Rob Smith Day 2 Working with Parents Colleagues and

Report
Rob Smith: CEM Inset Provider
Working with Colleagues, Parents and Students
Course: Using CEM Data in Practice
Day 2 Session 3
Thursday 28th February 2013
[email protected]
Interpreting IPRs
Exercise
Have a look at the three IPRs on the following
pages.
What do the scores suggest about the students
and how would you use this information to aid
the teaching and learning process for each of
them?
1
2
3
Proof-Reading 88
PSA 108
Case Study 1
You are given data relating to an institution where students completed the ALIS computer
adaptive test. They are chosen because they show significant differences between the
various parts of the test. Remember scores are standardised around 100.
Name
Overall
Vocab
Maths
Non Verbal
Average
A Level subjects chosen
St.Score
Band
St.Score
Band
St.Score
Band
St.Score
Band
GCSE
A
78
D
49
D
99
B
92
C
na
Biology, Maths, Business, Art
B
94
C
115
A
85
D
104
B
na
Biology, Business, Psychology, English
C
88
D
97
C
85
D
104
B
5.6
History, Psychology, English, Media
D
101
B
107
B
97
C
80
D
5.9
Business, History, English, Drama
E
104
B
87
D
112
A
116
A
na
Biology, Physics, Maths, Business
F
81
D
47
D
103
B
111
B
na
Maths, Further Maths, Business
G
93
C
113
A
84
D
113
A
na
Biology, Business, French, Geography
H
97
C
111
A
89
D
99
C
7
Art, English, Psychology, Religious St.
I
87
D
68
D
100
B
109
B
5.4
Maths, Geography, French, Music
J
105
B
67
D
124
A
85
D
6.1
Maths, Further Maths, Psychology,
Economics
K
96
C
71
D
110
A
97
C
na
Biology, Maths, Art, English
L
92
C
60
D
111
A
97
C
na
Maths, History Religious St., English
a) Are there any apparent mismatches between the subjects being followed and this data?
b) What support can be given to those students who have weaknesses in Vocabulary or
Mathematics ?
c) How might predictions made for these students be tempered in the light of the inconsistencies
in the test components and missing average GCSE points scores?
Case Study 2
What are the Strengths and weaknesses of this A/AS level student?
To use the IPR (Individual pupil record) familiarise yourself with the terms
standard score, band, stanine, percentile and confidence band
a)Which AS/A level subjects might be
avoided?
b) This student chose English, Film
Studies, Music Technology and
Psychology.
Is this a good choice? Do you foresee
any problems?
Case Study 3
Here is the Individual Pupil Record
from the ALIS computer adaptive
test done in Year 12 for a current
Year 13 student.
This student had a high positive
value added in every GCSE subject
as measured using MidYIS as a
baseline.
( Average GCSE score 7.44)
On the next page are her A level
predictions and chances graphs
Why are the predictions different?
Are the chances graphs useful
here?
Using PARIS software and
tweaking the predictions for
prior value added by these
subjects, then from a GCSE
baseline A*s are predicted in
three of the four.
If we did the same for the
adaptive test baseline solid Bs
might be predicted in all three.
It is also worth looking at
the value added at GCSE.
See commentary
Commentary
The differences in prediction from the GCSE baseline and the computer adaptive
test for some students are interesting and these can be in either direction. Here
there has been a very large value added at GCSE which may or may not be
sustainable at A level. This student’s history is shown below
GCSE PREDICTIONS MIDYIS ALL
GSCE Grade predictions
GCSE ACHIEVED
VALUE ADDED RAW
Drama
5.8
BA*
2.2
English
6
B
A*
2
GCSE PREDICTIONS MIDYIS IND.
GSCE Grade predictions
GCSE ACHIEVED
VALUE ADDED RAW IND
6.6
A/B
A*
1.4
6.8
AA*
1.2
English Lit German
6
5.5
B
B/C
A
A
1
1.5
6.7
AA
0.3
6.5
A/B
A
0.5
Latin
6.5
A/B
A*
1.5
Maths
6
B
A*
2
Music
5.9
BA
1.1
6.9
AA*
1.1
6.9
AA*
1.1
6.9
AA
0.1
Science
5.8
BA
1.2
6.9
AA
0.1
from year7 data
from year9 data
Average GCSE score =7.44
The value added here at GCSE is between 1 and 2 grades (for all institution data at year
7) and significantly positive for subjects (for the Independent school data from year 9)
Actually if we measure this student’s value added from an average GCSE score of 7.44
next year, it does not tell the whole story. We need to look as well at the value added from
the adaptive test too.
The chances graphs should be used with extreme caution here and the growth mindset is
vital if used with students
CASE STUDY No. 4
A school uses the Yellis “predictions” to give target grades
for each GCSE subject a pupil is taking
This target grade is called a ‘Baseline Suggested Grade’
Through the progress reporting system teachers are asked
to assess current progress against this BSG and to suggest
what the likely outcome is AT THE END OF THE COURSE
Over the page is a pupil’s IPR followed by an end of term
report
If you were the pupil’s Form Teacher, how would you
approach a discussion with his/her parents at a Parents’
Evening?
Subjects
Who should
data be
shared with?
Colleagues
Subject Teachers
Heads of Department
Pastoral Staff
Managers
Subject Teachers/HODs
1. This will be interpreted as a personalised
prediction
2. The data doesn’t work for this particular
student
3. You’re raising false expectation – he’ll
never get that result
4. You’re making us accountable for
guaranteeing particular grades – when
the pupils don’t get them we’ll get sacked
and the school will get sued
Subject Teachers/HODs
Remind them that:
1. Baseline data can give useful information
about a pupil’s strengths and weaknesses
which can assist teaching and learning
2. “Predictions” are not a substitute for their
professional judgement
Reassure them that:
1. It is not a “witch hunt”
2. Value added data is used to assess pupil
performance not teacher performance!
Pupils
1. Make sure they know why they are taking
the test.
2. Make sure they take it seriously
3. Make sure they don’t deliberately mess it
up in order to lower their BSGs!
4. Be prepared to look for clear anomalies
and re-test if necessary
5. Explain the chances graphs to them
clearly
Parents
1. Make sure they know why the pupils are
taking the test
2. Explain the results to them
3. Explain lots of times that the chances
graphs and BSGs do NOT give
personalised predictions
4. Ensure that they receive good quality
feedback from staff when ambers or reds
are awarded
5. Encourage them to ask lots of questions

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