AIMSweb

Test of Early Numeracy - Oral Counting

 

Cost

Technology, Human Resources, and Accommodations for Special Needs

Service and Support

Purpose and Other Implementation Information

Usage and Reporting

AIMSweb Test of Early Numeracy (TEN) is included in a subscription to AIMSweb Pro Math, or AIMSweb Pro Complete, which range from $4.00 to $6.00 per student per year.

Every AIMSweb subscription provides unlimited access to the AIMSweb online system, whichincludes:

  • AIMSweb assessments for universal screening and progress monitoring
  • Data management and reporting
  • Browser-based scoring
  • Training manuals
  • Administration and scoring manuals

Internet access is required for full use of product services.

Testers will require 2 – 4 hours of training.

Paraprofessionals can administer the test.
 

Pearson
19500 Bulverde Road
San Antonio, TX 78259
Phone: 866-313-6194
Visit AIMSweb.com

General Information:
866-313-6194 option 2
sales@aimsweb.com

Tech support:
866-313-6194 option 1 aimswebsupport@ pearson.com

Access to field tested training manuals are included with AIMSweb subscriptions which provide administration, scoring, and implementation information.

Ongoing technical support is provided. 

Professional Development opportunities are available.

Test of Early Numeracy (TEN) measures consist of:

Oral counting: Student counts aloud for 1 minute

Number Identification: Student names numbers up to 10 (or 20) presented in random order for 1 minute

Quantity Discrimination: Students indicates which of two numbers up to 10 (or 20) is greater for 1 minute

Missing Number: Student says the value of the missing number from a sequence of three numbers

33 standardized and individually administered alternate forms per grade for Kindergarten and grade 1.

Raw score, national percentiles (K and grade 1) and normative performance levels by grade and season, individual student growth percentiles by grade and season (based on rates of improvement, ROI), and success probability scores (cut scores that indicate a 50% or 80% probability of passing the state test).  Local norms are also available.

 

Reliability of the Performance Level Score

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Type of Reliability Age or Grade n (range) Coefficient Information (including normative data)/Subjects
Median
Inter-scorer 1st   0.78 Clarke and Shinn (2004)
Alternate form (Fall) 1st   0.99 Clarke and Shinn (2004)
Test-retest (2 weeks) 1st   0.93 Clarke and Shinn (2004)
Test-retest (13 weeks) 1st   0.77 Clarke and Shinn (2004)
Test-retest (16 weeks) 1st   0.80 Clarke and Shinn (2004)

 

Reliability of the Slope

GradeK1
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Type of Reliability Age or Grade n (range) Coefficient Information (including normative data)/Subjects
range median
Ratio of Observed Variance to True Variance estimated through HLM. Grade K aggregated sample across ethnicities. 242 0.84 Reliability of slope for Total sample = 0.84. Reliability of slope based on 10 observational data points collected over the course of 10 weeks (i.e., one data point per week).

Validity of the Performance Level Score

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Type of Validity Age or Grade Test or Criterion Coefficient
Median
Concurrent 1st Number Knowledge Test (Fall) 0.70
Concurrent 1st Woodcock Johnson Math Applications Test (Winter) 0.64
Concurrent 1st M-CBM (Winter) 0.49
Concurrent 1st Woodcock Johnson Math Applications Test (Spring) 0.60
Concurrent 1st M-CBM (Spring) 0.50
Predictive 1st CBM-M (Winter) 0.56
Predictive 1st CBM-M (Spring) 0.56
Predictive 1st Woodcock Johnson Math Applications Test (Spring) 0.72

Predictive Validity of the Slope of Improvement

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Type of Validity Age or Grade Test or Criterion n (range) Coefficient Information (including normative data)/Subjects
range median
Predictive Kindergarten Grade 1 M-CBM Spring Benchmark 242 0.69 Predictive validity of slope for Total sample = 0.69. Reliability of slope based on 10 data points collected over the course of 10 weeks (i.e., one data point per week).

 

Bias Analysis Conducted

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RatingNoNo

Disaggregated Reliability and Validity Data

GradeK1
RatingYesNo

Disaggregated Validity of the Performance Level Score

Type of Reliability Age or Grade n (range) Coefficient Information (including normative data)/Subjects
range median
Ratio of Observed Variance to True Variance estimated through HLM. Grade K disaggregated by ethnicity. 242 0.41 to 0.91 Reliability of slope for Caucasian subsample = 0.82. Reliability of slope for African American subsample = 0.41 (likely due to very small subsample). Reliability of slope for Hispanic subsample  = 0.91. Reliability of slope based on 10 observational data points collected over the course of 10 weeks (i.e., one data point per week).

Disaggregated Predictive Validity of the Slope of Improvement

Type of Validity Age or Grade Test or Criterion n (range) Coefficient Information (including normative data)/Subjects
range median
Predictive Kindergarten Grade 1 M-CBM Spring Benchmark. 242 0.63 to 0.70 Predictive validity of slope for Caucasian sample = 0.69. Reliability of slope for African American sample = 63. Reliability of slope for Hispanic sample = 0.70. Reliability of slope based on 10 data points collected over the course of 10 weeks (i.e. one data point per week).

 

Alternate Forms

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1. Evidence that alternate forms are of equal and controlled difficulty or, if IRT based, evidence of item or ability invariance:

Early Numeracy Curriculum-Based Measurement Reliability for All Testing Sessions

EN-CBM Inter-Scorer Alternate-Form
(Fall)
Alternate Forms
(Winter)
Test-retest
(2 weeks)
Test-retest
(13 weeks)
Test-retest
(26 weeks)
Oral Counting 0.78 0.99 -- 0.93 0.77 0.80

2. Number of alternate forms of equal and controlled difficulty:

30 alternate forms in grade K and 1.

Rates of Improvement Specified

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

Yes.

a. Specify the growth standards:

Grade Percentile ROI
K 90 0.6
75 0.7
50 0.8
25 0.7
10 0.7
Mean 0.7
1 90 0.2
75 0.6
50 0.8
25 0.7
10 0.8
Mean 0.6

 

b. Basis for specifying minimum acceptable growth:

Criterion-referenced and Norm-referenced.

2. Normative profile:

Representation: National
Date: 2001-2008
Number of States: 49 & DC

3. Procedure for specifying criterion for adequate growth:

AIMSweb TEN will derive its standards for adequate growth in two ways. First, Rates of Improvement (ROI) are calculated using the composite normative sample of AIMSweb customers. Year 1 normative data are being compiled with information on relative standing and rates of improvement provided in continuously updated normative tables as shown on the next pages. Spring AIMSweb TEN data are currently being collected. Second, AIMSweb users will be able to identify their own criterion-referenced rates of progress by linking their AIMSweb TEN scores to Mathematics CBM (M-CBM) measures and/or their state-mandated high stakes mathematics tests. See (Hintze, Ryan, & Stoner, 2003) as an example of how empirical linkages can be used for goal setting in CBM.

By establishing the predictive relationship between TEN and any accepted high-stakes criterion test, AIMSweb users can use the score that predicts passing with an 80% or 90% probability.

End-of-Year Benchmarks

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

Yes.

a. Specify the end-of-year performance standards:

Customers can 1) Define their own benchmark targets based on norm tables or other data, 2) Use AIMSweb presets, which are based on the score at the 50th percentile from the AIMSweb National Norms; 3) Use DIBELS presets, or 4) Use the AIMSweb test correlation feature to generate benchmark targets that predict success on high stakes testing.

b. Basis for specifying minimum acceptable end-of-year performance:

Norm-referenced and criterion-referenced

Normative profile:
Representation: National
Date: 2001-2008
Number of States: 49 & DC
Size: 315,866

Sensitive to Student Improvement

GradeK1
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1. Describe evidence that the monitoring system produces data that are sensitive to student improvement (i.e., when student learning actually occurs, student performance on the monitoring tool increases on average).

In order to assess the sensitivity of the AIMSWeb TENS to student improvement, data from one year were analyzed for students in Kindergarten and 1st grade who were receiving Tier 2 RTI supplemental instruction. These students were drawn from an overall sample of 320 students attending two primary schools in the northeast. Students were chosen for Tier 2 intervention based on their TENS scores in universal screening at one of the three benchmark testing periods (fall, winter, and spring).

The intervention was provided four times a week by a remedial math teacher, in sessions of 30 to 40 minutes.The structured intervention, Number Worlds, is ungraded but is differentiated by level. Students in Kindergarten were provided with intensified instruction at Levels A through C, which focus on counting and conceptual structure for single-digit numbers, and the relationship of number concepts to the formal symbol system. Students in grade 1 were provided intensified instruction at Level D, which addresses number sense, number pattern and relationship, addition, subtraction, geometry, measurement, and data analysis and applications.

A series of single-sample t tests were computed at each grade level and for each TENS measure comparing the average rate of improvement (ROI) for students in intervention to the mean ROI for students in general education who were not receiving supplemental intervention.

Results were significant (p < 0.05) for each measure at the Kindergarten level. On the Oral Counting measure, RTI students (n=24) improved at a faster rate than their general education peers by 3.00 numbers correct per minute per week.

Results were significant (p < 0.05) at Grade 1 for the Oral Counting measure. On the Oral Counting measure, RTI students (n=27)improved at a faster rate than their general education peers by 0.71 numbers correct per minute per week.

Results of these analyses provide evidence that the AIMSWeb TEN assessment measures are sensitive to validated interventions.

Decision Rules for Changing Instruction

GradeK1
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Specification of validated decision rules for when changes to instruction need to be made: The newest version of the AIMSweb online system, to be released for piloting in the fall of 2012 and made available to all users no later than the fall of 2013, applies a statistical procedure to the student’s monitoring scores in order to provide empirically-based guidance about whether the student is likely to meet, fall short of, or exceed their goal. The calculation procedure (presented below) is fully described in the AIMSweb Progress Monitoring Guide (Pearson, 2012) and can be implemented immediately by AIMSweb users if they create a spreadsheet or simple software program. Once the new AIMSweb online system is fully distributed, the user will not have to do any calculations to obtain this data-based guidance. The decision rule is based on a 75% confidence interval for the student’s predicted score at the goal date. This confidence interval is student-specific and takes into account the number and variability of monitoring scores and the duration of monitoring. Starting at the sixth week of monitoring, when there are at least four monitoring scores, the AIMSweb report following each monitoring administration includes one of the following statements: “The student is projected to not reach the goal.” This statement appears if the confidence interval is completely below the goal score. “The student is projected to exceed the goal.” This statement appears if the confidence interval is completely above the goal score. “The student is on track to reach the goal. The projected score at the goal date is between X and Y” (where X and Y are the bottom and top of the confidence interval). This statement appears if the confidence interval includes the goal score. If Statement A appears, the user has a sound basis for deciding that the current intervention is not sufficient and a change to instruction should be made. If Statement B appears, there is an empirical basis for deciding that the goal is not sufficiently challenging and should be increased. If Statement C appears, the student’s progress is not clearly different from the aimline and so there is not a compelling reason to change the intervention or the goal; however, the presentation of the confidence-interval range enables the user to see whether the goal is near the upper limit or lower limit of the range, which would signal that the student’s progress is trending below or above the goal.  A 75% confidence interval was chosen for this application because it balances the costs of the two types of decision errors. Incorrectly deciding that the goal will not be reached (when in truth it will be reached) has a moderate cost: an intervention that is working will be replaced by a different intervention. Incorrectly deciding that the goal may be reached (when in truth it will not be reached) also has a moderate cost: an ineffective intervention will be continued rather than being replaced. Because both kinds of decision errors have costs, it is appropriate to use a modest confidence level.
 
Calculation of the 75% confidence interval for the score at the goal date. Calculate the trend line. This is the ordinary least-squares regression line through the student’s monitoring scores. Calculate the projected score at the goal date. This is the value of the trend line at the goal date. Calculate the standard error of estimate (SEE) of the projected score at the goal date, using the following formula: [((1 + 1/k + (GW – mean(w)))/(k – 2))((sum(y – y’)2)/(sum(w – mean(w))2))]1/2 where k = number of completed monitoring administrations w = week number of a completed administration GW = week number of the goal date y = monitoring score y’ = predicted monitoring score at that week (from the student’s trendline). The means and sums are calculated across all of the completed monitoring administrations up to that date. Add and subtract 1.25 times the SEE to the projected score, and round to the nearest whole numbers.
 
Evidentiary basis for these decision rules: The decision rules are statistically rather than empirically based. The guidance statements that result from applying the 75% confidence interval to the projected score are correct probabilistic statements, under certain assumptions: The student’s progress can be described by a linear trend line. If the pattern of the student’s monitoring scores is obviously curvilinear, then the projected score based on a linear trend will likely be misleading. We provide training in the AIMSweb Progress Monitoring Guide about the need for users to take non-linearity into account when interpreting progress-monitoring data. The student will continue to progress at the same rate as they have been progressing to that time. This is an unavoidable assumption for a decision system based on extrapolating from past growth. Even though the rules are not derived from data, it is useful to observe how they work in a sample of real data. For this purpose, we selected random samples of students in the AIMSweb 2010-2011 database who were progress-monitored on either Reading Curriculum-Based Measurement (R-CBM) or Math Computation (M-COMP). All students scored below the 25th percentile in the fall screening administration of R-CBM or M-COMP. The R-CBM sample consisted of 1,000 students (200 each at grades 2 through 6) who had at least 30 monitoring scores, and the M-COMP sample included 500 students (100 per grade) with a minimum of 28 monitoring scores. This analysis was only a rough approximation, because we did not know each student’s actual goal or whether the intervention or goal was changed during the year. To perform the analyses, we first set an estimated goal for each student by using the ROI at the 85th percentile of AIMSweb national ROI norms to project their score at their 30th monitoring administration. Next, we defined “meeting the goal” as having a mean score on the last three administrations (e.g., the 28th through 30th administrations of R-CBM) that was at or above the goal score. At each monitoring administration for each student, we computed the projected score at the goal date and the 75% confidence interval for that score, and recorded which of the three decision statements was generated (projected not to meet goal, projected to exceed goal, or on-track/no-change).
 
In this analysis, accuracy of guidance to change (that is, accuracy of projections that the student will not reach the goal or will exceed the goal) reached a high level (80%) by about the 13th to 15th monitoring administration, on average. The percentage of students receiving guidance to not change (i.e., their trendline was not far from the aimline) would naturally tend to decrease over administrations as the size of the confidence interval decreased. At the same time, however, there was a tendency for the trendline to become closer to the aimline over time as it became more accurately estimated, and this worked to increase the percentage of students receiving the “no change” guidance. 

Decision Rules for Increasing Goals

GradeK1
RatingFull bubbleFull bubble
Specification of validated decision rules for when increases in goals need to be made: The newest version of the AIMSweb online system, to be released for piloting in the fall of 2012 and made available to all users no later than the fall of 2013, applies a statistical procedure to the student’s monitoring scores in order to provide empirically-based guidance about whether the student is likely to meet, fall short of, or exceed their goal. The calculation procedure (presented below) is fully described in the AIMSweb Progress Monitoring Guide (Pearson, 2012) and can be implemented immediately by AIMSweb users if they create a spreadsheet or simple software program. Once the new AIMSweb online system is fully distributed, the user will not have to do any calculations to obtain this data-based guidance. The decision rule is based on a 75% confidence interval for the student’s predicted score at the goal date. This confidence interval is student-specific and takes into account the number and variability of monitoring scores and the duration of monitoring. Starting at the sixth week of monitoring, when there are at least four monitoring scores, the AIMSweb report following each monitoring administration includes one of the following statements: “The student is projected to not reach the goal.” This statement appears if the confidence interval is completely below the goal score. “The student is projected to exceed the goal.” This statement appears if the confidence interval is completely above the goal score. “The student is on track to reach the goal. The projected score at the goal date is between X and Y” (where X and Y are the bottom and top of the confidence interval). This statement appears if the confidence interval includes the goal score. If Statement A appears, the user has a sound basis for deciding that the current intervention is not sufficient and a change to instruction should be made. If Statement B appears, there is an empirical basis for deciding that the goal is not sufficiently challenging and should be increased. If Statement C appears, the student’s progress is not clearly different from the aimline and so there is not a compelling reason to change the intervention or the goal; however, the presentation of the confidence-interval range enables the user to see whether the goal is near the upper limit or lower limit of the range, which would signal that the student’s progress is trending below or above the goal.  A 75% confidence interval was chosen for this application because it balances the costs of the two types of decision errors. Incorrectly deciding that the goal will not be reached (when in truth it will be reached) has a moderate cost: an intervention that is working will be replaced by a different intervention. Incorrectly deciding that the goal may be reached (when in truth it will not be reached) also has a moderate cost: an ineffective intervention will be continued rather than being replaced. Because both kinds of decision errors have costs, it is appropriate to use a modest confidence level.
 
Calculation of the 75% confidence interval for the score at the goal date. Calculate the trend line. This is the ordinary least-squares regression line through the student’s monitoring scores. Calculate the projected score at the goal date. This is the value of the trend line at the goal date. Calculate the standard error of estimate (SEE) of the projected score at the goal date, using the following formula: [((1 + 1/k + (GW – mean(w)))/(k – 2))((sum(y – y’)2)/(sum(w – mean(w))2))]1/2 where k = number of completed monitoring administrations w = week number of a completed administration GW = week number of the goal date y = monitoring score y’ = predicted monitoring score at that week (from the student’s trendline). The means and sums are calculated across all of the completed monitoring administrations up to that date. Add and subtract 1.25 times the SEE to the projected score, and round to the nearest whole numbers.
 
Evidentiary basis for these decision rules: The decision rules are statistically rather than empirically based. The guidance statements that result from applying the 75% confidence interval to the projected score are correct probabilistic statements, under certain assumptions: The student’s progress can be described by a linear trend line. If the pattern of the student’s monitoring scores is obviously curvilinear, then the projected score based on a linear trend will likely be misleading. We provide training in the AIMSweb Progress Monitoring Guide about the need for users to take non-linearity into account when interpreting progress-monitoring data. The student will continue to progress at the same rate as they have been progressing to that time. This is an unavoidable assumption for a decision system based on extrapolating from past growth. Even though the rules are not derived from data, it is useful to observe how they work in a sample of real data. For this purpose, we selected random samples of students in the AIMSweb 2010-2011 database who were progress-monitored on either Reading Curriculum-Based Measurement (R-CBM) or Math Computation (M-COMP). All students scored below the 25th percentile in the fall screening administration of R-CBM or M-COMP. The R-CBM sample consisted of 1,000 students (200 each at grades 2 through 6) who had at least 30 monitoring scores, and the M-COMP sample included 500 students (100 per grade) with a minimum of 28 monitoring scores. This analysis was only a rough approximation, because we did not know each student’s actual goal or whether the intervention or goal was changed during the year. To perform the analyses, we first set an estimated goal for each student by using the ROI at the 85th percentile of AIMSweb national ROI norms to project their score at their 30th monitoring administration. Next, we defined “meeting the goal” as having a mean score on the last three administrations (e.g., the 28th through 30th administrations of R-CBM) that was at or above the goal score. At each monitoring administration for each student, we computed the projected score at the goal date and the 75% confidence interval for that score, and recorded which of the three decision statements was generated (projected not to meet goal, projected to exceed goal, or on-track/no-change).
 
In this analysis, accuracy of guidance to change (that is, accuracy of projections that the student will not reach the goal or will exceed the goal) reached a high level (80%) by about the 13th to 15th monitoring administration, on average. The percentage of students receiving guidance to not change (i.e., their trendline was not far from the aimline) would naturally tend to decrease over administrations as the size of the confidence interval decreased. At the same time, however, there was a tendency for the trendline to become closer to the aimline over time as it became more accurately estimated, and this worked to increase the percentage of students receiving the “no change” guidance.

Improved Student Achievement

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Improved Teacher Planning

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