This document discusses terminology and concepts related to measuring value-added in education. It defines key terms like baseline score, value-added scores, standardized residuals, and confidence limits. It provides an example of how value-added is measured for students with low, average, and high ability. Graphs and scatter plots are used to identify patterns in student performance and understand how data can be used to identify over and underachievers.

1. Value-Added Feedback
Reports & Graphs

2. Subject X
20
40
60
80
100
120
4 5 6 7 8
Baseline
Outcome
.
-ve VA+ve VA
Trend Line/Regression Line
Raw Residual
Measuring Value-Added – Terminology
Examgrade
BASELINE SCORE

3. Measuring Value-Added – An Example
Low Ability Average Ability High Ability
Baseline Score
A*
U
B
C
D
E
F
G
Result
Alf Bob
Chris+ve (+ 2 grades)
-ve (- 2 grades)
National Trend
‘Average’ Student
The position of the national trend line is of
critical importance
Subject A
Subject B

4. Standardisation of Residuals
• (Raw) Residuals can be used to examine an individual’s performance
• Standardised Residuals are used to compare performance of groups
• Standardised Residuals are independent of year or qualification type
• For a class, subject, department or whole institution the Average
Standardised Residual is the ‘Value-Added Score’
• Standardised Residual = Residual / Standard Deviation (National Sample)
• When using Standardised Residuals then for an individual subject
• 95% Confidence Limit = 2.0 x Standard Error
• 99% Confidence Limit = 2.6 x Standard Error
• 99.7% Confidence Limit = 3.0 x Standard Error
N
1
ErrorStandard = where N = number of results in the group
(for combinations of subjects consult the relevant project)

5. -0.2 -0.2 -0.1
0.1
-0.6
0.1
-0.2
-0.7 -0.7
-0.4 -0.4
-0.2
-2.2
-0.5
2.0
-0.7
-0.5
0.1
0.4
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
Art
Biology
Chemistry
DesignandTechnology
Drama
EnglishLanguage
EnglishLiterature
French
Geography
German
History
HomeEconomics
InformationTechnology
Maths
MediaStudies
Music
Physics
ReligiousStudies
Spanish
AverageStandardisedResidual
Subject Summary
Standardised Residual Graph

6. The Scatter Plot
Baseline Score
GradePointsEquivalent
Look for Patterns…
General Underachievement / over
achievement ?
Do any groups of students stand out ?
– high ability vs low ability ?
– male vs female ?

7. Other things to look for…
Why did these students do so badly ?
Why did this student do so well ?

8. Discussion

9. Prompts…
How can we use this data to identify over/under achievers?
How can we use this data to set targets for individuals or classes?
What data should we share with the student (if any?)
What data should we share with parents (if any?)
What is the role of the HOD / HOF / HOG in all of this?

10. Prompts…
How can we use this data to identify over/under achievers?
How can we use this data to set targets for individuals or classes?
What data should we share with the student (if any?)
What data should we share with parents (if any?)
What is the role of the HOD / HOF / HOG in all of this?