Designing school-level indicators in French Speaking Belgium

1. Designing school-level indicators in French Speaking Belgium Alix Dandoy, Marc Demeuse, Alexandra Franquet, Nathanaël Friant, & Jonathan Hourez

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

3. 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

4. Understanding segregation Is there segregation between schools? e.g. Segregation indexes (Demeuse & Baye, 2008) Global view of segregation What are the pupils flows between several schools? What kind of pupils do leave the most privileged schools ? Systemic view What are the mechanisms fostering segregation? Where do these pupils go? Local view of a school situation 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?

5. 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

6. 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

7. Results: Local view

8. Results: Local view

9. Results: Local view

10. Results: Local view

11. 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

12. Results: Systemic view D B C A C’

13. Equipping decision makers with relevant information Prospective Policy change future Past Present Prevision Description • Requires to • Model individual behaviour • Model the system and change parameters

14. 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)

15. 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

16. Simulation using NetLogo: prevision

17. Simulation using NetLogo: prevision

18. Simulation using NetLogo: prevision

19. Simulation using NetLogo: prospective

20. 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

21. 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

22. 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?

23. Thank you for your attention! • Institute of School Administration • Prof. Marc Demeuse: marc.demeuse@umons.ac.be • Alix Dandoy: alix.dandoy@umons.ac.be • Alexandra Franquet: alexandra.franquet@umons.ac.be • Nathanaël Friant: nathanael.friant@umons.ac.be • Information Systems • Jonathan Hourez: jonathan.hourez@umons.ac.be

24. School B local systemic

25. School A local systemic

26. School C local systemic

27. School C’ local systemic

28. School D local systemic

29. 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 steps • Collecting information • Diagnosing • Determining the actions • Implementing solutions

30. Pilotage « Ideal state » definition

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

32. 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).