You are here
Home ›AIMSweb
Cost 
Technology, Human Resources, and Accommodations for Special Needs 
Service & Support 
Purpose & Other Implementation Information 
Usage & Reporting 
AIMSweb Test of Early Literacy (TEL) is included in a subscription to AIMSweb Pro Reading, AIMSweb Pro Language Arts, 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, which includes:

Internet access is required for full use of product services. Testers will require 2 – 4 hours of training. Paraprofessionals can administer the test. 
Pearson 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 Literacy (TEL) measures consist of: Letter Naming Fluency: Student names printed upper and lower case letters (presented in random order) for 1 minute. Letter Sound Fluency: Student produces the sound for upper and lower letters presented in print (and in random order for 1 minute. Phoneme Segmentation Fluency: Student segments orally presented words into phonemes for 1 minute. Nonsense Word Fluency: Student reads nonsense words for 1 minute. 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. 
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.  666  0.86  Reliability of slope for Total sample = 0.91.  Reliability of slope based on 15 observational data points collected over the course of 15 weeks (i.e., one data point per week). 
Type of Reliability  Age or Grade  n (range)  Coefficient  Information (including normative data)/Subjects  

range  median  
Alternate Form  1st Grade  77231  0.670.88  0.83  Data collected during 7 points in time during one academic year. 
Type of Validity  Age or Grade  Test or Criterion  n (range)  Coefficient  Information (including normative data)/Subjects  

range  median  
Concurrent  1st Grade  Woodcock Johnson Readiness  62126  0.350.59  0.31  Data collected at 8 points during one academic year 
Concurrent  1st Grade  StanfordBinet Verbal Reasoning  0147  0.170.40  0.31  Data collected at 8 points during one academic year 
Concurrent  1st Grade  StanfordBinet Verbal Reasoning  0147  0.210.37  0.32  Data collected at 8 points during one academic year 
Predictive  1st Grade  May of 1st grade CBMR  70242  0.680.82  0.73  Data collected at 8 points during one academic year 
Predictive  1st Grade  Feb of 2nd grade CBMR  5258  0.630.85  0.74  Data collected at 8 points during one academic year 
Predictive  1st Grade  May of 2nd grade WJ Total Reading Cluster  58116  0.520.77  0.87  Data collected at 8 points during one academic year 
Predictive  1st Grade  May of 2nd grade CBMR  5157  0.600.85  0.77  Data collected at 8 points during one academic year 
Type of Validity  Age or Grade  Test or Criterion  n (range)  Coefficient  Information (including normative data)/Subjects  

range  median  
Predictive  Kindergarten  Grade 1 RCBM Spring Benchmark  666  0.72  Predictive validity of slope for Total sample = 0.72.  Reliability of slope based on 15 data point collected over the course of 15 weeks (i.e., one data point per week). 
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. 
666 
0.83 to 0.87 
Reliability of slope for Caucasian subsample = 0.87. Reliability of slope for African American subsample = 0.83. Reliability of slope for Hispanic subsample = 0.86. 
Reliability of slope based on 15 observational data points collected over the course of 15 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 RCBM Spring Benchmark. 
666 
0.69 to 0.72 
Predictive validity of slope for Caucasian sample = 0.72. Reliability of slope for African American sample = 0.69. Reliability of slope for Hispanic sample = 0.70. 
Reliability of slope based on 15 data points collected over the course of 15 weeks (i.e. one data point per week). 
1. Evidence that alternate forms are of equal and controlled difficulty or, if IRT based, evidence of item or ability invariance:
Direct evidence for equal and controlled difficulty is provided by field testing with a group of 85 kindergarten students from diverse communities in 2 school districts in Illinois. Trained examiners administered 3 of 5 randomly sampled AIMSweb NWF measures (Forms 5, 10, 17, 21 and 30) from the pool of all AIMSweb PSF measures in a blocked manner. For example, Student 1 read Forms 5, 10, and 17. Another subject was given Forms 10, 17, and 21. This blocked approach resulted in 4857 sets of 3 comparison scores.
Means, Standard Deviations and Medians are shown in the accompanying table. Analysis of Variance showed no reliable differences among Forms (p = 0.370)
Form 5  Form 10  Form 17  Form 21  Form 30  

Mean  40.7  46.4  45.3  42.5  36.8 
SD  24.1  29.9  31.6  25.9  18.1 
Median  35.0  40.0  36.0  36.0  34.0 
Median alternate form reliability is 0.95.
Correlations among forms are shown in the accompanying table.
Form 5  Form 10  Form 17  Form 21  

Form 10  0.95  
Form 17  0.94  0.95  
Form 21  0.74  0.97  0.97  
Form 30  0.93  0.99  0.92  0.87 
2. Number of alternate forms of equal and controlled difficulty:
30 alternate forms in grade K and 1.
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 TELS, data from a 2year period were analyzed for students in Kindergarten and 1st grade from students who were receiving Tier 2 RTI supplemental instruction. The intervention for students in both grades consisted of intensified instruction and intervention in phonemic segmentation, alphabetic principle, decoding, encoding, word analysis, vocabulary development, sight word recognition, fluency, and comprehension (i.e., Wilson Fundations). A series of singlesample t tests were computed at each grade level and for each TELS measure comparing the average rate of improvement for students in intervention to the mean rate of improvement for students in general education and not receiving supplemental intervention. Results were significant (p < .01) for each measure at each grade level.
More specifically, on the NWF measure, students receiving RTI support outperformed their general education Tier 1 peers by 2.72 and 2.31 in Kindergarten and 1st grade, respectively. The sample sizes were 78 for Kindergarten and 43 for 1st grade.
Results of these analyses provide evidence that the AIMSWeb TELS assessment measures are sensitive to validated interventions.
1. Are benchmarks for minimum acceptable endofyear performance specified in your manual or published materials?
Yes.
a. Specify the endofyear 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 endofyear performance:
Normreferenced and criterionreferenced
Normative profile:
Representation: National
Date: 20012008
Number of States: 49 & DC
Size: 264,758
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  ROI 

K  0.72 
1  0.92 
b. Basis for specifying minimum acceptable growth:
Criterionreferenced and normreferenced
2. Normative profile:
Representation: National
Date: 20012008
Number of States: 49 & DC
Size: 264, 758
3. Procedure for specifying criterion for adequate growth:
All Early Literacy measures derive its standards for adequate growth in two ways. First, Rates of Improvement (ROI) are calculated using a composite normative sample of AIMSweb customers, compiled since 2002 when the system was launched. AIMSweb does report by AYP categories. Educators are able to track progress of individual subgroups. Second, AIMSweb users can identify their own criterionreferenced rates of progress by linking their AIMSweb Early Literacy probes to AIMSweb RCBM scores and statemandated high stakes reading tests.
Additionally, AIMSweb users can identify their own criterionreferenced rates of progress that predict passing with an 80% or 90% probability by linking their AIMSweb Early Literacy scores to AIMSweb RCBM scores and statemandated high stakes reading tests.
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 empiricallybased 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 databased 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 studentspecific 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 confidenceinterval 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 leastsquares 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 nonlinearity into account when interpreting progressmonitoring 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 20102011 database who were progressmonitored on either Reading CurriculumBased Measurement (RCBM) or Math Computation (MCOMP). All students scored below the 25^{th} percentile in the fall screening administration of RCBM or MCOMP. The RCBM sample consisted of 1,000 students (200 each at grades 2 through 6) who had at least 30 monitoring scores, and the MCOMP 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 85^{th} percentile of AIMSweb national ROI norms to project their score at their 30^{th} monitoring administration. Next, we defined “meeting the goal” as having a mean score on the last three administrations (e.g., the 28^{th} through 30^{th} administrations of RCBM) 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 ontrack/nochange).
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 13^{th} to 15^{th} 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.
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 empiricallybased 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 databased 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 studentspecific 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 confidenceinterval 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 leastsquares 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 nonlinearity into account when interpreting progressmonitoring 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 20102011 database who were progressmonitored on either Reading CurriculumBased Measurement (RCBM) or Math Computation (MCOMP). All students scored below the 25^{th} percentile in the fall screening administration of RCBM or MCOMP. The RCBM sample consisted of 1,000 students (200 each at grades 2 through 6) who had at least 30 monitoring scores, and the MCOMP 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 85^{th} percentile of AIMSweb national ROI norms to project their score at their 30^{th} monitoring administration. Next, we defined “meeting the goal” as having a mean score on the last three administrations (e.g., the 28^{th} through 30^{th} administrations of RCBM) 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 ontrack/nochange).
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 13^{th} to 15^{th} 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.