Transcript Slide 1

CEM CONFERENCE EXETER
Data for Monitoring Target Setting and
Reporting
Day 2 Session 2
28th February 2013
Geoff Davies
[email protected]
Unintelligent target setting
•
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•
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•
•
•
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Health service examples
Bankers pay!
Prison Service
What you measure is what you get!
Some Key stage assessment targets
Attendance targets
Concentration on only certain borderlines
Tick box mentality
Payment by results
English Bac? Some targets are ‘cancelled’ before the event!
TARGET SETTING
‘Intelligent’ Target Setting involves:
•
•
•
•
•
•
•
Using reliable predictive data
Points and/or Grades
Nationally standardised baseline
Independent sector standardised baseline (MidYIS only)
Prior value-added (MidYIS, Yellis and Alis)
Chances graphs
Dialogue between the users: teachers, parents and students? (empowering,
ownership, and taking responsibility)
• The use of professional judgement……..
There is wide-ranging practice using CEM data to set student,
department and institution targets.
Increasingly sophisticated methods are used by schools and colleges.
The simplest model is to use the student grade predictions. These
then become the targets against which student progress and
achievement can be monitored.
Theoretically, if these targets were to be met, residuals would be zero so
overall progress would be average.
The school/college would be at the 50th percentile.
More challenging targets would be those based on the basis of history.
For example. Where is the school/college now? Where is your subject
now?
If your subject value added history shows that performance is in the
upper quartile it may be sensible to adjust targets. This may have the
effect of raising point predictions between 0.2-0.5 of a grade.
This would be a useful starting point, but it would not be advisable to use
the predictions for below average subjects, which might lead to
continuing under achievement.
Paris97.xls
Subject
Art & Design
Business Studies
Design & Technology
Drama
English
English Literature
French
Geography
German
History
Home Economics
ICT
Maths
Music
Physical Education
Religious Studies
Double Science
Welsh
Number of
Students
Percentage
of A* to C
Grades
Percentage
of A* to G
Grades
68
64
103
27
181
15
53
84
7
49
48
71
180
12
72
37
180
177
84
48
63
85
64
60
64
63
71
67
48
68
54
67
65
70
52
72
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
School Average GCSE score:
Average
Grade
5.2
4.3
4.7
5.3
4.8
4.6
4.9
4.8
5.1
5.1
4.5
4.9
4.5
5.2
4.9
5.2
4.4
5.1
(C)
(C/D)
(C/D)
(B/C)
(C)
(C/D)
(C)
(C)
(C)
(C)
(C/D)
(C)
(C/D)
(C)
(C)
(C)
(C/D)
(C)
4.7 (C/D)
Counted Performance Statistics (Based on Subject Choice Predictions)
5 or more A* to C Grades:
106
1 or more A* to C Grades:
141
5 or more A* to G Grades:
181
1 or more A* to G Grades:
181
58%
77%
99%
99%
5 or more A* to C Grades inc Maths and English:
2 or more A* to C Grades - Sciences:
1 or more A* to C Grades - Modern Foreign Language:
54%
51%
20%
98
93
36
The underlying predictions summarised here are based on expectations for an average school achieving
zero value added results. Appropriate care should be taken in interpreting them within your school.
Please note that the cut-off points for grade C and grade G have been set at 4.5 and 0.5 respectively.
Due to the sensitive nature of the cut off points, predictions may vary for your school if the cut off points
could be altered.
(*Predictions Adjusted for Positive Prior Value-added Performance)
Subject
Art & Design
Business Studies
Design & Technology
Drama
English
English Literature
French
Geography
German
History
Home Economics
ICT
Maths
Music
Physical Education
Religious Studies
Double Science
Welsh
Number of
Students
Percentage
of A* to C
Grades
Percentage
of A* to G
Grades
68
64
103
27
181
15
53
84
7
49
48
71
180
12
72
37
180
177
84
48
87
100
69
67
96
73
86
67
79
96
57
92
65
70
59
86
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
School Average GCSE score:
Average
Grade
5.2
4.3
5.3
6.0
4.9
4.9
6.4
5.2
5.6
5.1
5.2
5.7
4.6
5.7
4.9
5.3
4.7
5.5
(C)
(C/D)
(B/C)*
(B)*
(C)*
(C)*
(A/B)*
(C)*
(B/C)*
(C)
(C)*
(B/C)*
(C/D)*
(B/C)*
(C)
(B/C)*
(C/D)*
(B/C)*
5.1 (C)
Counted Performance Statistics (Based on Subject Choice Predictions)
5 or more A* to C Grades:
125
1 or more A* to C Grades:
162
5 or more A* to G Grades:
181
1 or more A* to G Grades:
181
69%
89%
99%
99%
5 or more A* to C Grades inc Maths and English:
2 or more A* to C Grades - Sciences:
1 or more A* to C Grades - Modern Foreign Language:
56% *
58% *
30% *
102
106
54
*
*
*
*
(*Predictions Adjusted for 75th Percentile)
Subject
Art & Design
Business Studies
Design & Technology
Drama
English
English Literature
French
Geography
German
History
Home Economics
ICT
Maths
Music
Physical Education
Religious Studies
Double Science
Welsh
Number of
Students
Percentage
of A* to C
Grades
Percentage
of A* to G
Grades
68
64
103
27
181
15
53
84
7
49
48
71
180
12
72
37
180
177
97
63
73
96
70
67
74
70
71
84
63
77
61
83
72
81
59
82
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
School Average GCSE score:
Average
Grade
5.5
4.6
5.0
5.5
5.0
4.9
5.1
5.1
5.4
5.4
4.8
5.2
4.8
5.5
5.2
5.5
4.7
5.4
(B/C)*
(C/D)*
(C)*
(B/C)*
(C)*
(C)*
(C)*
(C)*
(B/C)*
(B/C)*
(C)*
(C)*
(C)*
(B/C)*
(C)*
(B/C)*
(C/D)*
(B/C)*
5.0 (C)
Counted Performance Statistics (Based on Subject Choice Predictions)
5 or more A* to C Grades:
123
1 or more A* to C Grades:
162
5 or more A* to G Grades:
181
1 or more A* to G Grades:
181
68%
89%
99%
99%
5 or more A* to C Grades inc Maths and English:
2 or more A* to C Grades - Sciences:
1 or more A* to C Grades - Modern Foreign Language:
60% *
58% *
23% *
109
106
41
*
*
*
*
YELLIS PREDICTIONS FOR MODELLING
FOUR approaches
•YELLIS GCSE Predictions
•YELLIS GCSE Predictions + say 0.5 a grade
• Prior value added analysis based on 3 year VA per department
• 75th percentile analysis
Setting targets: one suggested approach
• Discuss previous value added data with each HoD
• Start with an agreed REALISTIC representative figure
based previous years (3 ideally) of value added data
• add to each pupil prediction, and convert to grade (i.e. inbuilt value added)
• By discussion with students and using professional
judgement, AND THE CHANCES GRAPHS, adjust target
grade
• calculate the department’s target grades from the addition
of individual pupil’s targets
7
7
7
5
7
6
7
5
5
7
6
7
7
6
6
7
5
6
6
7
6
6
6
7
6
6
7
5
B
A
A
C
A*
A*
A*
B
B
A
A
A
B
C
A*
A*
B
A
A*
A
A
A
B
B
A
B
A*
A
7
6.7
7.1
5.4
7.6
7.8
7.8
6.2
6.4
6.8
6.9
7.3
6.3
5.4
8.1
9.4
6
6.9
7.9
7.4
6.5
6.5
5.9
6.1
7.3
6.4
7.9
6.7
5.8
5.4
5.8
4.2
6.4
6.6
6.6
4.9
5.2
5.5
5.6
6.1
5.1
4.1
6.9
8.1
4.7
5.7
6.7
6.2
5.3
5.2
4.7
4.9
6.1
5.2
6.7
5.5
B
B
B
D
A
A
A
C
C
B
B
B
C
D
A
A*
C
B
A
B
B
C
C
C
B
C
A
B
A
A
A
B
A
A
A
B
B
B
A
A
B
B
A
A*
B
A
A
A
A
B
A
B
B
B
B
B
A
A
A
B
A
A
A
B
B
A
A
A
A
B
A
A*
C
A
A
A
A
B
A
A
A
A
A
B
Comment Y10 French
EFFORT(EGIP)10Fr
Summer Grade French Y10
TARGETS FR Y10
INTERIMS FR Y10
YELLIS GCSE PREDICTION FR
75th percentileYpredFr
PriorValueaddedYpredFr
97.5
97.5
96.9
95.7
86.4
90.7
93.2
88.9
97.5
97.5
96.3
87
87
94.4
96.3
96.9
87.7
99.4
84
95.1
98.1
95.7
93.8
97.5
100
90.7
99.4
99.4
GradePriorValueaddedYFr
Percentage Attendance
10Y
10Y
10Y
10E
10H
10G
10D
10N
10H
10H
10Y
10H
10E
10Y
10D
10G
10E
10E
10D
10D
10N
10N
10W
10E
10E
10D
10H
10H
MF TA MFL Subject Wa Key
Stage 3 Validated Result
Registration Group
Surname Forename
SHARED DATA eg Year 10 French class
E
E
E
G
E
E
E
G
G
E
E
E
E
E
E
E
G
E
E
E
E
G
E
G
E
E
E
E
ALIS
You are the subject teacher and are discussing possible A2 target grades
with individual students. You are about to talk to Jonathan who achieved
an average GCSE score of 6.22. This gives a statistical
prediction=28.35x6.22-99.57= 77 UCAS points using the regression
formula at A2 for this subject (Grade C at A2). Assume that the computer
adaptive baseline test confirms this prediction. Chances graphs for this
subject are shown showing the percentage of students with similar profiles
achieving the various grades.
Individual chances graph for Jonathan
1
a) Why are these two chances graphs different?
---------------------------------------------------------------------------------------------------------(b) ‘Most candidates with Jonathan’s GCSE background score achieved a C in my
subject last year so Jonathan’s target grade should be a C’.
What are the weaknesses of this statement?
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(c) What other factors should be taken into consideration apart from chances graph
data, when determining a target grade?
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------1
The difference in the chances graphs is that one of them provides for a range of
GCSE scores whilst the other is linked to Jonathan’s individual average GCSE
score of 6.22. The strength of the chances graph is that it shows more than a bald
prediction.
True, most students starting from an average GCSE score like Jonathan did
achieve a C grade at A2 in examinations for this subject. However the probability of
a B grade is also high since his score was not at the bottom of this range. This
might be reflected too if the department also has a history of high prior value
added. The converse is also true with a D grade probability warning against
complacency. Students are not robots who will always fit with statistics so it is
dangerous to make sweeping statements based on one set of results.
As well as looking at the prediction you should use the chances graph as a starting
point, with your professional judgement taking into and account factors such as his
and the departments’ previous performance in the subject, his attitude to work what
he is likely to achieve based on your own experience. You might want to start with
the most popular outcome grade C and use your judgement to decide how far up
(or down!) to go. He may be a very committed student and if the department has
achieved high value added in the past, an A/B grade may be more appropriate
though A* looks unlikely. If you are using aspirational targets for psychological
reasons with students then A may be appropriate even though it less probable than
B/C.
Chances graphs MidYIS and YELLIS
Situation
You are a tutor to a Year 10 pupil and you wish to help him/her to set target grades.
Here is a chances graph based on the pupil’s Year 7 MidYIS score (114) and one
based on the Year 10 Yellis test (58%)
Yellis Chances Graph
MidYIS Chances Graph
This graph is based on the pupil’s exact midyis score, adjusted to include the
school’s previous value-added performance.
This graph is based on one ability band and has no valueadded adjustment.
2
a) What do the graphs tell you about this pupil’s GCSE chances in this subject
(Maths)?
b) What could account for the differences between the two graphs and are these
important?
IMPORTANT FOR STAFF AND STUDENTS TO UNDERSTAND THE DIFFERENCE
Fixed Mindset:
[My intelligence is fixed and tests tell me how clever I am.]
This graph tells me I’m going to get a B, but I thought I was going to get an A. I’m
obviously not as clever as I hoped I was and so the A and A* grades I’ve got for my work
so far can’t really be true.
Growth Mindset:
[My intelligence can develop and tests tell me how far I have got.]
This tells me that most people with the same MidYIS score as me achieved a B last year,
but I think I have a good chance of an A and I know that my work has been about that
level so far so I must be doing well. What do I need to do to be one of the 10% who gets
an A*?
How was this information produced?
The MidYIS graphs are produced using the predictions spreadsheet. Select the pupil(s) and subject(s) to display or print using the GCSE Pupil Summary
1 tab. Adjustments for value-added can be made for individual subjects on the GCSE Preds tab.
The Yellis graphs for all GCSE subjects (showing all four ability bands) can be downloaded from the Yellis+ website.
2
From Midyis The most likely grade is a B (35%) but remember
there is a 65% (100-65) chance of getting a different grade but also
a 75% (35+30+10) chance of the top three grades.
From Yellis The most likely grade appears to be a C but remember
that the band has been decided over a range, not for the individual
student and this pupils score is near the top of that range, 58
compared with 60.8. It has also not been adjusted for this school’s
prior value added in the past.
In an interview with the student one has to use your professional
judgement about that student, taking everything into account.
Certainly the Yellis chart warns against complacency, but if the
school has a strong value added history it is better to rely in this
case on the Midyis chart for negotiating a target. Grade A is a fair
aspirational target for the student but accountability for a teacher
cannot fairly be judged by not achieving this grade with this
student. Even a very good teacher may only achieve B or C with this
student.
Can the aspirational target set for the student be the same as that
used for staff accountability purposes? There is a trap here.
3
Case study no.1: setting targets.
•
•
•
•
Uses valid and reliable data e.g chances graphs
Involves sharing data with the students
Gives ownership of the learning to the student
Enables a shared responsibility between student,
parent(s)/guardian, and the teacher
• Encourages professional judgement
• Leads to the teachers working smarter and not harder
• Leads to students being challenged and not ‘over
supported’, thus becoming independent learners…
DEPARTMENT:
GCSE ANALYSIS
year
2006
2007
2008
2009
no. of
pupils
66
88
92
108
av. Std.
raw resid. Resid
0.8
0.8
1.1
0.7
0.6
0.5
0.8
0.6
n.b. A raw residual of 1.0 is equivalent to one grade.
TARGETS FOR 2011, using CEM predictive data and dept's prior value-added
The target grade has a prior value-added of 0.8
1
2
3
4
5
6
7
8
9
10
12
M
F
M
F
M
F
M
M
F
M
M
prediction
5.4
3.8
3.6
4.2
5.7
6.5
7.0
3.8
4.2
5.9
3.8
pred
grade
(B/C)
(D)
(D/E)
(D)
(B/C)
(A/B)
(A)
(D)
(D)
(B)
(D)
target
6.2
4.6
4.4
5.0
6.5
7.3
7.8
4.6
5.0
6.7
4.6
target
grade
B
C
D
C
B
A
A*
C
C
A
C
etc.
dept adj
grade
A
C
D
D
B
A*
A*
C
C
B
D
Geography
Individual Student
Chances no.1
GraphGCSE
for student
A- GCSE English
MidYIS Score 105 MidYIS Band B
Teacher's Adjustment : 0 grades / levels / points
45
40
35
Prediction/expected grade: 5.4
grade B/C
36
32
Percent
30
25
Most likely grade
20
14
15
14
10
5
0
0
0
U
G
F
3
2
0
E
D
Grade
C
B
A
A*
StudentGraph
no.1 GCSE
Geography
Individual Chances
for Student
A- GCSE English
MidYIS Score 105 MidYIS Band B
Teacher's Adjustment : 0.8 grades / levels / points
45
40
36
35
Percent
30
25
Prediction/expected grade: 6.2
grade B
Most likely grade
32
20
20
15
9
10
4
5
0
0
0
0
U
G
F
E
0
D
Grade
C
B
A
A*
Results
13
19
23
21
10
6
COMMENTS?
Monitoring Student Progress
Monitoring students’ work against target grades is established practice in
schools and colleges, and there are many diverse monitoring systems in
place.
Simple monitoring systems can be very effective
Current student achievement compared to the target grade done at
predetermined regular intervals to coincide with, for example internal
assessments/examinations
Designated staff having an overview of each student’s achievements across
subjects
All parents being informed of progress compared to targets
Review of progress between parents and staff
Subject progress being monitored by a member of the management team in
conjunction with the head of subject/department
A tracking system to show progress over time for subjects and students
J
M 97.3
C
F
71.8
101 A
MIDYIS ON ENTRY
99 B
132 131 127 105
94 5
4
5
-2.2 6
5
6
6
-2.5 5
6
6
5
-3
101
86 6
4
5
-0.1 5
4
3
4
-2 5
5
5
4
-1.8
83 116
KEY STAGE 3 STATUTORY TEACHER ASSESSMENT
94
Pupil Tracking
92 113
SOSCA STANDARDISED SCORES
98
96
83 102
98
90 103
87
97
95
98
83
95
88
SOSCA Maths
SOSCA St.ScoreSPACE.Maths
SOSCA St.ScoreNUMBERMaths
SOSCA St.ScoreH.DATAMaths
SOSCA St. Score Physics
SOSCA St. Score Chemistry
SOSCA St. Score Biology
SOSCA (STA.) Reading
St. res. MIDYIS- KS3 Sc
SC TA Science Subject Wa
PE TA Phys Ed Subject Wa
MU TA Music Subject Wa
MF TA MFL Subject Wa
St. res. MIDYIS- KS3 Ma
MA TA Maths Subject Wa
IC TA Inf Tech Sub Wa
HI TA History Subject Wa
GE TA Geography Sub Wa
St. res. MIDYIS- KS3 En
EN TA English Subject Wa
DA TA Des and Tech Sub Wa
AR TA Art Subject Wa
MidYIS Skills Standardise
MidYIS Non Verbal Standar
MidYIS Vocabulary Standar
MidYIS Maths Standardised
MidYIS Overall Standardis
MidYIS Overall Band Year
LONDON READING
% Attendance Y10
Gender
Surname Forename
Tracking at departmental level for one student
Student: Peter Hendry
test: Geol
Time Scale
target
grade
test essay:
radiometric
dating
test: dating
homework
rock cycle
pract: rock
textures
2006-8
test:
igneous
rocks
15/09/2006 22/09/2006 06/10/2006 20/10/2006 06/11/2006 21/11/2006
97%
A
84%
68%
B
C
Department: Geology
57%
54%
D
E
U
50%
SURNAME
Briggs
Fletcher
Green
Havard
etc
punctuality
meeting deadlines
D
B
A
A
1
2
1
3
2
2
1
3
1
2
2
4
C
B
B
B
1
2
2
4
1
1
2
2
1
1
2
2
meeting deadlines
punctuality
DEC
effort
OCT
current level
effort
C
B
B
A
current level
C
A
C
A
meeting deadlines
Alice
Kevin
Felicity
Michael
punctuality
yr 12
effort
BIOLOGY
current level
FORENAME
negociated target
grade
subject:
initial target grade
Traditional mark book approach
07-08
MAR
Targets
for learning…. reporting to pupils
-7.07013
0.59938
A
B
C
D
E
F
g
h
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
ZA
ZB
MidYis Score Test Score
80
96
95
119
111
84
67
88
118
91
120
108
115
87
117
105
98
69
69
115
118
109
123
89
115
76
90
97
33
63
80
80
73
45
45
63
50
60
50
35
35
58
83
45
73
5
30
70
50
45
60
30
65
10
55
70
41.12001 49.34402
50.17065 60.20478
49.87096 59.84515
64.1362 76.96344
59.46104 71.35324
43.33772 52.00526
33.02838 39.63406
100 55.02614
45.85511
63.83651 76.60381
90 56.96813
47.47344
64.79552 77.75462
80 69.12355
57.60296
62.09831 74.51797
45.31567
70 54.37881
62.99738 75.59685
55.80482
60 66.96578
51.54922 61.85907
34.4669
50 41.36028
34.10727 40.92872
61.91849
40 74.30219
63.71663 76.45996
58.32222
30 69.98666
66.47378 79.76854
46.03493
20 55.24191
61.55887 73.87064
38.48274
10 46.17929
46.57437 55.88924
50.88990 61.06789
-7.07013 -8.484156
60
-7.07013 -8.484156
-7.07013 -8.484156
-7.07013 -8.484156
-7.07013 -8.484156
-7.07013 -8.484156
Test Score
Name
32.89601
40.13652
39.89677
51.30896
47.56883
34.67017
26.42271
36.68409
51.06921
37.97875
51.83641
46.08237
49.67865
36.25254
50.3979
44.64386
41.23938
27.57352
27.28581
49.5348
50.97331
46.65777
53.17903
36.82794
49.24709
30.78619
37.2595
40.71192
-5.656104
70
-5.656104
-5.656104
-5.656104
-5.656104
-5.656104
Astronomy 7N
MidYis Test Review
80
90
100
110
MidYis Score
..\..\MidYis Proformanonames.xls
120
130
SOME TRAPS TO AVOID
TRAP 1
• Y6 class of 10 pupils
• Each predicted a Level 4
• Each with 90% chance of success
• Best estimate is that one will not make it
• Best estimate = 90% L4+
TRAP
2
PSYCHOLOGICAL EFFECT ON PUPILS
THE C/D boundary problem at GCSE
PUPILS NEED HIGH EXPECTATIONS
Teachers who set high expectations should not
be criticised for setting them slightly too high
at times.
What are the implications for the performance
management of teachers?
MONITORING PITFALLS
1. Tracking developed ability measures over time.
2. Looking at average standardised residuals for teaching sets.
3. Effect of one result in a small group of students
SOSCA
Reading
LONDON
READING
YEAR 9
SOSCA
Reading
Band
YEAR 7
SOSCA
Reading
DIFFERENCE
NEGATIVES
104
B
129
-25
99
B
120
-21
108
B
128
-20
108
B
128
-20
111
B
129
-18
90
C
108
-18
101
B
118
-17
88
C
104
-16
122
A
137
-15
85
D
99
-14
103
B
117
-14
71
D
83
-12
115
A
127
-12
106
B
118
-12
96
C
107
-11
90
C
100
-10
94
C
104
-10
89
C
99
-10
88
D
97
-9
119
A
128
-9
Sample
results from
spreadsheet
comparing
performance
in reading in
Year 7 and
year 9 on two
different tests
for cohorts of
2007 and
2008.
Correlation is
0.75.
Note the
regression
towards the
mean pattern.
See next two
slides
YEAR 9
SOSCA
Reading
Band
LONDON
READING
YEAR 7
DIFFERENCE
POSITIVES
127
A
108
19
122
A
102
20
115
A
95
20
97
C
77
20
121
A
100
21
118
A
97
21
125
A
104
21
112
A
91
21
146
A
125
21
129
A
107
22
112
A
90
22
111
B
89
22
113
A
90
23
134
A
110
24
115
A
90
25
116
A
91
25
109
B
84
25
139
A
107
32
130
A
97
33
141
A
106
35
Differece SOSCA reading-London Reading
40
30
Difference SOSCA -LONDON
20
10
difference
Poly. (difference)
0
60
70
80
90
100
110
-10
-20
-30
London Reading
120
130
140
150
REGRESSION TOWARDS THE MEAN
DIFFERENCE SOSCA-MIDYIS MATHS
20
10
0
SOSCA-MIDYIS DIFFERENCE
50
70
90
110
130
150
170
-10
DIFFERENCE
-20
-30
-40
-50
MIDYIS MATHS STANDARDISED
Pupils with high MidYIS scores tend to have high SOSCA scores but
not quite as high. Similarly pupils with low MidYIS scores tend to have
low SOSCA scores, but not quite as low. It is a phenomenon seen in
any matched dataset of correlated and normally-distributed scores,
the classic example is a comparison of fathers' and sons'
heights. Regression lines reflect this phenomenon - if you look at the
predictions used in the SOSCA value-added you can see that for
pupils with high MidYIS scores their predicted SOSCA scores are
lower than their MidYIS scores, whereas for pupils with low MidYIS
scores their predicted SOSCA scores are higher than their MidYIS
scores.
Marksheet Name : SUBJECT REVIEW
Marksheet Group : 11S1
62 A
A
B
GCSE Standard Residual Ma
WJEC/GCSE 018403 ResGF
WJEC/GCSE 018402 ResGF
WJEC/GCSE 018401 ResGF
YELLIS GCSE PREDICTION MA
PREDICTION MATHS
YELLIS MATHS BAND
Yellis Band
Yellis Score
Admission No.
Students
CLASS
REVIEW
: 04/10/2005
Maths Test K3 Wa
Export Date
a
90019
6
b
90090
7
B
A
c
90045
6
63 A
B
B
B
B
0.10
d
90063
7
64 A
A
B
B
B
0.10
e
90166
6
48 B
B
B
C
C
0.40
f
90123
7
70 A
A
A
A
B
-0.40
g
90129
6
47 C
C
B
C
C
0.50
h
90146
6
59 B
B
A
B
A
1.40
I
90047
7
62 A
A
B
B
B
0.20
j
90115
7
67 A
A
*
A
A*
1.70
k
90004
6
46 C
B
B
C
B
1.50
B
1.20
B
l
90164
7
65 A
A
A
B
A*
1.90
m
90099
7
70 A
A
A
A
A*
1.50
n
90011
7
61 A
A
A
B
A
1.30
o
90112
7
66 A
A
B
A
A
0.80
p
90058
6
70 A
A
B
A
A
0.50
q
90150
7
72 A
A
A
A
A
0.40
r
90127
6
52 B
B
B
C
B
1.00
s
90030
6
58 B
B
B
B
B
0.50
t
90050
7
71 A
A
A
A
A
0.40
u
90016
6
69 A
A
B
A
B
-0.40
v
90174
7
74 A
A
A
A
A
0.20
w
91165
6
62 A
B
B
B
x
90109
7
63 A
B
B
B
B
0.10
y
90138
7
47 C
B
B
C
B
1.40
z
90122
7
60 A
A
*
B
A
1.30
ab
90009
7
60 A
A
A
B
A
1.30
ac
90169
7
79 A
A
*
A*
A
-0.20
ad
90153
6
56 B
B
B
B
ae
90010
7
64 A
B
B
B
A
1.00
af
90154
7
61 A
C
B
B
B
0.30
Total
B
B
0.70
1868
109
105
201
156
12
190
31
30
30
30
31
30
2
29
30
Mean
42.68
62.27
3.63
3.5
6.48
5.2
6
6.55
0.70
Mean Grade
6.00
B
B
B
B
The Critchlow Effect
INTERPRETATION
0.20
1323
Num ber of Results
BEWARE
PITFALLS
B
B
20.90
Teaching Sets
SUBJECT M
Sex
M
M
F
M
F
F
M
M
F
F
F
M
Score
(Band)
53 (B)
38 (C)
36 (D)
48 (C)
52 (B)
65 (A)
70 (A)
38 (C)
40 (C)
70 (A)
44 (C)
56 (B)
Predicted Grade
5.4 (B/C)
4.5 (C/D)
4.4 (C/D)
5.1 (C)
5.3 (B/C)
6.1 (B)
6.4 (A/B)
4.5 (C/D)
4.6 (C/D)
6.4 (A/B)
4.8 (C)
5.6 (B/C)
5.3 (B/C)
Achieved Grade
6 (B)
3 (E)
3 (E)
5 (C)
6 (B)
7 (A)
3 (E)
4 (D)
5 (C)
7 (A)
6 (B)
5 (C)
5.0 (C)
Raw
Residual
0.6
-1.5
-1.4
-0.1
0.7
0.9
-3.4
-0.5
0.4
0.6
1.2
-0.6
-0.3
Standard
ised
Residual
0.5
-1.1
-1.0
-0.1
0.5
0.7
-2.5
-0.4
0.3
0.4
0.9
-0.4
-0.2
REVISED
0.5
-1.1
-1.0
-0.1
0.5
0.7
-0.4
0.3
0.4
0.9
-0.4
0.0
MONITORING MIDYIS YEAR 7 TO SOSCA SCIENCE SCORE YEAR 9
Sex
MidYIS
Test
Score
Predicted
SOSCA
Score
Actual
SOSCA
Score
Raw
Residual
Standardised
Residual
A
F
99
95
97
2
0.2
B
F
105
99
98
-1
-0.1
C
M
102
97
96
-2
-0.2
D
F
72
80
76
-4
-0.4
E
F
152
126
142
16
1.5
Surname
Forename
MONITORING MIDYIS YEAR 7 TO SOSCA READING SCORE YEAR 9
Sex
MidYIS
Test
Score
Predicted
SOSCA
Score
Actual
SOSCA
Score
Raw
Residual
Standardised
Residual
A
F
99
96
91
-5
-0.5
B
F
105
100
115
15
1.7
C
M
102
98
87
-11
-1.2
D
F
72
80
87
7
0.8
E
F
152
128
134
6
0.6
Surname
Forename
MONITORING MIDYIS YEAR 7 TO SOSCA MATHS SCORE YEAR 9
Sex
MidYIS
Test
Score
Predicted
SOSCA
Score
Actual
SOSCA
Score
Raw
Residual
Standardised
Residual
A
F
99
93
96
3
0.4
B
F
105
97
97
-1
-0.1
C
M
102
95
86
-10
-1.1
D
F
72
73
73
1
0.1
E
F
152
134
121
-13
-1.5
Surname
Forename
CEM CONFERENCE EXETER
Data for Monitoring Target Setting and
Reporting
Day 2 Session 2
28th February 2013
Geoff Davies
[email protected]