Teachers Need Feedback

The first semester End-of-Course and NCFE testing are probably completed in your schools and you your team is busy examining the scores and thinking about what can be done to improve student performance for the end of year testing. Teachers need to know how they performed and what changes need to be made to improve their instruction.

Here are five tasks I assist my clients with to improve student outcomes:

  1. Match your EVAAS predicted score to the EOC score and estimate growth, so you know who your strong teachers are and who may need support. These scores are in the form of projected percentile rank scores. For individual students, you can just compare the differences between the achieved percentile rank and the EVAAS projected percentile rank. Be warned, however, that a 5 percentile rank difference near the mean is interpreted differently than at the extremes of the scale. Furthermore, if you want to aggregate the data into a class unit, you will need to convert the percentile rank scores into Normal Curve Equivalent (NCE) scores.
  2. Print the Teacher Goal Summary reports so that the teachers can see how the class did on the curriculum areas. Better yet, export the WinScan file with the sub-scores and other test data and examine the disaggregated averages sub-score data by achievement levels and subgroups. 
  3. Match your EOC scores with benchmark scores, including sub-scores and determine the strength of the relationship between the sets of scores. Ask: Are the benchmarks “predictive” of the EOC performance? What sub-scores on the benchmark are more predictive? 
  4. Create a predictive statistical model using regression analysis, which includes benchmark scores, in the dataset to identify students who may need support in the upcoming semester. 
  5. Most importantly, share the information with teachers, and provide training so that they understand the information.       

Contact me if you would like more information about what we do with student data or wish to discuss how Data Smart LLC can improve student data management and student achievement.

Cohort Reporting

Cohort Reporting is a Necessity

Most schools and school districts report the summative data by grade for the year, and may sometimes report the previous year’s data as a comparison. Unfortunately, this does not provide an accurate picture of student performance in the district. The problem of incomplete or non-comparative data is due to:

  1. The data are not the same student group and differences in the groups’ pre-performance from year to year may cause variations.
  2. Percent proficiency scores vary across grades, so that a grade 4 group may actually do better than their grade 3 performance, but the percent proficient declines.
  3. The inability of the district’s data system to track students’ performance across time as a cohort.

To solve this problem, three changes in the way data are stored and coded are necessary.

Requirements

First, student scores cannot be stored in separate files, one for each year of testing.

Second, student scores need to be transformed into a common scale which will make them comparable across time. Percentile ranks are not the answer, instead all percentile rank scores need to be transformed to normal curve equivalent (NCE) scores, while keeping the percentile rank scores in the data file. Third, a kindergarten entry date or a first date of testing in grade 3 added to the data file is necessary for ease in writing queries for reports. So for each year the student scores are uploaded into a data system the same student ID number has the same K_ENTRY date. Therefore, a chart of the data would look like this:

The Report 

School 000 is a school with an increasing number of students achieving proficiency and a very high NCE Difference EVAAS Gain. Whereas, the 001 school has a small drop in proficiency, not enough to raise concerns, however the school also has a declining EVAAS Gain Score for both RD and MA for grade 5.

Mid-Year Data Review

The first semester End-of-Course and NCFE testing are probably completed in your schools and you your team is busy examining the scores and thinking about what can be done to improve student performance for the end of year testing. 
Here are five  tasks I assist my clients with to improve student outcomes :

Match your EVAAS predicted score to the EOC score and estimate growth, so you know who your strong teachers are and who may need support.

Match your EOC scores with benchmark scores, including sub-scores and determine the strength of the relationship between the sets of scores. Ask: Are the benchmarks “predictive” of the EOC performance? What sub-scores on the benchmark are more predictive?

Create a predictive statistical model, which includes benchmark scores, in the data set to identify students who may need support in the upcoming semester. 

If the assessments have a high relationship with the EOC, keep the assessmants secure. If the assessment scores  are not well correlated to the EOC score then tweak the assessment, using item analysis to identify those “weak” items. 

Share the information with teachers.       

Contact me if you would like more information about what we do with student data or wish to discuss how Data Smart LLC can improve student data management and student achievement.