data for monitoring target setting and reporting cem conference exeter geoff davies day 2 session 2...
TRANSCRIPT
Data for Monitoring Target Setting and Reporting
CEM CONFERENCE EXETER
Geoff Davies
Day 2 Session 2 28th February 2013
Unintelligent target setting
• 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!
‘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……..
TARGET SETTING
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
SubjectNumber of Students
Percentage of A* to C Grades
Percentage of A* to G
Grades
Art & Design 68 84 100 5.2 (C)Business Studies 64 48 100 4.3 (C/D)Design & Technology 103 63 100 4.7 (C/D)Drama 27 85 100 5.3 (B/C)English 181 64 100 4.8 (C)English Literature 15 60 100 4.6 (C/D)French 53 64 100 4.9 (C)Geography 84 63 100 4.8 (C)German 7 71 100 5.1 (C)History 49 67 100 5.1 (C)Home Economics 48 48 100 4.5 (C/D)ICT 71 68 100 4.9 (C)Maths 180 54 100 4.5 (C/D)Music 12 67 100 5.2 (C)Physical Education 72 65 100 4.9 (C)Religious Studies 37 70 100 5.2 (C)Double Science 180 52 100 4.4 (C/D)Welsh 177 72 100 5.1 (C)
4.7 (C/D)
106 58%141 77%181 99%181 99%
98 54%93 51%36 20%
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:
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.
1 or more A* to C Grades: 5 or more A* to G Grades: 1 or more A* to G Grades:
Average Grade
School Average GCSE score:
Counted Performance Statistics (Based on Subject Choice Predictions)5 or more A* to C Grades:
SubjectNumber of Students
Percentage of A* to C Grades
Percentage of A* to G
Grades
Art & Design 68 84 100 5.2 (C)Business Studies 64 48 100 4.3 (C/D)Design & Technology 103 87 100 5.3 (B/C)*Drama 27 100 100 6.0 (B)*English 181 69 100 4.9 (C)*English Literature 15 67 100 4.9 (C)*French 53 96 100 6.4 (A/B)*Geography 84 73 100 5.2 (C)*German 7 86 100 5.6 (B/C)*History 49 67 100 5.1 (C)Home Economics 48 79 100 5.2 (C)*ICT 71 96 100 5.7 (B/C)*Maths 180 57 100 4.6 (C/D)*Music 12 92 100 5.7 (B/C)*Physical Education 72 65 100 4.9 (C)Religious Studies 37 70 100 5.3 (B/C)*Double Science 180 59 100 4.7 (C/D)*Welsh 177 86 100 5.5 (B/C)*
5.1 (C)
125 69% *162 89% *181 99% *181 99% *
102 56% *106 58% *54 30% *
(*Predictions Adjusted for Positive Prior Value-added Performance)
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:
1 or more A* to C Grades: 5 or more A* to G Grades: 1 or more A* to G Grades:
Average Grade
School Average GCSE score:
Counted Performance Statistics (Based on Subject Choice Predictions)5 or more A* to C Grades:
SubjectNumber of Students
Percentage of A* to C Grades
Percentage of A* to G
Grades
Art & Design 68 97 100 5.5 (B/C)*Business Studies 64 63 100 4.6 (C/D)*Design & Technology 103 73 100 5.0 (C)*Drama 27 96 100 5.5 (B/C)*English 181 70 100 5.0 (C)*English Literature 15 67 100 4.9 (C)*French 53 74 100 5.1 (C)*Geography 84 70 100 5.1 (C)*German 7 71 100 5.4 (B/C)*History 49 84 100 5.4 (B/C)*Home Economics 48 63 100 4.8 (C)*ICT 71 77 100 5.2 (C)*Maths 180 61 100 4.8 (C)*Music 12 83 100 5.5 (B/C)*Physical Education 72 72 100 5.2 (C)*Religious Studies 37 81 100 5.5 (B/C)*Double Science 180 59 100 4.7 (C/D)*Welsh 177 82 100 5.4 (B/C)*
5.0 (C)
123 68% *162 89% *181 99% *181 99% *
109 60% *106 58% *41 23% *
(*Predictions Adjusted for 75th Percentile)
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:
1 or more A* to C Grades: 5 or more A* to G Grades: 1 or more A* to G Grades:
Average Grade
School Average GCSE score:
Counted Performance Statistics (Based on Subject Choice Predictions)5 or more A* to C Grades:
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. in-built 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
SHARED DATA eg Year 10 French classS
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10Y 97.5 7 A 6.7 5.4 B A A E
10Y 96.9 7 A 7.1 5.8 B A A E
10E 95.7 5 C 5.4 4.2 D B B G
10H 86.4 7 A* 7.6 6.4 A A A E
10G 90.7 6 A* 7.8 6.6 A A A E
10D 93.2 7 A* 7.8 6.6 A A A E
10N 88.9 5 B 6.2 4.9 C B B G
10H 97.5 5 B 6.4 5.2 C B B G
10H 97.5 7 A 6.8 5.5 B B A E
10Y 96.3 6 A 6.9 5.6 B A A E
10H 87 7 A 7.3 6.1 B A A E
10E 87 7 B 6.3 5.1 C B A E
10Y 94.4 6 C 5.4 4.1 D B B E
10D 96.3 6 A* 8.1 6.9 A A A E
10G 96.9 7 A* 9.4 8.1 A* A* A* E
10E 87.7 5 B 6 4.7 C B C G
10E 99.4 6 A 6.9 5.7 B A A E
10D 84 6 A* 7.9 6.7 A A A E
10D 95.1 7 A 7.4 6.2 B A A E
10N 98.1 6 A 6.5 5.3 B A A E
10N 95.7 6 A 6.5 5.2 C B B G
10W 93.8 6 B 5.9 4.7 C A A E
10E 97.5 7 B 6.1 4.9 C B A G
10E 100 6 A 7.3 6.1 B B A E
10D 90.7 6 B 6.4 5.2 C B A E
10H 99.4 7 A* 7.9 6.7 A B A E
10H 99.4 5 A 6.7 5.5 B B B E
ALISYou 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
(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?
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(c) What other factors should be taken into consideration apart from chances graph data, when determining a target grade?
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a) Why are these two chances graphs different?
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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
SituationYou 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%)
MidYIS Chances Graph
Yellis 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 value-added 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?
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.
IMPORTANT FOR STAFF AND STUDENTS TO UNDERSTAND THE DIFFERENCEFixed 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*?
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
yearno. of pupils raw resid.
av. Std. Resid
2006 66 0.8 0.62007 88 0.8 0.52008 92 1.1 0.82009 108 0.7 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-addedThe target grade has a prior value-added of 0.8
predictionpred
grade targettarget grade
dept adj grade
1 M 5.4 (B/C) 6.2 B A2 F 3.8 (D) 4.6 C C3 M 3.6 (D/E) 4.4 D D4 F 4.2 (D) 5.0 C D5 M 5.7 (B/C) 6.5 B B6 F 6.5 (A/B) 7.3 A A*7 M 7.0 (A) 7.8 A* A*8 M 3.8 (D) 4.6 C C9 F 4.2 (D) 5.0 C C10 M 5.9 (B) 6.7 A B12 M 3.8 (D) 4.6 C D
etc.
0 0 03
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U G F E D C B A A*
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Individual Chances Graph for student A- GCSE EnglishMidYIS Score 105 MidYIS Band B
Teacher's Adjustment : 0 grades / levels / points
Prediction/expected grade: 5.4 grade B/C
Most likely grade
Student no.1 GCSE Geography
0 0 0 04
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U G F E D C B A A*
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Individual Chances Graph for Student A- GCSE EnglishMidYIS Score 105 MidYIS Band B
Teacher's Adjustment : 0.8 grades / levels / points
Prediction/expected grade: 6.2 grade B
Most likely grade
Student no.1 GCSE Geography
Results13192321106
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
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MIDYIS ON ENTRY KEY STAGE 3 STATUTORY TEACHER ASSESSMENT SOSCA STANDARDISED SCORES
J M 97.3 101 A 132 131 127 105 94 5 4 5 -2.2 6 5 6 6 -2.5 5 6 6 5 -3 92 113 98 90 103 97 95 98
C F 71.8 99 B 101 83 116 94 86 6 4 5 -0.1 5 4 3 4 -2 5 5 5 4 -1.8 96 98 83 102 87 83 95 88
Pupil Tracking
Student: Peter Hendry Department: Geology 2006-8
test: Geol Time Scale
test essay: radiometric
dating test: datinghomework rock cycle
pract: rock textures
test: igneous
rocks
target grade 15/09/2006 22/09/2006 06/10/2006 20/10/2006 06/11/2006 21/11/2006
A 97% 84%
B 68%
C 57%54%
D 50%
E
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Tracking at departmental level for one student
subject: BIOLOGY yr 12 07-08OCT DEC MAR
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Briggs Alice C C D 1 2 1 C 1 1 1Fletcher Kevin A B B 2 2 2 B 2 1 1Green Felicity C B A 1 1 2 B 2 2 2Havard Michael A A A 3 3 4 B 4 2 2
etc
Traditional mark book approach
0.59938 -7.07013Name MidYis Score Test Score
A 80 33 41.12001 49.34402 32.89601 Astronomy 7NB 96 63 50.17065 60.20478 40.13652C 95 80 49.87096 59.84515 39.89677D 119 80 64.1362 76.96344 51.30896E 111 73 59.46104 71.35324 47.56883F 84 45 43.33772 52.00526 34.67017g 67 45 33.02838 39.63406 26.42271h 88 63 45.85511 55.02614 36.68409I 118 50 63.83651 76.60381 51.06921J 91 60 47.47344 56.96813 37.97875K 120 50 64.79552 77.75462 51.83641L 108 35 57.60296 69.12355 46.08237M 115 35 62.09831 74.51797 49.67865N 87 58 45.31567 54.37881 36.25254O 117 83 62.99738 75.59685 50.3979P 105 45 55.80482 66.96578 44.64386Q 98 73 51.54922 61.85907 41.23938R 69 5 34.4669 41.36028 27.57352S 69 30 34.10727 40.92872 27.28581T 115 70 61.91849 74.30219 49.5348U 118 50 63.71663 76.45996 50.97331V 109 45 58.32222 69.98666 46.65777W 123 60 66.47378 79.76854 53.17903X 89 30 46.03493 55.24191 36.82794Y 115 65 61.55887 73.87064 49.24709Z 76 10 38.48274 46.17929 30.78619ZA 90 55 46.57437 55.88924 37.2595ZB 97 70 50.8899 61.06789 40.71192
-7.07013 -8.484156 -5.656104-7.07013 -8.484156 -5.656104-7.07013 -8.484156 -5.656104-7.07013 -8.484156 -5.656104-7.07013 -8.484156 -5.656104-7.07013 -8.484156 -5.656104
MidYis Test Review
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60 70 80 90 100 110 120 130
MidYis Score
Tes
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Targets for learning…. reporting to pupils
..\..\MidYis Proformanonames.xls
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+
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?
TRAP 2
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
YEAR 9
SOSCA Reading
Band
LONDON READING
YEAR 7
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
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
SOSCA Reading
YEAR 9
SOSCA Reading
Band
LONDON READING
YEAR 7
DIFFERENCE
POSITIVESSample 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
Differece SOSCA reading-London Reading
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60 70 80 90 100 110 120 130 140 150
London Reading
Dif
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Poly. (difference)
REGRESSION TOWARDS THE MEAN
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.
DIFFERENCE SOSCA-MIDYIS MATHS
-50
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50 70 90 110 130 150 170
MIDYIS MATHS STANDARDISED
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a 90019 6 62 A A B B A 1.20
b 90090 7 B B
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
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 B 0.20
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 B 0.70
ae 90010 7 64 A B B B A 1.00
af 90154 7 61 A C B B B 0.30
Total 1323 1868 109 105 201 156 12 190 20.90
Number of Results 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 B B
Marksheet Name : SUBJECT REVIEW
Marksheet Group : 11S1
Export Date : 04/10/2005
CLASS REVIEW
BEWARE PITFALLS
INTERPRETATION
Teaching Sets
The Critchlow Effect
SexScore (Band)
Raw Residual
Standardised
Residual REVISEDM 53 (B) 5.4 (B/C) 6 (B) 0.6 0.5 0.5M 38 (C) 4.5 (C/D) 3 (E) -1.5 -1.1 -1.1F 36 (D) 4.4 (C/D) 3 (E) -1.4 -1.0 -1.0M 48 (C) 5.1 (C) 5 (C) -0.1 -0.1 -0.1F 52 (B) 5.3 (B/C) 6 (B) 0.7 0.5 0.5F 65 (A) 6.1 (B) 7 (A) 0.9 0.7 0.7M 70 (A) 6.4 (A/B) 3 (E) -3.4 -2.5 M 38 (C) 4.5 (C/D) 4 (D) -0.5 -0.4 -0.4F 40 (C) 4.6 (C/D) 5 (C) 0.4 0.3 0.3F 70 (A) 6.4 (A/B) 7 (A) 0.6 0.4 0.4F 44 (C) 4.8 (C) 6 (B) 1.2 0.9 0.9M 56 (B) 5.6 (B/C) 5 (C) -0.6 -0.4 -0.4
5.3 (B/C) 5.0 (C) -0.3 -0.2 0.0
Predicted Grade Achieved Grade
SUBJECT M
Surname ForenameSex
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
MONITORING MIDYIS YEAR 7 TO SOSCA SCIENCE SCORE YEAR 9
Surname Forename
SexMidYIS
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
MONITORING MIDYIS YEAR 7 TO SOSCA READING SCORE YEAR 9
Surname Forename
SexMidYIS
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
MONITORING MIDYIS YEAR 7 TO SOSCA MATHS SCORE YEAR 9
Data for Monitoring Target Setting and Reporting
CEM CONFERENCE EXETER
Geoff Davies
Day 2 Session 2 28th February 2013