlation between different forms of the Computation measure. High alternate-form reliability coefficients suggest that these multiple forms are measuring the same construct. Coefficient alpha is a measure of reliability that is widely used in education research and represents the proportion of true score to total variance. Alpha incorporates information about the average inter-test correlation as well as the number of tests. Inter-rater reliability indicates the extent to which results generalize across assessors scoring the measure.
- *Describe the sample(s), including size and characteristics, for each reliability analysis conducted.
- Alternate-form reliability and alpha were calculated from data collected during the 2018-2019 school year and entered in Acadience Data Management. There were 146,297 students in this sample from 1,339 schools in 606 districts in 46 states. Demographic information is not available for this sample. Inter-rater reliability was calculated from two samples. Data was collected for second, fourth, and fifth grade Computation during the 2012-2013 school year. There were 188 students in this sample from four schools in four districts in four US states. Inter-rater reliability for third and sixth grade Computation was collected during the 2014–2015, 2015–2016, and 2016–2017 school years. There were 264 students across 24 schools in 12 districts in 8 US states in this sample.
- *Describe the analysis procedures for each reported type of reliability.
- Alternate-form reliability is reported as the correlation between two alternate forms of the Computation measures. Coefficient alpha treats each of the two tests as separate indicators and is calculated using the alternate-form reliability, where the number of tests is equal to two. To calculate inter-rater reliability, photocopies were made of unscored student worksheets. The two copies (original and photocopy) were scored separately and independently. The inter-rater reliability coefficient is the correlation between the total scores from these two independently scored assessments.
*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:
- Yes
- Provide citations for additional published studies.
- Gray, J. S., Warnock, A. N., Dewey, E. N., Latimer, R., & Wheeler, C. E. (2019) Acadience™ Math Technical Adequacy Brief. Eugene, OR: Acadience Learning Inc.
- 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
|
|---|---|---|---|---|---|
| Rating |
|
|
|
|
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- *Describe each criterion measure used and explain why each measure is appropriate, given the type and purpose of the tool.
- The Stanford Achievement Test Series, Tenth Edition–Total Math score (SAT10; Pearson, 2003) was used as the external criterion. The SAT10 is a widely used, timed, group-administered, norm-referenced achievement test appropriate for children in kindergarten through grade 12. In second through sixth grade, the SAT10 Total Math score includes scores from the subtests of Mathematics Problem Solving and Mathematics Procedures. Students are given 80 minutes in total to complete both subtests. The SAT10 Total Math score was compared to all Acadience Math measures given during the year, providing both predictive criterion-related validity correlations for beginning- and middle-of-year measures and concurrent criterion-related validity data for end-of-year measures.
- *Describe the sample(s), including size and characteristics, for each validity analysis conducted.
- Validity data were collected during the 2017–18 school year. This sample included 530 students across five schools in four districts in four US states. Demographic information is not available for this sample.
- *Describe the analysis procedures for each reported type of validity.
- Predictive validity is the correlation between Computation at the beginning of the year and the SAT10 score at the end of the school year. This coefficient represents the extent to which Computation can predict later math outcomes. Concurrent validity is the correlation between the Computation score and the SAT10 measure both at the end of the year. This coefficient represents the extent to which the Computation score is related to important math outcomes.
*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:
- Yes
- Provide citations for additional published studies.
- Gray, J. S., Warnock, A. N., Dewey, E. N., Latimer, R., & Wheeler, C. E. (2019) Acadience™ Math Technical Adequacy Brief. Eugene, OR: Acadience Learning Inc.
- Describe the degree to which the provided data support the validity of the tool.
- Both the concurrent and predictive correlation are generally high. These strong correlations suggest that the Acadience Math Computation measure is assessing skills relevant to math outcomes. Given the wide range of skills assessed on the SAT10, these data support the conclusion that the Computation measure is an excellent indicator of math proficiency.
- 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
|
|---|---|---|---|---|---|
| Rating | Yes | Yes | Yes | Yes | No |
- 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:
- Bias was conceptualized as different classification accuracy between different groups. This was assessed using a Cleary model, with the outcome being performance on the Group Mathematics Assessment and Diagnostic Evaluation (GMADE; Williams, 2004), where the Computation score, subgroup , and the interaction between the two were used as predictors. The GMADE is a group-administered, norm-referenced test appropriate for children in kindergarten through grade 12. In second through fifth grade, the GMADE includes scores from the subtests of Concepts and Communications, Operations and Computation, and Process and Applications. If a model with the subgroup and interaction term do not add significantly to model fit, there is evidence that the Computation measure is not biased. The effect size for bias was assessed using the model change in R-squared. This procedure was conducted in grades 2, 3, 4, and 5 (data were unavailable at grade 6). These models were built for each time of year at each grade under consideration. This resulted in 3 (times of year) X 2 (subgroup comparisons) X 4 (number of grades) = 24 total models being compared.
- b. Describe the subgroups for which bias analyses were conducted:
- Bias was assessed among white and non-white students and students who do and do not qualify for free/reduced lunch.
- 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.
- Of the 12 models assessing racial bias, 2 were statistically significant (grade 2 middle of year and grade 5 middle of year). The largest change in R-squared was .025. Of the 12 models assessing bias in free/reduced lunch, again 2 were statistically significant (grade 2 beginning and middle of year). The largest R-squared here was .043. Overall, the rate of significant models is not much higher than the nominal Type I error rate of .05, and the change in R-squared values indicates that if bias exists, the extent is small.
Data Collection Practices
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