Data for Target Setting and Monitoring

Data for Target Setting and Monitoring

Course: Using CEM data in PracticeDay 2 Session 3

Wed 30th May 2012

Peter Hendry: CEM Consultant

[email protected]

Data for Target Setting and Monitoring

What type of predictive data should be used to set the targets?

• Points and/or Grades• Nationally standardised baseline• Independent sector standardised baseline

(MidYIS only)• Prior value-added (MidYIS, Yellis and Alis)• Chances graphs

Case study 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…

-0.6

-0.8

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Art

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Des

ign

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Dra

ma

Eng

lish

Eng

lish

Lite

ratu

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Fren

ch

Geo

grap

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Ger

man

His

tory

Hom

e E

cono

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s

ICT

Mat

hs

Mus

ic

Phy

sica

l Edu

catio

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Rel

igio

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tudi

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Dou

ble

Sci

ence

Spa

nish

Wel

sh

SC

Phy

sica

l Edu

catio

n

SC

Rel

igio

us S

tudi

es

Ave

rage

Sta

ndar

dise

d R

esid

ual Value Added 2009

CASE STUDY No. 1

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.

CASE STUDY No. 1

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.

CASE STUDY No. 1

0 0 03

14

3632

14

20

5

10

15

20

25

30

35

40

45

U G F E D C B A A*

Perc

ent

Grade

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

CASE STUDY No. 1

0 0 0 04

20

3632

9

0

5

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45

U G F E D C B A A*

Perc

ent

Grade

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

CASE STUDY No. 1

gradestat targ

nosdept adj

nosA* 9 6

A 21 22B 26 22C 18 26D 13 12E 4 3F 0 0G 0 0

CASE STUDY No. 1

Peter Hendry10JG2011/2012

AUTUMN SPRING SUMMER

Subject Teachcu

rren

t gr

ade

targ

et

grad

e

is:

effo

rt

conc

ern

curr

ent

grad

e

targ

et

grad

e

is:

effo

rt

conc

ern

year

10

exam

targ

et

grad

e

is:

effo

rt

conc

ern

English CB C B LIKELY 5 ORG B B SECURE 5

English Literature CB B B SECURE 5 ORG C B LIKELY 5

Maths MC E B UNLIKELY 3 HW D B POSSIBLE 4

Science CPa E C UNLIKELY 4 ORG D C POSSIBLE 4 ORG

Science Additional CPa D C LIKELY 4 ORG B C LIKELY 4 ORG

French CK C B LIKELY 4 C B LIKELY 5

History KM A A SECURE 5 B A SECURE 5

RS CG D B POSSIBLE 4 C B POSSIBLE 4

KEY - target is: SECURE LIKELY POSSIBLE UNLIKELY

effort: 5 excellent - 4 good - 3 satisfactory - 2 poor - 1 very poor

concern: WW working well - ATT attitude - BEH behaviour TEN attendance - PUN punctuality - HW homework CON confidence - ORG organisation - EAL language

CASE STUDY No. 1

Alis predictive data

Alis predictive data

Surname Forename Av GCSE PredictionClosest Grade

Prior Value Added Prediction

Adjusted Prediction Grade

Final ‘target’

1 7.7 127.1 A*A 145.8 140 A* A*

2 6.1 98.8 B 117.6 117.6 A B

3 6 97.1 B 115.8 115.8 A A

4 6.9 113 A/B 131.7 131.7 A*/A A*

5 6.9 113 A/B 131.7 131.7 A*/A B

6 5.5 88.2 C 107 107 A/B A

7 7.5 123.6 A 142.3 140 A* A*

8 6.6 107.7 A/B 126.4 126.4 A*/A A*

Subject Score Prediction Closest Grade Adj Prediction Adj Closest Grade

(A2) Business Studies: Single 6.1 91.6 B/C 95 B

(A2) Art and Design 6.1 98.8 B 117.6 A

(A2) History Of Art 6.1 95.4 B 95.4 B

(A2) Geography 6.1 89.2 B/C 90.5 B/C

Alis predictive data

Surname Forename Sex Mid

YIS

Scor

e

Mid

YIS

Band

Art &

Des

ign

Biol

ogy

Engl

ish

His

tory

Mat

hem

atic

s

Scie

nce

KELLY JAMES MICHAEL M 127 A 6.9 7.2 7.1 7.1 7.3 7.1

RUMBLE MARK ADAM M 119 A 6.5 6.8 6.5 6.4 6.6 6.5

MILLS HANNAH ELLA F 110 A 6.1 6.2 5.8 5.6 5.7 5.7

DURSTON WILLIAM PETER M 100 C 5.5 5.6 5.0 4.7 4.7 4.8

MITCHELL JENNIFER LOUISE F 90 C 5.1 5.1 4.3 3.8 3.8 4.0

Point ‘predictions’ to GCSE (National)

Point ‘predictions’ to GCSE (Independent)KELLY JAMES MICHAEL M 114 A 7.3 7.6 7.3 7.4 7.5 7.2

RUMBLE MARK ADAM M 105 B 7.1 7.2 6.9 7.0 7.1 6.7

MILLS HANNAH ELLA F 93 C 6.8 6.7 6.4 6.5 6.5 6.2

DURSTON WILLIAM PETER M 80 D 6.4 6.0 5.8 5.7 5.6 5.4

MITCHELL JENNIFER LOUISE F 68 D 6.0 5.3 5.2 5.0 4.9 4.8

• Compare the ‘predictions’ for National and Independent sector. What pattern do you notice?

Student 1Student 2

Student 4Student 3

Student 5

Student 1Student 2Student 3Student 4Student 5

Prediction/expected grade: 5.0 grade C

0 0 15

22

37

26

8

10

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25

30

35

40

45

U G F E D C B A A*

Perc

ent

Grade

Individual Chances Graph for WILLIAM PETER DURSTON -GCSE English

MidYIS Score 100 MidYIS Band C

Student 4

Prediction: 5.8 grade B

INDEPENDENT SECTOR

Student 4

0 0 0 1

6

26

42

21

30

5

10

15

20

25

30

35

40

45

50

U G F E D C B A A*

Perc

ent

Grade

Individual Chances Graph for WILLIAM PETER DURSTON -GCSE English

MidYIS Score 80 MidYIS Band D