Designing school-level indicators in French Speaking Belgium

Faculty of Psychology and Education Science

Faculty of Psychology and Education Science

Designing school-level indicators in French Speaking Belgium

Alix Dandoy, Marc Demeuse, Alexandra Franquet, Nathanaël Friant, & Jonathan

Hourez

Université de Mons Alix Dandoy | Service de Méthodologie et Formation / INAS 2

Objectives and theoretical framework

Educational system context Educational « quasi-market » (Maroy & Dupriez, 2000 ; Duru-Bellat & Meuret,

2001). Free school choice and organization Public financing Competitive interdependencies (Delvaux & Joseph, 2003) – Hierarchical

ranking of schools

Segregation (Crahay, 2000 ; Demeuse, 2005 ; Baye & al., 2004 ; Demeuse & Baye, 2007; 2008)

Very privileged schools (sanctuary schools) <----------> Very underprivileged schools (ghetto schools)

Little leverage to encourage social mixing Public founds allocation (Demeuse et al., 2010) Schools as real units


Objectives and theoretical framework

Research context Research project funded by the public schools organized by the

French Community of Belgium

To equip decision makers at the school and at the system levels with relevant information to make decisions

Pilotage (monitoring of educational outcomes) : “A set of proceedings aiming at modifying a system in order to reach targets” (Demeuse & Baye, 2001)

To produce scientific knowledge: a more accurate understanding of the mechanisms fostering segregation at the educational system level


Understanding segregationGlobal view of segregation

Local view of a school situation

Systemicview

Is there segregation between schools?

Is the school privileged? Does it gain or lose pupils

from one grade to another?

Do many pupils leave the school before 6th grade?

Do many pupils enter the school after the 1st grade?

What kind of pupils do leave the most privileged schools ?

Where do these pupils go?

What are the pupils flows between several schools?

What are the mechanisms fostering segregation?

e.g. Segregation indexes (Demeuse & Baye, 2008)


Method

Data Census tables (administration)

1 record = 1 pupil Variables (school, year of studies, track, date of birth, home country

and town, diplomas, socioeconomic index) Aggregated at the school level Evolution over 5 years

Socioeconomic index (SES) (Demeuse & al., 1999) Home district Metric variable (normal distribution) Not definitive


Method

School level indicators Structure

Average SES, SES by track Population structure

Size SES

Flows Evolution Incoming and outgoing flows + SES Grade repetition management (generated, treated, externalized) +

SES


Results: Local view

Population 604

Socioeconomic index - 0,25

Students leaving 20%

Students entering 5%

Grade repetition 15%

Of which externalised 27%

Grade repetition coming from other schools 2%

… …

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

D1C

G


Results: Local view

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

D1C

G

Population 601







… …

580

585

590

595

600

605

610

1 2 3

year

N p

up

ils

-0,3

-0,25

-0,2

-0,15

-0,1

-0,05

0

SE

S p

up

ils

population

SES


Results: Local view

Population 590







… …

580

585

590

595

600

605

610

1 2 3

year

N p

up

ils

-0,3

-0,25

-0,2

-0,15

-0,1

-0,05

0

SE

S p

up

ils

population

SES

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

D1C

G


Results: Local view

0 20 40 60 80 100 120 140

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140

1er e

2eme

3eme

4eme

5eme

6eme

7eme

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

0 50 100 150 200 250 300

1

2

3

4

5

6

7


Results: Systemic view• City map

Each point = one school Point size = school size Schools and districts are coloured

Dark red = low SES Dark green = high SES


Results: Systemic view

0 50 100 150 200 250 300

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140

1er e

2eme

3eme

4eme

5eme

6eme

7eme

A

B

C

C’

D

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7


Equipping decision makers with relevant information

Past Present future

DescriptionPrevision

Policy change

Prospective

Requires to

• Model individual behaviour

• Model the system and change parameters


Extracting individual behaviour rules

• Data mining Automatic extraction of information from large datasets Tool: Weka (Hall et al., 2009)

• Individual behaviour rules: school choice Distance SES Moving houses Changing streams (relegation)


Simulation using NetLogo

• NetLogo (Wilenski, 1999) « Multi-agent programming language and modeling

environment for simulating natural and social phenomena » (Wilensky, 1999)

Agents operating independantly

• Our model Agents

Pupils Schools

Located on districts


Simulation using NetLogo: prevision






Simulation using NetLogo: prospective


System level information

• What would be the systemic consequences of Closing a school? Creating new schools?

E.g. creating new schools in underprivileged districts could foster segregation


Discussion

• Scientific importance Beyond the static picture Systemic point of view

Conciliates micro studies and macro data Allows to understand relations between schools and complex

systemic effects Simulation

Allows to test hypotheses or unpredicted effects otherwise impossible to test

But it can’t take into account all the possible parameters


Discussion

• Educational importance Produces relevant information that could equip decision makers

to make sound decisions E.g. monitor the public schools’ network

Work in progress: Parameters can be added Consultation with the silent partner

Ethics the purpose is NOT to provide a marketing tool Who could use this information, and for which purpose?


Thank you for your attention!

Institute of School Administration

•Prof. Marc Demeuse: [email protected]

•Alix Dandoy: [email protected]

•Alexandra Franquet: [email protected]

•Nathanaël Friant: [email protected]

Information Systems

•Jonathan Hourez: [email protected]


School B

Population 590







… …

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

D1C

G

local systemic


School A

Population 1094

Socioeconomic index 0,33






… …

0 50 100 150 200 250 300

1

2

3

4

5

6

7

D1C

G

local systemic


School C

Population 467







… …

0 20 40 60 80 100 120 140

1

2

3

4

5

6

7

1B

D1C

P

TQ

TT

local systemic


School C’

Population 489







… …

0 20 40 60 80 100 120 140 160 180

1

2

3

4

5

6

7

1B

D1C

P

TQ

TT

local systemic


School D

Population 590







… …

0 20 40 60 80 100 120 140

1ere

2eme

3eme

4eme

5eme

6eme

7eme

1B

D1C

G

TQ

local systemic


Pilotage

• Definition (Demeuse & Baye, 2001) « A set of proceedings aiming at modifying a system in order to

reach targets »

There is a definition, at least temporary, of an « ideal state », i.e. a target to reach

Four steps1. Collecting information2. Diagnosing3. Determining the actions4. Implementing solutions


Pilotage

Diagnosing

Implementing solutions

Collecting information

Determining actions

« Ideal state » definition


Data mining

« The nontrivial process of identifying valid, novel, pontentially useful, and ultimately understandable patterns in data » (Fayyad et al., 1996).


Segregation indexes (Demeuse & Baye, 2008)« The proportion of pupils belonging to the target group that should change schools in order to achieve a homogenous distribution of this group in all of the schools » (Gorard and Taylor 2002).

Documents

Designing school-level indicators in French Speaking Belgium